Final Report FYP

UNIVERSITI TEKNOLOGI MARA

A STUDY TO MAINTAIN PURCHASING POWER FOR MIDDLE INCOME MALE RETIREES IN MALAYSIAN

PRIVATE SECTOR

BACHELOR OF SCIENCE (Hons.) ACTUARIAL SCIENCE

FACULTY OF COMPUTER AND MATHEMATICAL SCIENCES

January 2015

MUHAMMAD BIN ALIZAN 2012357375 SUMAIYYAH BINTI ROSHIDI 2012995507 MOHD AMIR ASYRAF B ABD MALIK 2012321191 MOHD HAZIM BIN SAARI 2012532905

UNIVERSITI TEKNOLOGI MARA

A STUDY TO MAINTAIN PURCHASING POWER FOR MIDDLE INCOME MALE RETIREES IN MALAYSIAN

PRIVATE SECTOR

January 2015

MUHAMMAD BIN ALIZAN 2012357375 SUMAIYYAH BINTI ROSHIDI 2012995507 MOHD AMIR ASYRAF B ABD MALIK 2012321191 MOHD HAZIM BIN SAARI 2012532905

Project submitted in fulfillment of the requirements For the degree of

Bachelor of Science (Hons.) Actuarial Science Faculty of Computer and Mathematical Sciences

Approved by

.. Supervisor

Zahrul Azmir ABSL Kamarul Adzhar Faculty of Computer and Mathematical Sciences

Universiti Teknologi MARA, Shah Alam, Malaysia.

CANDIDATES DECLARATION

I hereby declare that this project was carried out in accordance with regulations of Universiti Teknologi MARA. It is original and is the result of my own work, unless otherwise indicated or acknowledged as referenced work. This topic has not been submitted to any other academic institution or non-academic institution for any other degree or qualification.

In event that my project be found to violate the conditions mention above, I voluntarily waive the right of conferment of my degree and agree be subjected to the disciplinary rules and regulations of Universiti Teknologi MARA.

Name of candidate : Muhammad Bin Alizan

Student ID : 2012357375

Programme : Bachelor of Science (Hons.) Actuarial Science

Faculty : Faculty of Computer and Mathematical Sciences

Project Title : A Study to Maintain Purchasing Power for Middle Income Male Retirees in Malaysian Private Sector

Signature of Candidate

Date 26 January 2015




Name of candidate : Sumaiyyah Binti Roshidi

Student ID : 2012995507









Name of candidate : Mohd Amir Asyraf B Abd Malik

Student ID : 2012321191









Name of candidate : Mohd Hazim Bin Saari

Student ID : 2012532905






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ABSTRACT

Inflationary risk is the risk that the inflation will undermine the performance of an investment. In other words it is the uncertainty over the future real value of an investment. Inflation is on the rise now and the future value of a pensioners pension fund is at stake. If no action is taken, retirees could be forced to lower their standard of living. This could be problematic to both the retirees and governments. There are two main objectives of this study; to investigate the inflation trend in Malaysia and to investigate the sufficiency of the pension fund throughout the future lifetime of the retirees. The study begins with a brief description on the problems arising due to the increase in inflation every year. Next it explains the data and methods being used in the study. The collected data is categorized in secondary data concerning a group of private workers in Malaysia from 2009 to 2012. The methods used to achieve our studys objectives are the Mixed Autoregressive Integrated Moving Average (ARIMA) Model and Hypothetical simulation model. The findings are highlighted based on these methods and it can be concluded that the inflation will continue to rise in the future and the annuity that has been converted from a lump sum will suffice throughout the future lifetime of retirees.

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ACKNOWLEDGEMENT

In the name of Allah S.W.T the most Gracious, the most Grateful

We, Sumaiyyah binti Roshidi, Muhammad bin Alizan, Mohd Amir Asyraf bin Abd Malik and Mohd Hazim bin Saari would like to thank our supervisor, Encik Zahrul Azmir ABSL Kamarul Adzhar for his patience and endless support and supervision in guiding us as well as providing us with some ideas and advices for our final year project. Other than that we would like to express our upmost gratitude to our parents for supporting us.

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TABLE OF CONTENT

ABSTRACT i

ACKNOWLEDGEMENT ii

TABLE OF CONTENT iii

LIST OF TABLES v

LIST OF FIGURES ix

CHAPTER 1

1.1 Background of The Study 1 1.2 Problem Statement 3 1.3 Research Objectives 6 1.4 Research Questions 6 1.5 Significance of The Study 6 1.6 Scope and Limitations of Study 7

CHAPTER 2 2.1 Introduction 8

2.2 Issues or Problem 10 2.3 Data and Methodology 11

2.3.1 The Mixed Autoregressive Integrated Moving Average (ARIMA) model 11

2.4 Assumptions and Limitation 14

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CHAPTER 3 3.1 Introduction 16 3.2 Data Description 16 3.3 Methodology 17 3.3.1 ARIMA Model 17

3.4 Calculation of the Accumulated Value of the Individuals Contribution under Employee Provident Fund (EPF) 20

3.5 The Simulation of the Future Income for Retirees 21 3.5.1 Assumptions used in the Hypothetical Life Course

Simulation 27 3.5.2 Compare the results by scenarios with different

assumptions 30

CHAPTER 4 4.1 Introduction 31 4.2 Research Data 31 4.3 Inflation Forecasting 32 4.3.1 Analyzing and Fitting the Malaysias Inflation Data Series 32 4.3.2 Results of the Inflation Rate Forecast Values 41 4.4 Findings on Hypothetical Simulation Model 44

CHAPTER 5 5.1 Conclusion 53 5.2 Limitations and Recommendations 55

REFERENCES 56

APPENDICES 60

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LIST OF TABLES

Table Title Page 2.1 Mean and Volatilities of CPI Inflation from Various Countries 9 3.1 Age of Second Pre-Retirement Withdrawal 30 4.1 The Result of Mean Square Error (MSE) output for each model 40 4.2 Inflation Forecast from December 2014 until December 2019 41 4.3 Annual Average Inflation Rate from 2014 until 2019 42 4.4 Highest and Lowest Inflation Rate for Monthly and Annually 42 4.5 Result for Scenario A 45 4.6 Result for Scenario B 46 4.7 Result for Scenario C 47 4.8 Result for Scenario D 48 4.9 Result for Scenario E 49 4.1 Result for Scenario F 50

4.11 Result for Scenario G 51 4.12 Result for Scenario H 52

4.13 Malaysia Inflation Rate from January 1995 to November 2014. Data source: National Institute of Statistics Malaysia 69

4.14 CPI according to Market Basket in Malaysia 70 4.15 CPI according to Market Basket in Malaysia 71 4.16 Unemployment and Employment Rate in Malaysia 72 4.17 Male Employment Rate in Malaysia 73 4.18 Occupation Class 74 4.19 Variable in HIS data 75 4.20 Calculation on average initial salary grade 78 4.21 Salary Age 24 79 4.22 Salary Age 25 79 4.23 Salary Age 26 80 4.24 Salary Age 27 80 4.25 Accumulated Fund EPF from age 24 81

4.26 Accumulated Fund EPF from age 24 with Pre-Retirement Withdrawal 82

4.27 Accumulated Fund EPF from age 25 83






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LIST OF FIGURES

Figure Title Page 3.1 Three Stages of ARIMA 17 3.2 Process of Hypothetical Simulation Model 25 4.1 Time Series Plot of Malaysias Monthly Inflation 32 4.2 Trend Analysis Plot of Malaysias Monthly Inflation 33 4.3 Autocorrelation Function Plot of Malaysias Inflation 33 4.4 Partial Autocorrelation Function Plot of Malaysias Inflation 34 4.5 Time Series Plot in Seasonal Difference 35 4.6 The Autocorrelation Function of Zt 36 4.7 The Partial Autocorrelation Function of Zt 36 4.8 Time Series Plot of Wt 37 4.9 The Autocorrelation Function of Wt 38 4.10 The Partial Autocorrelation Function of Wt 38 4.11 Time Series Plot for Inflation with Forecast 43 6.1 Functions of Stat in Minitab 60 6.2 ARIMA 61 6.3 ARIMA (Forecast) 62 6.4 Types of Time Series Plot 63 6.5 The Dependent Variable on Y-Axis 63 6.6 The Time/Scale on the X-Axis 64 6.7 Trend Analysis 64 6.8 Autocorrelation Function Plot 65 6.9 Partial Autocorrelation Function Plot 65 6.10 The Minitab Output 66

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CHAPTER 1 INTRODUCTION

1.1 Background of The Study

This paper tackles the problem that retirees face when they retire. During retirement, there is a significant decrease of income compared to the time of employment. Due to the effect of inflation on the price of necessities, the price of goods will increase substantially. Hence the purchasing power will decrease and the standard of living for people who rely solely on their pension will decline. This study focuses mainly on how to cope with the problems associated with inflation and purchasing power. Such problem can be solved by fully utilizing the power of compounded interest matched with a long period of investment.

William H.Aitken (April, 1996) stated that the prospect of no money generated every month is not attractive at all. Being able to maintain the same standard of living after retirement is important to most people. In order to achieve this, a thorough planning, funding and continuous monitoring are required. Pension scheme is one of the ways to achieve this goal. Pension fund can be defined as a form of institutional investor that functions by collecting; pooling and investing the funds contributed by sponsors and beneficiaries to provide an annuity to the beneficiaries in the future (Davis 1995a).

This is a way designed to save money and accumulate interest in order to fund the consumptions in retirement (E Philip Davis). There are two major types of pension schemes, namely defined benefit and defined contribution. In a defined benefit plan, the benefit has been defined earlier. In order to ensure this, certain calculations will take place in order to determine how much contribution would be needed. The main

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advantage of a defined benefit plan is that the income offered is stable and is subsequently indexed to inflation. The major weaknesses include the lack of benefit portability when changing jobs and the complex valuation of plan liabilities. In a defined benefit plan, when a worker moves jobs, he can end up with a much lower pension in retirement.

A defined contribution plan has a defined amount of contribution payable by both employee and employer, often as a fixed percentage of salary. The employees retirement benefit is determined by the size of the accumulation at retirement. Retirees who are under the Defined Contribution Pension Plan are exposed more to risk compared to those who are under Defined Benefit Pension Plan (Bodie et al., 1988). At retirement, the beneficiaries can usually take the money as a life annuity, a phased withdrawal plan, a lump sum payment, or some combination of these. As the value of the pension benefits is simply determined as the market value of the backing assets, the pension benefits are easily transferable between jobs. While the employer or sponsor is only obliged to make regular contributions, the employees bear a range of risks. In particular, they bear asset price risk (the risk of losses in the value of their pension fund due to falls in asset values) at retirement and inflation risk (the risk of losses in the real value of pensions due to unanticipated inflation). The real payoff during the retirement could be severely damaged by the accumulation of inflation in a long period of time (Nan-wei Hana). During the calculation of retirement income, the inflation rate needs to be taken into consideration because it affects directly the real rate of return as well as the purchasing power of the income stream produced. Inflation is crucial in the investment

decision making process and low inflation is the key towards a better standard of living through savings and investments (Megginson, 2008).

Risk can be defined as the probability of an occurrence of an unfavorable event which may lead to a loss. Inflation can be defined as a general increase in prices and fall

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in the purchasing value of money. . Inflation risk can be defined as the chance that the cash flows from an investment will be of lesser value in the future because of changes in purchasing power caused by inflation. Inflation is an important economic indicator that needs considering because it affects the economic growth directly. For example, if the price is expected to rise rapidly in the future, people will react by purchasing goods now. This will eventually lead to further increases in price for these services and goods. Another way to describe inflation is that the amount of money supplied is too much. This can be explained by the demand and supply theory. People will have more money to offer for goods when the supply of money is increased. This will increase the demand for goods but not the supply. If the demand is more than the supply, this will result in an increase in the price of goods. This happens not because goods are scarcer than before, but because money is more abundant.

In Malaysia, the life expectancy is expected to increase. Most retirement income systems in Asia are not well prepared for the growing ageing population that is forecasted to increase in numbers over the next two decades (OECD, 2009). According to the statistics department, this year the consumer price index rose from 2.9 per cent to 3.3 per cent which is the highest level since November 2011. Inflation is going to be on the rise in 2014, especially in second half of the year (Nor Zahidi). Even though inflation risk is on the rise, there are ways to control it. Monetary policy which works by reducing the aggregate demand can be used to control inflation. Other policies such as Fiscal policy, Wage control, Monetarism and Supply side policy can be used control inflation.

1.2 Problem Statement

Inflation risk can be defined as the chance that the cash flows from an investment will be of lesser value in the future because of changes in purchasing power caused by inflation. For example, the price of a house this year is RM500k but next year the price

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might rise above RM500k due to inflation. There are several factors of inflation. The first factor of inflation is because the aggregate demand is rising faster than the aggregate supply. This will pull up the prices and known as the demand-pull inflation. The second factor is known as the cost-push inflation. The wage increases forced upon the economy by labor unions under threat of strike, or costs may be raised by business monopolies.

The ministry of finance stated that the Consumer Price Index, CPI will increase from 4% to 5% in 2015, compared to an average of 3.3% in 2014. Malaysia has embarked on a series of fiscal consolidation moves following global ratings agency Fitch which revised Malaysias sovereign debt outlook from Stable to Negative in July. Inflation in Malaysia is also expected to remain manageable despite trending above the long-term average. This is because Bank Negara Malaysia, BNM is expected to increase interest rates in order to help contain the inflation.

According to Malaysian Chief executive of Private Pension Administrator, Datuk Steve Ong, Malaysians are at risk of a lower standard of living during retirement if they rely solely on the employee provident fund, EPF. He said that Malaysias current income replacement levels for retirement were only 30 per cent while the average for OECD countries was 57 per cent. Pensioners who do not plan for their retirement risked having to work longer or cut back on their standard of living during retirement.

Inflation risk is faced by all pensioners. Even the largest fund in Malaysia, the employee provident fund, EPF is facing a very high risk trying to keep returns high by investing in more overseas property and domestic stocks while fending off concerns that these are too risky. The purchasing power could be reduced and the standard of living could be reduced by inflation. Since the Defined Benefit plan is adjusted to inflation, we

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will be focusing mainly on pensioners with the Defined Contribution plan. Most of the pensioners in DC plan are not aware of the rise in inflation that will affect their pension

later on when they retire. Even though the inflation forecast in the future may seem

modest, market conditions can change quickly. Without proper inflation protection, the pensioners could risk the loss of asset value and purchasing power. A few studies found out that those who are making minimum or no contributions towards their Defined Contribution Pension Plan will have a hard time maintaining their standard of living when they retire due to the fact that their funds are inadequate (Samwick and Skinner, 2001; Choi et al., 2002; Thaler and Benartzi, 2004).

There are 3 stages of workers: young workers, middle aged workers and old workers. Young workers should not worry about direct inflation protection. This is because they are still young and there is room for growth. As long as their wages could keep up with the inflation, there should be no problem. For middle aged workers, their short run inflation risk is gradually growing. They should gradually shift to direct inflation hedging strategies. They should not focus on growth anymore but protection instead. For the old workers, now is all about protecting themselves from the loss of purchasing power. The 2 main risks they face now are the unexpected inflation and a drop in asset values relative to steady and predictable inflation.

There are several ways to manage inflation. According to Gottfried Haberler, inflation can be controlled using Fiscal policy. Fiscal policy is where the government

controls the expenditure and the revenue by injecting or retracting money. This method is a bit slow because it has to go through parliamentary procedures. The other way is through the monetary policy. It is a measure that can be initiated swiftly and changes can be implemented quickly.

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1.3 Research Objectives

The research objectives of this study are:

i. To investigate the inflation trend in Malaysia. ii. To convert the accumulated fund in EPF account into an annuity.

iii. To investigate the sufficiency of the pension fund throughout the future lifetime of the retirees.

1.4 Research Questions

The research questions of this study are:

i. What is the inflation pattern in Malaysia?

ii. Will the pension fund last throughout the future lifetime for the retirees?

1.5 Significance of the Study

This research may benefit the Malaysian government and the pensioners. Our research provides the inflation pattern in Malaysia and the inflation risk pattern among a group of private workers in Malaysia from year 1995 until year 2014 ranging from age 24 to age 60. The obtained result could prove to be beneficial to assist other researchers in the future by having a thorough and simple explanation. Other than that it aims to study the role of two different parameters that is, retirement age and contribution rates.

In order to ensure that the pensioners could continue living the way they used to live, certain measurements should be imposed. The amount of contributions the pensioners contribute during their active working life should be adjusted to match the anticipated rate of inflation in the future. By having the contributions adjusted, the pensioners could have their purchasing power in the future just as strong as now. This

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will aid the pensioners greatly. Hence, this project paper will benefit the pensioners that wish to maintain their same standard of living.

1.6 Scope and Limitations

Study for this topic has the following scope due to the inability the collect the large amount of data needed:

1. The average salary calculated is being based on people working in the private sector.

2. The number of researches that can aid this study is very minimal. 3. Very hard to find suitable and relevant articles related to the research.

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter will discuss about the issues or problems concerning the state of purchasing power when inflation hits for pensioners with defined contribution plans. Other than that this chapter will also discuss how to forecast the future inflation by using the data and methodology related to those problems, the findings, conclusions, the significance, and the assumptions as well as the limitations that are being used by those who had been doing researches related to this topic.

Inflation management is one of the hardest tasks an economic policymaker has to undertake. Inflation is, at the same time, one of the most dreaded and one of the most misunderstood of economic phenomena. We know from experience, combined with cogitation, that the prices of commodities will, over time, rise and fall, responding to the pulls and pushes of demand and supply. Except for 1949, 1955, and 2009, the prices of goods and services have, on average, risen each year since 1945. Inflation rose in the 1960s, peaked in the 1970s and early 1980s, and has been generally low but positive since then.

Inflation in Malaysia has been below the global average for the whole sample period and it followed the global trend up to 2003. From this date onward, the recent upward inflation trend in Malaysia and in its trading partners has forced a faster conversion towards the world average inflation at approximately 3.5 percent. How do Malaysian Inflation rates compare to those of similar economies in the region? To

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answer this question we select Singapore, Thailand, Korea, Indonesia and Philippines as the base countries for a cross-country study.

Table 2.1 Mean and Volatilities of CPI Inflation from Various Countries

Countries MEANS VOLATILITIES

91-96 97-99 00-05 All Period 91-96 97-99 00-05 All Period

Indonesia 8.77 29.32 8.40 12.73 1.42 28.88 4.04 15.25

Korea 6.01 4.26 3.16 4.52 1.82 2.96 0.82 2.20

Malaysia 3.89 3.56 1.71 2.95 0.66 1.40 0.68 1.33

Philippines 9.91 7.44 4.46 7.24 4.21 2.25 2.87 4.14

Singapore 2.37 0.59 0.77 1.37 0.82 1.26 0.82 1.22

Thailand 4.98 4.67 2.15 3.79 1.21 3.77 1.44 2.42

Industrial Countries 2.89 1.63 2.04 2.30 0.86 0.34 0.44 0.80

Non-Oil Develop.Ctys 45.63 10.31 5.55 22.53 18.31 1.59 0.83 22.28

Malaysian Trade

Partners 2.56 3.50 1.96 1.57 1.20 0.64 0.78 0.57

Malaysia is only second to Singapore in terms of lowest average inflation in the region during the distinct sub periods. Both countries display inflation rates comparable to those of Industrial countries and far from other non-oil developing economies. In terms of volatility, Malaysian inflation has experienced the lowest volatility among its regional counterparts in the last decade and a half. For the first five years of the sample period Malaysian inflation volatility was even lower than that in Industrial economies.

Many different factors and policies have been held responsible for inflation. It is a widely held view that inflation is always and everywhere a monetary phenomenon resulting from and accompanied by a rise in the quantity of money relative to output.

One of the factors is that a more rapid rate of money growth plays an active role in inflation and results either from mistaken policies of the Federal Reserve or because the Federal Reserve subordinates itself to the fiscal requirements of the federal government and finances budget deficits through money creation. For example, Federal Reserve policies that are likely to produce inflation are those that fix rates of interest too low or that support unrealistic foreign exchange values of the dollar.

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Due to the steady increment of inflation in Malaysia, a projection of future inflation is important in order to forecast the inflation. It is best for the government to have a projection of inflation so that they can aid the pensioners in making decisions related to their retirement. There are significant methods that have been used to forecast the inflation. The flexibility of these approaches can be used in determining the important factors that can affect inflation.

2.2 Issues or Problem

One of our objectives of this study is to investigate the sufficiency of the pension fund throughout the future lifetime of the retirees. By having these studies, the pensioners will have a clearer view on how to maintain their purchasing power and their standard of living too. One reason measuring inflation is so important is because if and when it returns, it will affect the purchasing power of our dollars. Our objective is backed by an annual report of EPF in 2013 where it shows that the average savings for active members by the time they hit the age of 54 stood at RM166, 650 and the average savings for inactive members in their EPF accounts is RM26, 250. Since the average expenditure for a household is RM5000, this amount of saving is definitely will not be enough.

Older people aged 60 and over are expected to increase in number from 1,398.5 million in 2000 to 3,439.6 million in 2020 in Malaysia (DOS, 2000b). This eventually led to an increased interest in regards of income in later life. It can be expected that a retiree will at least live for another 20 years after they start to retire (Leoi, 2008). Other than that, the increase in life expectancy will affect the amount of pension that is needed in order to provide an acceptable standard of living for the retirees. The sustainability from fiscal perspective of the current pension system has emerged as a major concern for policy makers. However, less interest has been shown regarding whether the system

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itself will manage to provide a pension of sufficient value to ensure a decent standard of living post retirement. Post marriage, it is not an unusual thing for women to stop

working in order to focus on taking care of their family. This puts responsibility on the men as the sole bread winner. If the amount in the accumulated fund is small, later on it will not suffice to fund the expenses during retirement. Furthermore, the current global economic downturn has affected the economy of many developing countries such as Malaysia in a way that may change the pension scheme in an unfavorable manner.

With the unstable economic conditions, a growing ageing population and increasing life expectancy, there is a probability that it may affect retirement savings and income during retirement. Some speculate that by increasing the number of working years will result in happiness, higher morale, better adjustment, greater longevity, larger social networks and better perceived health among the elderly (Mohamed, 2000). Theoretically, by having longer working careers and increasing the retirement age would make the pension scheme more sustainable and the standard of living could be maintained (McGillivray, 2005).

2.3 Data and Methodology

2.3.1 The Mixed Autoregressive Integrated Moving Average (ARIMA) Model

ARIMA methods for forecasting time series are essentially agnostic. Unlike

other methods they do not assume knowledge of any underlying economic model or

structural relationships. When using ARIMA model for the purpose of forecasting, it is

assumed that past values of the series plus previous error terms contain information

needed in order to forecast the value of the data.

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Several methods for identifying ARIMA models have been suggested by Box-

Jenkins and others. Makridakis et al. (1982), and Meese and Geweke (1982) in their

writings have discussed the methods of identifying univariate models. For forecasting

Irish inflation using ARIMA models Aidan Meyler, Geoff Kenny and Terry Quinn

(1998) used two different approaches.One of the approach is the Box Jenkins approach

and another one is objective penalty function methods for identifying appropriate

ARIMA models. The emphasis is on forecast performance, which suggests that ARIMA

forecast has outperformed. Toshitaka Sekine (2001) estimated an inflation function and

forecasted inflation one-year ahead for Japan and he found that there are a mark-up

relationship, excess money supply and the output gap are important in determining long

run equilibrium correlation model of inflation. He emphasized the importance of

adjustment to a pure model-based forecast by utilizing information of alternative

models.

George E.P. Box and Gwilym M. Jenkins (1970) integrated the existing

knowledge on time series and they introduced univariate models for time series which

simply made systematic use of the information included in the observed values of time

series. This offered an easy way to predict the future development of the variable.

Moreover, these authors developed the three-stage iterative cycle for time series which

are identification, estimation, and verification. Their book had an enormous impact on

the theory and practice of modern time series analysis and forecasting. With the advent

of the computer, it popularized the use of autoregressive integrated moving average

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(ARIMA) models and their extensions in various areas of science. Since then, the

development of new statistical procedures and more sophisticated computers as well as

the availability of larger data sets has advanced the application of time series methods.

After the introduction by Yule (1921), the autoregressive and moving average models

have been greatly favored in time series analysis.

According to Aidan Meyler, Geoff Kenny and Terry Quinn (1998) the main

advantage of ARIMA forecasting is that it requires data on the time series. Firstly,

forecasting a large number of time series are more favorable. Secondly, a problem that

occurs at certain times with multivariate models can be prevented. For example,

consider a model including wages, prices and money. It is possible that a consistent

money series is only available for a shorter period of time than the other two series,

restricting the time period over which the model can be estimated. Third, there is a

problem with timeliness of data when using the multivariate models. If one constructs a

large structural model containing variables which are only published with a long lag,

such as wage data, then forecasts using this model are conditional forecasts based on

forecasts of the unavailable observations, adding an additional source of forecast

uncertainty

There are also some drawbacks when forecasting using ARIMA model. Aidan

Meyler, Geoff Kenny and Terry Quinn (1998) stated that to identify the model

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formulation can be very subjective and need an expert like forecasters whom have skills

and experiences in identifying the model. Other than that, it is not embedded within any

underlying theoretical model or structural relationships. Hence, the economic

significance of the chosen model is not clear. Therefore, it is impossible to run policy

simulations with ARIMA models, unlike with structural models. The third disadvantage

of ARIMA models is poor at predicting turning points because of its backward looking characteristic and can be improve if the turning point represents a return to a long-run

equilibrium.

However, ARIMA models have proven themselves to be effectively working and

outperform more sophisticated structural models when forecasting for a short period of

time.

2.4 Assumptions and Limitation

From the journals that we have read, each research has their own limitation on their study. According to Antolin(2007), this research has lack of data to estimate and forecast mortality rates and life expectancy for the very old (those aged 85 or more). Data at very old ages are not very accurate because of small sample problems. Only a few countries have certified population statistic which are sufficiently accurate to produce consistent estimates of death rates at higher ages. It is commonly accepted that between ages 30 and 85, age-specific death rates tend to rise approximately at fixed rate of increase. This rate of increase tend to fall for ages 85, and even possibly, at the more

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extreme ages to become zero or negative, although one cannot be certain of the latter because of the sparseness of the data above age 100.

Aidan Meyler (1998) stated in his research project has considered autoregressive integrated moving average (ARIMA) forecasting. ARIMA models are theoretically justified and can be surprisingly robust with respect to alternative (multivariate) modelling approaches. Indeed, Stockton and Glassman (1987) upon finding similar results for the United States commented that a simple ARIMA model of inflation can turn in such a respectable forecast performance relative to the theoretically based specification.

Although the forecasting results for the sample period 1993Q1-1998Q4 compare quite favorably with those from BVAR analysis, that does not mean that univariate modelling can supplant multivariate techniques. The period in question was one of relatively stable inflation. ARIMA models may not perform as well with more volatile series. Furthermore, ARIMA models are backward looking and are generally poor at forecasting turning points. Also well-specified multivariate models generally perform better than ARIMA models over longer time horizon.

Andreja Pufnik (2006) also used ARIMA models to forecast the consumer price index in Croatia and forecasting future values of variables from the past behavior of the series, and attempt to examine whether separate modelling and aggregating of the sub-indices improves the final forecast of the total index. His research considered the main problems associated with the characteristics of the consumer price index (CPI) series in Croatia because it lacks of length of series, changes in the methodology and structural breaks.

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CHAPTER 3

METHODOLOGY

3.1 Introduction

There are three objective of this study which is to investigate the inflation trend in Malaysia, the relationship between inflation risk and purchasing power among pensioners and the relationship between inflation risk and the amount of contribution and also to calculate the adjusted contribution for Malaysian public workers. Methodology for finding expected life expectancy and consumer price index in Malaysia are both different. These because each objective is related to different variable and each variable are suitable for different methods.

3.2 Data Description

In this project paper, the types of data that are being used are secondary data collected from the Malaysias Department of Statistic. We decided to use consumer price index (CPI) data, labour force survey time series data and life expectancy data to look the effect of inflation on purchasing power on pensioners.

Regarding with consumer price index (CPI) data in Malaysia, we manage to get from the January 1995 until November 2014 in form of monthly data and time series data. We also manage to get principal statistics of the labour force by sex in Malaysia from year 1982 to year 2012.

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3.3 Methodology

3.3.1 ARIMA MODELS

The integrated component of an ARIMA model represents the number of times a time series must be differenced to induce stationarity. A general notation for ARIMA models is ARIMA (p,d,q)(P,D,Q), where p denotes the number of autoregressive terms, q denotes the number of moving average terms and d denotes the number of times a series must be differenced to induce stationarity. P denotes the number of seasonal autoregressive components, Q denotes the number of seasonal moving average terms and D denotes the number of seasonal differences required to induce stationarity.

Figure 3.1 Three Stages of ARIMA

The objective of analyzing economic data is to predict or forecast the future values of economic variables. The proposed Box-Jenkin methodology for this research involves iterative three-stage cycles. Before start the first stage, after collecting the data, to forecast a time series, the stationary of the series must be maintained. A time series is said to a stationary if mean and the variance are constant over time. Through the stationary test we will also examine the properties of the time series variable, in order to

Find the orders of ARIMA(p,d,q) model

Having identified the values of ARIMA model

Stage 1 : Model

Identification

To ensure the best fitted model : 1)test the residuals estimated of the model. 2) Check if the white noise is present

If residuals tunred out to be white noise (model is fit)

Otherwise,restart the process

Stage 2 :Model Estimation and Diagnostic Testing

Estimation of parameter of selected AR and MA forms included in the model

Forecasting the series based on ARIMA model

Check the accuracy of the forecast

statistical measures will be used

Stage 3 : Model Application

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have a reliable regression tests to make sure that the CPI inflation forecasting model could not be subjected to Spurious Regression.

At the last stage, to compare the accuracy of various model, a statistical measures of Mean Squared Error (MSE) will be used.

In practical term, to make the series stationary requires performing three

processes: removing the trend, having a constant variance and finally, removing the seasonality.

Non-seasonal

A simple case of the model as represented by ARIMA(1,1,1) is written as ,

(1) = + +

Where wt =yt yt-1 represents the first difference of the series and is assumed stationary. In this case, the values of p = d= q = 1

Equation (1) can also be written as,

= + (2) = +

Assuming mean, = and substituting wt = ( 1 B) yt then equation (2) becomes,

=

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Expanding both sides we have,

+ = + = Now moving all the lag variables to the right, the equation can now be written as,

= + ARIMA with Seasonal Component

For series with seasonal component , then additional differencing is necessary to performed in order to eliminate the seasonality effect ( seasonal differencing).Let letter S denotes the seasonality component

For general equation can be represented as:

I. For monthly data series SARIMA (p,d,q)(P,D,Q)12 Let zt be the seasonally differenced series such that zt = yt yt-12

II. For quarterly data series SARIMA (p,d,q)(P,D,Q)4 Let zt be the differenced series such that zt = yt yt-4

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3.4 Calculation of the accumulated value of the individuals contribution under Employee Provident Fund (EPF)

According to Stephen G. Kellison (2009), rates of interest are positively correlated with rates of inflation.

+ = + + Where; = is the current interest rates, = is the current inflation rates, and; = is the real rates of interest.

Therefore, to calculate the future value of the participants at retirement age if the

individuals make a contribution the EPF, we will use this model; = . + = [( . + . ) + ]= Where; = Accumulated value of the fund in the individual account in EPF; = The percentage of the employees contribution under EPF;

= The percentage of the employers contribution under EPF; = The actual monthly salary of the individual; r = retirement age; and

e = entry age.

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= real rates of interest at time k

3.5 The simulation of future income for retirees.

Simulation is a unique type of modeling that simplifies a system or structure. When run, it will generate outputs and the aim is to predict any future trends and gaining a better understanding of some features in the social world (Gilbert and Troitzsch, 1999b).

It is best to conduct simulation models using a computer in order to allow the simulation of complex calculations. Different types of model serve different purposes or answer different research questions. For example, certain simulation methods are used to project future social and economic outcomes based on a set of parameters and the impact of social policy. The main aim of a micro simulation model is to analyze the possible impact of policy change upon household regardless of what type of simulation used (Harding and Gupta, 2007).

A model that produces a simulation based on individuals and their different characteristics could be categorized as a hypothetical simulation model. Hypothetical models are normally used to examine and explore the output of certain characteristics for different individuals. According to Joshi et al. (1996), if the inquiry is about what will happen to someone over their lifetime, some artificial time needs to be created in order

to simulate the hypothetical lifetime. In certain situations where the data is incomplete, this type of simulation model is considered an advantage because it does not require a complete life history data to obtain outcomes for each individual.

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Based on the objective for each model, the characteristics are set for each hypothetical model individually. In order to calculate the outcome required, each hypothetical individual can have any characteristic set as the parameter (Evans and Falkingham, 1997). However, hypothetical model has its own weaknesses too. Even though the characteristics are set to represent an individuals life characteristics, they may not show the individuals real life background and outcome in the real world (Joshi et al., 1996; Evans and Falkingham, 1997).

In 1997, Evans and Falkingham conducted a study using a hypothetical simulation model named PHYLIS (Pensions and Hypothetical Lifetime Income Simulation) to examine the consistency of the pension outcome for six different countries, i.e. the United Kingdom, Italy, Sweden, Poland, Chile and Australia. The results of the countries which use Defined Contribution Pension Plan paired with a non-existing fully funded system showed that the replacement rate levels were the highest among low-paid workers compared to those who had no breaks during employment. A study conducted by Rake et al. (1999) used a more up-to-date version of the PHYLIS model in order to investigate low-income individuals and their partners pension outcomes by changing certain assumptions in the model. The same thing is applied to the simulation method used in this research. From the second stage onwards, the simulation model was developed by considering flexible assumptions in the parameters.

Rake et al. (2000) stated that a hypothetical simulation model will allow a simulation to be carried out more thoroughly and able to explore in more detail on the impact of the policy towards individual outcomes.

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One of the weaknesses for using this model is that the results and findings derived from such a model do not represent the exact pension outcomes. They only illustrate the outcomes that may emerge from Malaysias current pension schemes for hypothetical individuals with the same characteristics. Another way of saying is that the result from the simulation model will show the level of an individuals retirement savings and monthly retirement income they might expect to have based on different characteristics. Some of the characteristics are different education levels, different employment history, and different retirement account activities. Retirement account activities consist of contribution rates and pre-retirement withdrawals. It is no possible to generalize from the results because they are highly sensitive to the choice of hypothetical parameters (An, 2004). Due to a lack of longitudinal data, this research used a hypothetical simulation model to predict and examine the effectiveness of Malaysias pension system and to explore the outcomes for different individuals at retirement

By choosing this type of model, it enabled a more sophisticated and complicated analysis of hypothetical individual life histories; that is a study on the impacts of different types of employment histories on retirement income and an investigation of the impacts of factors such as retirement age, contribution rates and pre-retirement withdrawals on estimated retirement income. Other than that, in order to run a hypothetical simulation model, it did not require a complete set of data compared to other simulation models like static or dynamic model.

It is a norm that employees will choose to receive a lump sum upon reaching

retirement age even though a survey has shown that 70% of retirees use up all of their EPF money within the first three years of retirement (EPF, 2008). This study estimated an actuarial value for an annuity plan. The total savings upon retirement are converted into an annuity plan, similar to previous research conducted by Samad and Kari (2007).

24

Narayanan (2002) conducted a study on the adequacy of the EPF fund upon reaching retirement. One of the reasons in his study for not having an adequate income during retirement was the high number and amount of pre-retirement withdrawals that were made during employment or known also as the retirement preparation phase. However both Naraynan (2002) and Samad and Kari (2007) used a completely different way to study the adequacy of Employees Provident Fund (EPF) balances at retirement. Narayanan (2002) calculated the adequacy of retirement income using the total contributions and the balances based on the contribution sizes as reported in the EPFs Annual Report. Samad and Kari (2007) used the salary range of the members to calculate it into a monthly annuity. However, it did not reflect the characteristic of each individuals salary or the contributions made because the method was based on the overall salary and contribution ranges instead of individuals.

Hence, the simulation model used is relevant to the purpose of this study which is to answer the research question, whose objective is to analyze the relationship between inflation and the amount of contribution and to calculate the adjusted contribution for Malaysian private workers.

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Figure 3.2 Process of Hypothetical Simulation Model

Code Excel formulas in the Excel

Spreadsheets

Create Hypothetical Life Course

Age starts working

Contributions and Retirement Income

Accumulated from EPF

Pre-Retirement

Withdrawal

Converts into Monthly Annuity Based on

Replacement Ratio

Compare with Retirees Future Lifetime Is it sufficient?

Calculate Total Years of Annuity Term

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Objectives of the simulation model:

For this paper, a hypothetical life-course simulation model approach was employed. The seven main objectives designed to be achieved for this model are:

i) To calculate the monthly annuity from the accumulated fund in the EPF and the monthly pension for the Pension Scheme; either with full employment or with

disruptions in employment years ii) To generate the estimated accumulated retirement income at different retirement

ages

iii) To generate the estimated accumulated retirement income by increasing the contribution rates

iv) To generate the estimated accumulated retirement income by making preretirement withdrawals from Account 2; by making single and two-phase withdrawals

v) To generate outcomes (ii-iv) through interactions between retirement age and pre-retirement withdrawals, between contribution rates and retirement age, and between pre-retirement withdrawals and contribution rates

vi) To calculate the Replacement Rate Level (RR) for the different factors used (iiv) and to determine the Poverty Level (PL)

vii) To generate the estimated accumulated retirement income with receiving credit pension credit contribution from the Government for unemployed women or women with disruptions due to care-taking responsibilities (this includes taking care of their children and elderly family members).

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In order for this simulation to be successful, there were a few important parameters that needed to be factored in. These parameters, which have been developed earlier during the early stage of the model development, were:

i) retirement age ii) contribution rates iii) pre-retirement withdrawals amount iv) EPF real rate of return v) annuity return vi) last drawn salary vii) total number of years of employment viii) salary grade

3.5.1 Assumptions used in the Hypothetical Life Course Simulation

1. EPF interest rate is 6.35% yearly Based on statistic report from Employee Provident Fund (EPF) 2014, the

interest rate reported in the financial statement in 6.35% annually. This indicated that the accumulated fund of contribution gain from employee and employer had been invested to another financial institution and raised their accumulated fund by 6.35% annually.

2. Starting age from 24 until 27

The period of the contribution for Employee Provident Fund (EPF) was influenced by the starting age when the worker started to employ by company. In this simulation model, the starting age of worker had been set from 24 years old to 27 years to make sure the fund in is sufficient to support their post-retirement life.

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3. Fresh graduate or first degree holder The level of education was used in determination of starting salary when

employee starts to work. It is important in order to calculate the total contribution in their fund. The starting salary that been used in this simulation is between RM2500 to RM2800.

4. Male full employed until retirement age which is 60 Statutory retirement age used in this simulation model was based on the

current retirement age in Malaysia which is 60 years old. This helps to calculate the total contribution in term of determine the period of full time worker from their starting age until reach full retirement term.

5. Salary grade is based on HIS data 2012 Household Income Survey (HIS) 2012 was used to calculate average

monthly salary and average yearly salary in each workers age in order to determine the total contribution in EPF at the retirement age.

6. Annuity interest rate is 3% based on past research paper Annuity interest rate is important to calculate the annuity that the

employee can get after the retirement. 3% interest rate been used to determine the number of years that the total fund can support the employee post-

retirement life expenditure based on the annuity value that influenced by the replacement ratio

.

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7. Replacement ratio is 50% based on maintaining current expenditure Replacement ratio was calculated from monthly income after retirement

divide with monthly last drawn salary. The percentage is determining the sufficiency value of income after retirement that can maintain with the current

expenditure during works.

8. Using 3% and 3.5% rate for salary increment Salary grade was used in the hypothetical simulation in order to calculate

the total contribution of the employee and employer in funding the EPF. The percentage of the salary increment which is 3% and 3.5% had been suggested by the previous research paper because of the limitation of the data that been used.

9. Male mortality for Malaysian is at age 79 years based on Global Age Watch Index 2014 from Department of Statistic Malaysia and Social Security Administration USA 2014

When the male worker reach their retirement age which is 60 years, based on Global Age Watch Index they can survive up to age 79 years old means that they can survive approximately 19 years after proceed their retirement.

10. Pre-retirement Withdrawal If the individual used the account 2 from EPF, the assumptions used in

hypothetical simulation are based on past research, Mazlynda(2012), which is 30% of the amount in EPF account will be withdraw after 5 years they start employed, and based on table 3.1 below they pre-retirement withdrawal for the purpose of financing ones childrens education, at the age of 18, because

30

children graduate from high school at 17 and are expected to further their undergraduate studies at the age of 18.

Table 3.1: Age of Second Pre-Retirement Withdrawal

Assumptions for age starts

working

Assumptions for age at

second withdrawal

Age 24 years old and below 38 years old

Between age 25 to 29 years

old 43 years old

3.5.2 Compare the results by scenarios with different assumptions

There are eight scenarios involved with different assumptions and parameters which is age start working (24 to 27) and either they do pre-retirement withdrawal or not;

i. Scenario A: Age starts working 24, without doing any withdrawal from account 2 EPF.

ii. Scenario B: Age starts working 24, with pre-retirement withdrawal. iii. Scenario C: Age starts working 25, without doing any withdrawal from

account 2 EPF.

iv. Scenario D: Age starts working 25, with pre-retirement withdrawal. v. Scenario E: Age starts working 26, without doing any withdrawal from

account 2 EPF.

vi. Scenario F: Age starts working 26, with pre-retirement withdrawal. vii. Scenario G: Age starts working 27, without doing any withdrawal from

account 2 EPF.

viii. Scenario H: Age starts working 27, with pre-retirement withdrawal.

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CHAPTER 4

RESULTS AND FINDINGS

4.0 Introduction

In this chapter, SARIMA regression model and Hypothetical simulation model as explain in chapter 3 will be used to measure the inflation trend and sufficiency of the

total contribution in Employee Provident Fund (EPF). The simulation model is been supported with knowledge of actuarial study regarding to calculate the salary increment, total contribution in the fund, rate of return of investment and monthly annuity payment after retirement.

4.1 Research Data

This study conducted by using secondary data of Consumer Price Index (CPI) and Household Income Survey (HIS) 2012. The data is obtainable from Malaysian Statistic Department.

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4.3 Inflation Forecasting

4.3.1 Analysing and Fitting the Malaysias Inflation Data series Step 1: Initial Data Investigation

Table 4.13 which can be retrieve at Appendix, shows the data of Malaysias monthly rates from January 1995 to November 2014, totalling two hundred and thirty nine (239) monthly observations. The data were obtained from National Institute of Statistics Malaysia. Figure 4.1 and Figure 4.2 show the plot of Malaysias monthly inflation and the trend analysis plot respectively. Figure 4.3 and Figure 4.4 also describe the features of the data that is the autocorrelation (ACF) plot and the partial autocorrelation plot respectively.

Figure 4.1 Time Series Plot Of Malaysias Monthly Inflation

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Figure 4.2 Trend Analysis Plot of Malaysias Monthly Inflation

Figure 4.3 Autocorrelation Plot of Malaysias Inflation

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Figure 4.4 Partial Autocorrelation Plot of Malaysias Inflation

At the initial stage, a simple data investigation was conducted to understand the basic pattern of the series and hence to identify any unusual observation or characteristic existing. This is done by constructing a simple time plot and fitting a linear trend line. A look at time series plot of the original data in Figure 4.1 implies that the series is non-stationary. Other than that, the trend analysis as shown in Figure 4.2 shows a decreasing trend but there was a sudden increase around the mid-year of 2008 and a sudden decrease of inflation rate around the mid-year of 2009. However, the ACF plot as shown in Figure 4.3 shows the wave like pattern and to a slightly lesser extent in Figure 4.4 and also tails off at lag 2.

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Step 2: Perform the Seasonal Differencing

Since this is a monthly series, the seasonal difference is given as zt = yt yt-12. By observing Figure 4.6 and 4.7, it can be concluded that the series in seasonal difference, zt, is not yet stationary. Note the ACF and PACF which depict the decaying and undulating characteristics.

Figure 4.5 Time series Plot in Seasonal Difference

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TIME SERIES PLOT IN SEASONAL DIFFERENCE

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Figure 4.6 The Autocorrelation Function of zt

Figure 4.7 The Partial Autocorrelation Function of zt

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Step 3: Perform Non-seasonal Differencing

During this step the non-seasonal differencing, wt = zt zt-1 was performed and the time plot was obtained. The time plot in Figure 4.8 does not show the presence of trend and there is stationarity in mean whilst the ACF indicates significant spikes at certain lags. In fact, Figure 4.9 and Figure 4.10 confirmed that the series is now stationary.

Figure 4.8 Time Series Plot of Wt

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TIME SERIES PLOT FOR NON-SEASONAL DIFF

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Figure 4.9 The ACF of Wt

Figure 4.10 The PACF of Wt

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In order to determine the best model formulations to be fitted to the data series, any significant spike(s) in Figure 4.9 and Figure 4.10 will be observed. Since, the series contains the seasonal component then the general formulation is written as SARIMA (p,d,q)(P,D,Q)12.

To identify for non-seasonal part, the significant spikes at lag other than 12, 24, 36 etc. is observed.

On the other hand, for the seasonal part, the ACF and PACF will be observed for any spikes at lag 12 or 24 or 36, though lags 36 and more are not common for most series.

As stated earlier, it is not easy to identify the exact and correct model for formulation due to the nature of the economic/business data series. Hence, several models that could be best possible formulations are identified and estimated.

Step 4: Models Identified

From Figure 4.9, one significant spike is observed. One at lag 1. This one spike can be used to specify the non-seasonal MA part of the model. Another significant spike is also observed at lag 12 to suggest the seasonal SMA part of the model.

Similarly, to identify the Autoregressive part of the model, the PACF in Figure 4.10 will be observed for any spikes. There are three significant spikes observed, one at lag 1, one at lag 7 and the other at lag 13 to suggest the non-seasonal AR part of the

40

model. There are also significant spikes at lag 12, 24, 36 to indicate the seasonal SAR part of the model.

However, even with these observations made we cannot be perfectly sure of the correct values of the respective p, q, P and Q that can be assigned to the model. Several models of formulations will be identified and estimated to ensure that a well specified model is formulated. Consequently, by using the statistic available from the Minitab software a final decision will be made on the best model formulation. The output from each model can be refer at the appendix section.

SARIMA(3,1,2)(3,1,1)12

SARIMA(2,1,2)(2,1,1)12

SARIMA(3,1,1)(3,1,1)12 SARIMA(2,1,1)(3,1,1)12

Model of formulations Mean Squared Error (MSE)

SARIMA (3,1,2)(3,1,1)12 0.1665 SARIMA (3,1,1)(3,1,1)12 0.1777 SARIMA (2,1,2)(2,1,1)12 0.2500 SARIMA (2,1,1)(3,1,1)12 0.1764

Table 4.1 The result of Mean Squared Error (MSE) output for each model

From Table 4.1, in order to choose the best model from the four models is by looking at the model that have the lowest value of Mean Squared Error(MSE). The best model is SARIMA (3,1,2)(3,1,1)12 have the lowest value of MSE which is 0.1665 .

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4.3.2 Results of the Inflation Rates Forecast Values

Using the ARIMA Model, this study is able to forecast the future inflation by referring to the inflation rates for the past 20 years (Jan 1995 Dec 2014). Below are the forecast values from December 2014 until December 2019.

Table 4.2 Inflation Forecast from December 2014 until December 2019

Period Month/Year Forecast Period Month/Year Forecast

240 Dec/14 3.00% 271 Jul/17 3.62%

241 Jan/15 3.09% 272 Aug/17 3.63%

242 Feb/15 3.26% 273 Sep/17 3.70%

243 Mar/15 3.36% 274 Oct/17 3.72%

244 Apr/15 3.36% 275 Nov/17 3.78%

245 May/15 3.40% 276 Dec/17 3.88%

246 Jun/15 3.55% 277 Jan/18 3.94%

247 Jul/15 3.67% 278 Feb/18 3.94%

248 Aug/15 3.64% 279 Mar/18 3.96%

249 Sep/15 3.86% 280 Apr/18 3.99%

250 Oct/15 3.64% 281 May/18 3.99%

251 Nov/15 3.41% 282 Jun/18 4.04%

252 Dec/15 3.44% 283 Jul/18 4.01%

253 Jan/16 3.34% 284 Aug/18 4.02%

254 Feb/16 3.11% 285 Sep/18 3.97%

255 Mar/16 3.08% 286 Oct/18 4.02%

256 Apr/16 3.18% 287 Nov/18 4.05%

257 May/16 3.26% 288 Dec/18 4.04%

258 Jun/16 3.19% 289 Jan/19 4.09%

259 Jul/16 3.05% 290 Feb/19 4.14%

260 Aug/16 3.05% 291 Mar/19 4.20%

261 Sep/16 3.17% 292 Apr/19 4.22%

262 Oct/16 3.28% 293 May/19 4.23%

263 Nov/16 3.25% 294 Jun/19 4.29%

264 Dec/16 3.20% 295 Jul/19 4.33%

265 Jan/17 3.28% 296 Aug/19 4.34%

266 Feb/17 3.43% 297 Sep/19 4.42%

267 Mar/17 3.52% 298 Oct/19 4.41%

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268 Apr/17 3.50% 299 Nov/19 4.38%

269 May/17 3.46% 300 Dec/19 4.41%

270 Jun/17 3.53%

All the monthly inflation rates projected are adjusted into yearly rates.

Table 4.3 Annual Average Inflation Rate from 2014 until 2019

No Year Annual Average Inflation Rate (%)

1 2014 3.17

2 2015 3.47

3 2016 3.18

4 2017 3.59

5 2018 3.40

6 2019 4.29

Throughout the 6 years, the data only show positive result. This means that inflation rate will continue to rise in the future. While for highest inflation rate for both monthly and annually are shown in Table 4.4.

Table 4.4 Highest and Lowest Inflation Rate for Monthly and Annually

Monthly Annually

Highest

Inflation Rate 4.42% 4.29%

Lowest

Inflation Rate 3.00% 3.17%

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Figure 4.11 Time Series Plot for Inflation with Forecast

As shown in Figure 4.11, the forecast values are in increasing pattern from

December 2014 until December 2019(time: 240 300). By generalizing the rule of thumb, the inflation rates are estimated to gradually continue to increase throughout the year.

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4.4 Findings on Hypothetical Simulation Model

In this section, eight scenarios are analyzed, and each scenario is analyzed based on the results presented in the tables at the beginning of each scenario. All the assumptions in each scenario are stated in Chapter 3, section 3.5, the assumption of hypothetical life course. The results are analyzed in order to identify what range of working age and pre-withdrawal of accumulated fund will makes the annuity income is sufficient to cover retirees until the term of annuity income is exceeds the future lifetime of retirees.

Calculation on average initial salary and table of salary grade for man who starts working at age 24, 25, 26 and 27 are provided in Appendix from Table 4.21 to Table 4.24. Besides that, each of scenarios is provided a table of accumulated fund in EPF at different age starts working and with or without pre-retirement withdrawal in detail in appendix.

This scenario is simulated to explore the change in parameters such as age starts working, what inflation rate, and which salary increment will make the annuity term with annuity monthly based on replacement ratio will sufficient to cover the retirees until age 79 future lifetime of retirees age 60. The sufficiency test is obtained by comparing both retirees future lifetime and total years received from annuity.

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Table 4.5 Result for Scenario A

Result Replacement Ratio 0.50 Last drawn salary 8821.22 Monthly received after retirement 4410.61 Dividend Rate Annuity 0.03 Total Amount Received from EPF at Age of Retirement (60) 1105448.90 Total Number of Payment Received 390.82 Total Months 32.57 Balloon Payment 434.11

Table 4.5 shows the output from the hypothetical simulation for scenario A which is individual who age starts working at age 24 full employed until retire age of 60 and without withdraw his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4410.61 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM1105448.90. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 32 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.6 Result for Scenario B

Result Replacement Ratio 0.50 Last drawn salary 8821.22 Monthly received after retirement 4410.61 Dividend Rate Annuity 0.03 Total Amount Received from EPF at Age of Retirement (60) 913389.47 Total Months Received 290.21 Total Years Received 24.18 Balloon Payment 222.37

Table 4.6 shows the output from the hypothetical simulation for scenario B which is individual who age starts working at age 24 full employed until retire age of 60 and with pre-retirement withdrawal from his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4410.61 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM913389.47. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 24 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.7 Result for Scenario C


Table 4.7 shows the output from the hypothetical simulation for scenario C which is individual who age starts working at age 25 full employed until retire age of 60 and without withdraw his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4261.46 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM1027051.13. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 30 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.8 Result for Scenario D


Table 4.8 shows the output from the hypothetical simulation for scenario D which is individual who age starts working at age 24 full employed until retire age of 60 and with pre-retirement withdrawal from his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4261.46 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM811806.12. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 21 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.9 Result for Scenario E


Table 4.9 shows the output from the hypothetical simulation for scenario E which is individual who age starts working at age 26 full employed until retire age of 60 and without withdraw his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4117.35 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM940664.07. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 28 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.10 Result for Scenario F


Table 4.10 shows the output from the hypothetical simulation for scenario F which is individual who age starts working at age 26 full employed until retire age of 60 and with pre-retirement withdrawal from his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM4117.35 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM743172.42. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 19 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.11 Result for Scenario G


Table 4.11 shows the output from the hypothetical simulation for scenario G which is individual who age starts working at age 27 full employed until retire age of 60 and without withdraw his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM3997.43 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM877800.85. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 26 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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Table 4.12 Result for Scenario H


Table 4.12 shows the output from the hypothetical simulation for scenario H which is individual who age starts working at age 27 full employed until retire age of 60 and with pre-retirement withdrawal from his account 2 EPF. Based on his Replacement Ratio on his last drawn salary, he supposed to receive a monthly annuity income of RM3997.43 and based on his simulation EPF, the accumulated fund in EPF account at age 60 is RM696650.73. Based on the monthly annuity income and his total fund from EPF at age 60, the annuity could cover him for 19 years approximately after he retires. Since, total years in annuity are greater than expected future lifetime for retirees, 19 years, therefore, it is sufficient.

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CHAPTER 5

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

In chapter 4, the study has calculated the inflation trend in Malaysia using the Mixed Autoregressive Integrated Moving Average (ARIMA) Model. It can be concluded that generally the movement of the inflation will be in an upward or positive position. At lag 1, one spike was observed and used to specify the non-seasonal Moving Average part of the model. Throughout the six years of forecasted future inflation, all six years show positive result. The highest future inflation is at 4.29% and the lowest is at 3.17%. Hence it can be concluded that the inflation rate will continue to rise in the future.

This research study focuses mainly on maintaining purchasing power for middle income employee when they face retirement at old age. The targeted variable in this study is for male employee in Malaysias private sector and start their career at age between 24 to 27 years old. By using the templates and excel sheet that have been developed; this study is able to calculate and determine the salary increments that have been used to stimulate the average salary for male employee. The data needed on calculating the annuity of contribution, also known as pension are salary with increment rate, rate of contribution in Employee Provident Fund for employee and employer,

inflation rate for past 10 years and rate of investment or dividend rate in Employee Provident Fund. The rates that have been used in salary increment are in between 3% to 3.5% which is relevant in Malaysia scenario. In this study, the focus revolved around male because of the fact that male is the key member in the family or the main source of income before and after retirement. By considering the life expectancy for person age 60, the employee can survive up to 19 years until age 79 and they still need to support

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family expenditure till age 79. Based on the hypothetical model, this study can determine the sufficiency of the total contribution in the EPF to generate monthly annuity regarding with their life expectancy. This scenario is simulated to explore the change in parameters such as age starts working, what inflation rate, and which salary increment will make the annuity term with annuity monthly based on replacement ratio will sufficient to cover the retirees until age 79 future lifetime of retirees age 60. The sufficiency column in Table 4.8 until 4.23 is obtained by comparing both annuity terms with future lifetime of retirees age 60. Table 5.1 also explain briefly about the sufficiency of annuity due to the pre-retirement withdrawal factor. If annuity term exceeds future lifetime of retirees age 60, it indicates sufficient, and not sufficient if vice versa. Hence, it can be concluded that in general, the amount in the fund is sufficient throughout the retirees future lifetime.

Table 5.1 Conclusion for Hypothetical Model

Age

Starts

Working

Conclusion

Without Pre-retirement Withdrawal (Age)

With Pre-retirement

Withdrawal (Age)

24 92 84

25 90 81

26 88 79

27 86 79

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5.2 Limitations and Recommendations for Further Study

The study conducted by hypothetical simulation model was unable to gain access to particular data, means not accessible to the public. One of the weaknesses of the hypothetical simulation model is the inability to generalise the results as the model stimulate hypothetical scenario which may differ from an individuals real employment pattern. However, the fact that the assumptions for the model have been based on data and research for Malaysia scenario which the results are still possible in context when the hypothetical simulation modelling exercise takes place.

For the future study of the cases, another researcher should consider using another models, subjects to the data availability since the hypothetical simulation model used in this study enables researchers to explore different type or real life courses and parameters that may be experienced by man in Malaysia.

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APPENDICES

1) Steps to Genera

Documents

Final Report FYP