Ecomet Final

The Impact of Gross Fixed Capital Formation Growth on the Economic Growth of the Philippines: A Time Series Approach

An Empirical Paper

Presented to

The Faculty of the School of Economics

De La Salle University

In Partial Fulfillment of the Requirements in ECOMET2

Submitted by:

Ibas, Shaira Marie S.

11239190

Submitted to:

Dr. Cesar Rufino

Table of Contents ...................................................................................................................................................................... 1

INTRODUCTION ............................................................................................................................................ 5

Background of the Study ............................................................................................................................... 5

Statement of the Problem ......................................................................................................................... 6

Objectives of the Study ............................................................................................................................. 6

Significance of the Study ........................................................................................................................... 7

Scope and Limitations ............................................................................................................................... 7

REVIEW OF RELATED LITERATURE .............................................................................................................. 8

THEORETICAL FRAMEWORK ...................................................................................................................... 11

Neoclassical Growth Theory ................................................................................................................... 11

Solow Growth Model ................................................................................................................................ 11

OPERATIONAL FRAMEWORK ..................................................................................................................... 13

Data ............................................................................................................................................................. 13

Description of the Variables Used .............................................................................................................. 13

A-Priori Expectations ............................................................................................................................... 14

INITIAL TESTS .............................................................................................................................................. 16

Unit Root Tests ........................................................................................................................................... 16

Graph Analysis ............................................................................................................................................ 16

Dickey-Fuller Unit Root Test ........................................................................................................................ 17

Phillips-Perron Unit Root Test for GDP Growth ........................................................................................... 18

Test for Lag Structure ................................................................................................................................. 19

Hendry’s Top-Down Approach .................................................................................................................... 19

Alt and Tinbergen (Ad Hoc Estimation) Method ......................................................................................... 20

Test for Cointegration ................................................................................................................................ 20

Augmented Engle-Granger Test .................................................................................................................. 21

Johansen Cointegration Test ....................................................................................................................... 21

Test for Causality ........................................................................................................................................ 22

Augmented Engle-Granger Causality Test .................................................................................................. 22

STATIC MODEL ............................................................................................................................................ 25

Initial Static Model ..................................................................................................................................... 25

Variance Inflation Factor Test for Multicollinearity .................................................................................... 25

Breusch-Pagan Test for Heteroscedasticity ................................................................................................ 26

Breusch-Godfrey Test for Autocorrelation .................................................................................................. 26

Final Static Model ....................................................................................................................................... 27

Prais-Winsten Robust Regression for Corrective Measures ........................................................................ 27

Final Static Model ....................................................................................................................................... 28

DYNAMIC MODELS ..................................................................................................................................... 29

General Distributed Lag Model .................................................................................................................. 29

Initial General Distributed Lag Model ......................................................................................................... 29

Initial Regression ......................................................................................................................................... 29

Test for Multicollinearity ............................................................................................................................ 29

Test for Heteroscedasticity ......................................................................................................................... 29

Test for Autocorrelation ............................................................................................................................. 30

Prais-Winsten Robust Regression for Corrective Measures ........................................................................ 30

Final General Distributed Lag Model .......................................................................................................... 31

Koyck Infinite Distributed Lag Model ........................................................................................................ 31

OLS Estimation ............................................................................................................................................ 32




Final Koyck Infinite Distributed Lag Model Using OLS ................................................................................ 33

Two-Stage Least Squares (2SLS) Estimation ............................................................................................... 35


Final Koyck Infinite Distributed Lag Model Using 2SLS ............................................................................... 36

Almon Polynomial Distributed Lag (PDL) Model ....................................................................................... 38





Prais-Winsten Robust Regression for Corrective Measures ....................................................................... 39

Final Almon Polynomial Distributed Lag Model .......................................................................................... 40

Autoregressive Distributed Lag Model (ARDL) .......................................................................................... 41





Final Autoregressive Distributed Lag Model ............................................................................................... 42

CONCLUSIONS AND RECOMMENDATIONS ............................................................................................... 43

BIBLIOGRAPHY ............................................................................................................................................ 44

INTRODUCTION

Background of the Study

In terms of finding ways on how to improve an economy’s performance, investment

has always been one of the most common instrument. Investment is usually measured

through gross fixed capital formation, also called net investment, which is defined as the

“net amount of the fixed capital acquired.” It indicates the rise in the capital stock minus

the removal of fixed assets (Pettinger, n.d.). At the one hand, economic performance is

not only determined by investments because of there are other factors such as

consumption, government expenditures, exports and imports (Blanchard & Johnson,

2013). Given that, it is possible that it is not investment on buildings, machineries,

infrastructures and equipment that leads to an increase in the growth of the economy,

and that it may be the growth of the economy which spurs the increase in the capital

formation of an economy. This issue has actually been in numerous literatures for quite

some time already. In fact, in one of Kuznet’s studies, it was said that there are situations

wherein economic growth happens before the capital increases (Blostrom, Lipsey &

Zejan, 1993).

The possibility that investments may not affect economic growth and the possibility

that investment determines economic growth will be discussed in the Review of Related

Literature of this paper wherein studies conducted in different countries and regions will

show varying results.

Statement of the Problem

This study aims to answer a number of questions regarding the relationship of

gross fixed capital formation growth and GDP growth. These questions are the following:

1. Is the gross fixed capital formation growth significant in determining the GDP

growth of the Philippines?

2. Is gross fixed capital formation growth a driver GDP growth or is it GDP growth

that is a driver of the gross fixed capital formation growth?

3. How long does gross fixed capital formation growth formation take in order for it to

affect GDP growth?

4. How can the Philippines improve its economy in terms of investments or capital

formations?

Objectives of the Study

This study aims to:

1. Determine the relationship of gross fixed capital formation growth and GDP

growth using dynamic econometric models.

2. Find out the causality between gross fixed capital formation growth and GDP

growth and determine whether the gross fixed capital formation are crucial to

GDP growth in the long-run.

3. Determine how long gross fixed capital formation growth will affect GDP growth.

Significance of the Study

This paper aims to show the relationship of gross fixed capital formation growth

and GDP growth because there may be a need to reconsider the policies of the

government like improving the physical and social infrastructures such as road and

railway constructions or renovations and school and hospital constructions. Given that

Philippines aims to develop and increasing investments and capital formations are taken

into consideration as instruments, this paper will be able to show its possible effects on

the economic growth. Given this, the paper may serve as a basis in future policy-making

with regards to determining how much should be released for capital formation to make

the economy grow further.

Scope and Limitations

This study concentrates on gross fixed capital formation as a determinant of

economic growth, thus, not taking into account other factors such as consumption,

government expenditure, exports and imports. In this study, the data used were taken

from World Bank’s World Development Indicator covering years 1961 to 2013 which

means that the dataset consists of 53 observations for both gross fixed capital formation

growth and GDP growth.

REVIEW OF RELATED LITERATURE

Capital formation is an established determinant of the economic growth. Given this,

numerous studies were done that used it as an explanatory variable for GDP. The

remaining parts of this section will discuss studies that will show the relation of gross fixed

capital formation and GDP.

Some studies say that it is investments that affect GDP growth. One of which is

the study in India where it was observed that the current investment rate has increased

dramatically since the 1950s. Because of the said increase in investment rate, the

researchers evaluated its effects on the growth of the economy. The results of the said

study showed that although the investment rate is high, its contribution to the GDP per

worker in India is not that high. Furthermore, the study showed that the high investment

rate today will not have a huge effect on the economic growth of India in the future and

that higher investment rates in the future may also have small effect on the growth of the

economy (Robertson, 2010). Another study that poses the same result is the study of De

Long and Summers (1991). Using the machinery and equipment investment and GDP

growth data from 1960 to 1985, the result indicated that a percent increase in GDP that

is spent for machineries and equipment will increase GDP growth by one-third of the

recorded annual percentage point.

There are also studies that say that it is GDP growth that affects investments. In

the study of Blomstrom, Zejan and Lipsey (1993). From the said study, investment was

measured in terms of fixed capital formation contribution to GDP. Simple and multiple

regressions were done using different factors that affect GDP growth. Aside from this,

causality test was also done and it showed that the effect of increase in fixed capital

formation towards the increase in GDP growth is lesser than the effect of increase in GDP

growth towards the increase in fixed capital formation. The researchers also found out

that high fixed capital investment rates contributes to boosting the GDP per capita growth,

however, it is not the only determinant of GDP growth. A similar study is done using the

gross domestic investment and GDP data for Middle East and North Africa (MENA) for

years 1970 to 2010. The said study of Mehrara and Muhsai (2013), indicated that there

is unidirectional causality between GDP and gross domestic investment such that it is

GDP that affects gross domestic investment. Thus, having higher GDP will allow higher

gross domestic investments.

Aside from determining which variable affects which, researchers have also been

interested in determining whether GDP growth and capital formation have long-run and

short-run relationship. A study was conducted in Nigeria which showed that gross fixed

capital formation has no short-run relationship to GDP growth. In addition, the same study

used vector autoregression to determine the long-run relationship of the two variables.

The results showed that gross fixed capital formation, exports and the lagged GDP growth

have long-run relationship with the current GDP growth. The same study also showed

that GDP has a unidirectional causal relationship with exports, gross fixed capital

formation, import and savings (Kanu & Ozurumba, 2014). Another study that showed the

short-run and long-run relationship of capital formation and economic growth is the study

of Mehta (2011). The study used data from India and employed cointegration and vector

error correction model to determine the short-run and long-run relationship of capital

formation and GDP. The cointegration test results showed that capital formation and GDP

have long-run relationship. However, the vector error correction technique yielded

insignificant coefficients which implies that the lagged values of the logarithm of GDP do

not affect capital formation.

Given the related literature discussed, this study aims to fill the gap in the lack of

empirical analysis on the relationship of GDP growth and gross fixed capital formation

growth in the Philippines.

THEORETICAL FRAMEWORK

When talking about investments, what are taken into consideration are business

fixed investments, residential investments and inventory investments. These investments

pertain to equipment, machines, houses, raw materials, work in progress and finished

goods that are stored (Mankiw, 2012). Given this definition, and the fact that gross fixed

capital formations are comprised of land improvements, machinery, equipment and

construction spending, then it is an investment.

Neoclassical Growth Theory

The neoclassical growth theory was first developed by Robert Solow and J.E.

Meade in the late 1950s and early 1960s. The said theory gives emphasis on the

relationship of capital accumulation and savings to economic growth. Specifically, the

theory states that there are two main factors of production such as labor and capital. Later

on, technology was also added in the production function. The claim that output affects

capital accumulation is presented through the Solow growth model.

Solow Growth Model

This model was created by Robert Solow in 1956 which showed the

factors that affect economic growth. From the Solow growth model, it can be

concluded that in the long run, the level of capital determines the level of output

produced. In addition, the level of output determines the level of savings and

investments, and level of capital accumulation. By holding the population,

participation rate and unemployment rates constant and that there is no

technological improvements, it can be said the increases in capital per worker

will increase output per worker. On the other hand, because private savings

equate to investment and is in proportion with income, then investment is in

proportion with income as well. Thus, when the output increases, savings also

increase which in turn increases the investments. Moreover, if it happens that

the capital per worker and output per worker are low, and investment is greater

than the value of depreciation, it will result to increase in capital per worker

and output per worker as time goes by. However, if capital per worker and

output per worker are high, and investments is less than the value of

depreciation, then capital per worker will decrease as time goes by (Blanchard

& Johnson, 2013). These relationships can be shown through the table below

such that the green arrow shows the first relationship, while the blue and

purple arrows indicate the second relationship.

Figure 3.1: Capital, Output and Saving/Investment (Blanchard & Johnson, 2013).

OPERATIONAL FRAMEWORK

Data . tsset year

time variable: year, 1961 to 2013

delta: 1 unit

In using dynamic models in econometrics, it is essential to ensure that the data

used is time series data. In this study, the data for the Philippine gross fixed capital

formation and economic growth are taken from the World Bank and it covers years 1961

to 2013, giving us 53 observations. Before proceeding with the tests and regressions, the

command tsset is used in Stata 12 in order to set the variables into time series form.

To show the relationship between gross fixed capital formation and GDP growth,

the data should be tested in terms of its stationarity, cointegration and causality which will

be shown in the next chapter of this paper. The table below shows the summary of all the

variables in the study.

. summarize

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

year | 53 1987 15.44345 1961 2013

gfcfgr | 53 5.435404 11.0547 -31.49475 31.98448

gdpgr | 53 4.167703 3.027177 -7.323683 8.920647

Description of the Variables Used

It is essential to identify the dependent variable and independent variable and its

specifications first before dealing with the tests and the regressions. The table below

shows the descriptions of the variables used.

DEPENDENT VARIABLE

gdpgr Gross domestic product

growth

It is the annual percentage

change of GDP which is

based on the constant

local currency.

INDEPENDENT VARIABLE

Variable Label Description

gfcfgr Gross fixed capital

formation growth

It is the annual percentage

change of gross fixed

capital formation which

consists of land

improvements,

machineries, equipment,

construction spending, etc.

at market prices based on

the constant local currency.

A-Priori Expectations

INDEPENDENT VARIABLE

ALGEBRAIC SIGN EXPLANATION

gfcfgr + More fixed capital formation

will mean that more roads

and facilities will be

constructed. Given this,

transferring of goods will be

faster and thus, increasing

the income of the country.

The model will be written as:

𝑔𝑑𝑝𝑔𝑟 = 𝛽1 + 𝛽2𝑔𝑓𝑐𝑓𝑔𝑟 + 𝑢𝑡

INITIAL TESTS

Unit Root Tests This is also known as the test for stationarity which is a requirement in a time

series analysis. The reason behind this is that non-stationary data will lead to results

that can be used for making conclusions. Therefore, it means that if a data is non-

stationary, the results that will be taken from different periods will be different because

the mean and variance over time also differ (Gujarati & Porter, 2009).

In order to check the stationarity of gross fixed capital formation and GDP

growth, the graph of both variables will be analyzed. In addition, unit root testing such

as Dickey-Fuller test and Phillips-Perron test will also be used.

Graph Analysis

. tsline gdpgr gfcfgr

-40

-20

02

04

0

1960 1970 1980 1990 2000 2010year

gdpgr gfcfgr

It can be observed that the line graphs for both GDP growth (gdpgr) and gross

fixed capital formation (gfcfgr) do not show any trend or breaks that may lead to making

the data for variables to become non-stationary. Given that the graph shows that the data

for the variables are stationary, it is still best to use other tests to ensure that this

conclusion is correct.

Dickey-Fuller Unit Root Test . dfuller gdpgr

Dickey-Fuller test for unit root Number of obs = 52

---------- Interpolated Dickey-Fuller ---------

Test 1% Critical 5% Critical 10% Critical

Statistic Value Value Value

------------------------------------------------------------------------------

Z(t) -4.106 -3.577 -2.928 -2.599

------------------------------------------------------------------------------

MacKinnon approximate p-value for Z(t) = 0.0009

. dfuller gfcfgr

Dickey-Fuller test for unit root Number of obs = 52




------------------------------------------------------------------------------

Z(t) -5.360 -3.577 -2.928 -2.599

------------------------------------------------------------------------------


Given the Dickey-Fuller unit root test above, the result shows that both GDP growth

and gross fixed capital formation growth are stationary which means that they do not have

unit roots. Therefore, there is no need to get the first difference of both variables. This is

so because for GDP growth, the absolute value of the test statistic (-4.106) is greater than

the absolute value of the critical value at 5% (-2.928). On the other hand, for gross fixed

capital formation growth, the absolute value of its test statistic (-5.360) is also greater than

the absolute value of the critical value at 5% (-2.928). Furthermore, if the p-value will

serve as the basis to know the stationarity of the data, the result will also be consistent

as the p-value for Z(t) for both GDP growth and gross fixed capital formation growth are

less than 0.05 which means that the null hypothesis that there is unit root is rejected.

Phillips-Perron Unit Root Test for GDP Growth . pperron gdpgr

Phillips-Perron test for unit root Number of obs = 52

Newey-West lags = 3




------------------------------------------------------------------------------

Z(rho) -26.591 -18.936 -13.316 -10.712

Z(t) -4.105 -3.577 -2.928 -2.599

------------------------------------------------------------------------------


. pperron gfcfgr

Phillips-Perron test for unit root Number of obs = 52

Newey-West lags = 3




------------------------------------------------------------------------------

Z(rho) -35.877 -18.936 -13.316 -10.712

Z(t) -5.300 -3.577 -2.928 -2.599

------------------------------------------------------------------------------


The null hypothesis for the Phillips-Perron test is that there is non-stationarity or

that there is a unit root. In the case of GDP growth, the p-value of Z(t) is also less than

0.05 and the absolute value of the test statistic of Z(rho) (-26.591) is greater than the

absolute value of the critical value at 5% (-13.316). On the other hand, for the gross fixed

capital formation growth, the p-value of Z(t) is also less than 0.05 and the absolute value

of the test statistic of Z(rho) (-35.877) is greater than the absolute value of the critical

value at 5% (-13.316). Therefore, the result of the Phillips-Perron test implies that the null

hypothesis should be rejected, meaning there is no unit roots and the data is stationary.

Test for Lag Structure

It is important to determine the optimal lag structure to find out the longest period

of time that the lagged value of a variable can affect the current value of a variable (Hill,

Griffiths & Lim, 2011). To find out the maximum lag, Hendry’s top-down approach and

Alt and Tinbergen or the Ad Hoc Estimation Method will be used.

Hendry’s Top-Down Approach

. varsoc gdpgr gfcfgr

Selection-order criteria

Sample: 1965 - 2013 Number of obs = 49

+---------------------------------------------------------------------------+

|lag | LL LR df p FPE AIC HQIC SBIC |

|----+----------------------------------------------------------------------|

| 0 | -290.294 520.423 11.9304 11.9597 12.0076* |

| 1 | -282.624 15.34* 4 0.004 448.157* 11.7806* 11.8685* 12.0122 |

| 2 | -281.073 3.1029 4 0.541 495.815 11.8805 12.027 12.2666 |

| 3 | -278.582 4.9823 4 0.289 528.635 11.9421 12.1472 12.4826 |

| 4 | -277.269 2.6253 4 0.622 592.58 12.0518 12.3155 12.7468 |

+---------------------------------------------------------------------------+

Endogenous: gdpgr gfcfgr

Exogenous: _cons

The Final Prediction Error (FPE), Akaike Information Criterion (AIC),

Bayesian/Schwartz Criterion Information (SBIC) and Hannan-Quinn Information Criterion

(HQIC) will serve as basis to determine the optimal lag. The results from the table above

implies that FPE, AIC and HQIC shows that the optimal lag is one while SBIC shows that

the optimal lag is zero. Therefore, the optimal lag should be one since most of the

information criterion chose lag one.

Alt and Tinbergen (Ad Hoc Estimation) Method

. regress gdpgr gfcfgr l1.gfcfgr

Source | SS df MS Number of obs = 52

-------------+------------------------------ F( 2, 49) = 31.35

Model | 266.272143 2 133.136071 Prob > F = 0.0000

Residual | 208.105933 49 4.24705985 R-squared = 0.5613

-------------+------------------------------ Adj R-squared = 0.5434

Total | 474.378076 51 9.30153089 Root MSE = 2.0608

------------------------------------------------------------------------------

gdpgr | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

gfcfgr |

--. | .1987499 .0270498 7.35 0.000 .1443913 .2531084

L1. | .0222847 .0269705 0.83 0.413 -.0319145 .0764838

|

_cons | 2.975012 .3336207 8.92 0.000 2.304576 3.645448

------------------------------------------------------------------------------

Upon adding one lag of gross fixed capital formation growth to the regression, the

result showed that the lagged variable has a p-value of 0.413 which means that it is

insignificant. Therefore, the regression should stop at the first lag.

The results of both Hendry top-down approach and Alt and Tinbergen method are

consistent. Therefore, the optimal lag should be one.

Test for Cointegration

In this test, the movement of the variable together over time is considered to find

out whether there is a relationship between the two variables in the long run. This test

serves as a proof that the results are not spurious (Gujarati &Porter, 2009).

In this section, Augmented Engle-Granger Test and Johansen Cointegration Test

will be used to determine whether there is a long run relationship between gross fixed

capital formation growth and GDP growth.

Augmented Engle-Granger Test

. regress gdpgr gfcfgr


-------------+------------------------------ F( 1, 51) = 64.10

Model | 265.379461 1 265.379461 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5482

Total | 476.517688 52 9.16380169 Root MSE = 2.0347

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr | .2043549 .0255241 8.01 0.000 .1531132 .2555965

_cons | 3.056952 .3120249 9.80 0.000 2.430536 3.683368

------------------------------------------------------------------------------

. predict uhat, residual

. regress d.uhat l.uhat l.d.uhat


-------------+------------------------------ F( 2, 48) = 12.59

Model | 93.7753949 2 46.8876975 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.3167

Total | 272.541309 50 5.45082619 Root MSE = 1.9298

------------------------------------------------------------------------------

D.uhat | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

uhat |

L1. | -.7331103 .1651285 -4.44 0.000 -1.065123 -.4010971

LD. | .100608 .1420287 0.71 0.482 -.1849597 .3861758

|

_cons | -.0410718 .2703671 -0.15 0.880 -.5846813 .5025377

------------------------------------------------------------------------------

The result shows that the t-ratio of the lagged residual is -4.44 that is less than the

critical value at 5% for a cointegration regression which is -3.37. Given this, the null

hypothesis that the residuals in the least squares is non-stationary is rejected. Therefore,

the residuals are stationary, and GDP growth and gross fixed capital formation growth

are cointegrated.

Johansen Cointegration Test

. vecrank gdpgr gfcfgr, lags(1)

Johansen tests for cointegration

Trend: constant Number of obs = 52

Sample: 1962 - 2013 Lags = 1

-------------------------------------------------------------------------------

5%

maximum trace critical

rank parms LL eigenvalue statistic value

0 2 -323.88075 . 46.2615 15.41

1 5 -308.25257 0.45178 15.0052 3.76

2 6 -300.74999 0.25066

-------------------------------------------------------------------------------

The trace statistic of rank 0 or line 1 (46.2615) is greater than the critical value at

5% (15.41). In addition, the trace statistic of rank 1 or line 2 (15.0052) is also greater than

the critical value at 5% (3.76). Given these, there is a strong evidence against the null

hypothesis that there are equations that are not cointegrated, meaning that the variables

are cointegrated with each other. Therefore, GDP growth and gross fixed capital

formation growth are cointegrated.

Test for Causality

Given that the variables have long-run relationship and are stationary, the cause

and effect relationship of the variables may be determined through testing for causality.

Therefore, at this part of the paper the question of “Is it gross fixed capital formation

growth that drives GDP growth?” and “Is it GDP growth that drives gross fixed capital

formation growth?” will be answered.

To answer this question, vector autoregressive model is ran prior to running the

command to determine the Granger causality of the variables.

Augmented Engle-Granger Causality Test

. var gdpgr gfcfgr, lags(1)

Vector autoregression

Sample: 1962 - 2013 No. of obs = 52

Log likelihood = -300.75 AIC = 11.79808

FPE = 456.0393 HQIC = 11.88439

Det(Sigma_ml) = 361.9671 SBIC = 12.02322

Equation Parms RMSE R-sq chi2 P>chi2

----------------------------------------------------------------

gdpgr 3 2.69515 0.2497 17.30546 0.0002

gfcfgr 3 10.4742 0.1433 8.698708 0.0129

----------------------------------------------------------------

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

gdpgr |

gdpgr |

L1. | .6255078 .1813128 3.45 0.001 .2701412 .9808745

|

gfcfgr |

L1. | -.05017 .0493318 -1.02 0.309 -.1468585 .0465186

|

_cons | 1.835601 .6817613 2.69 0.007 .4993736 3.171829

-------------+----------------------------------------------------------------

gfcfgr |

gdpgr |

L1. | 1.434962 .7046394 2.04 0.042 .0538945 2.81603

|

gfcfgr |

L1. | -.0176717 .1917191 -0.09 0.927 -.3934342 .3580908

|

_cons | -.5381844 2.649541 -0.20 0.839 -5.73119 4.654821

------------------------------------------------------------------------------

. vargranger

Granger causality Wald tests

+------------------------------------------------------------------+

| Equation Excluded | chi2 df Prob > chi2 |

|--------------------------------------+---------------------------|

| gdpgr gfcfgr | 1.0343 1 0.309 |

| gdpgr ALL | 1.0343 1 0.309 |

|--------------------------------------+---------------------------|

| gfcfgr gdpgr | 4.1471 1 0.042 |

| gfcfgr ALL | 4.1471 1 0.042 |

+------------------------------------------------------------------+

The null hypothesis for the first box is that the lagged gross fixed capital formation

growth does not Granger-cause GDP growth. The prob>chi2 shows that insignificance at

5% because it is greater than 0.05. This means that there is no evidence against the null

hypothesis. Therefore, the lagged gross fixed capital formation growth does not Granger-

cause GDP growth. On the other hand, for the second box, the null hypothesis is that the

lagged GDP growth does not Granger-cause gross fixed capital formation growth. For

this, the prob>chi2 is 0.042 which implies that it is significant at 5%. This means that there

is a strong evidence against the null hypothesis. Therefore, the null hypothesis is rejected

which means that the lagged GDP growth Granger-cause gross fixed capital formation

growth.

The results show that gross fixed capital formation does not Granger-cause GDP

growth. Because of this, the rest of the paper will be looking at the correlation of the

variables instead of claiming that gross fixed capital formation growth Granger-causes

GDP growth since it may result to incorrect results and interpretations. Thus, GDP growth

will be the dependent variable and gross fixed capital formation growth will be the

independent variable.

STATIC MODEL

Initial Static Model The estimates shown below are determined using Ordinary Least Squares estimation

method.

. regress gdpgr gfcfgr


-------------+------------------------------ F( 1, 51) = 64.10

Model | 265.379461 1 265.379461 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5482

Total | 476.517688 52 9.16380169 Root MSE = 2.0347

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr | .2043549 .0255241 8.01 0.000 .1531132 .2555965

_cons | 3.056952 .3120249 9.80 0.000 2.430536 3.683368

------------------------------------------------------------------------------

To validate the results shown above, the estimation results should be checked if it

violates the Classical Linear Regression Model (CLRM) assumptions. If in case it violates

the assumptions, there is a need to correct it which may cause the estimates to change.

The following sections of this chapter will present the tests and needed corrections.

Variance Inflation Factor Test for Multicollinearity

Multicollinearity occurs if there are two or more independent variables in the

multiple regression model that have perfect or exact linear relationship (Gujarati & Porter,

2009). To find out whether there is multicollinearity in the model, the variance inflation

factor test will be used. If the result of the mean vif is 10, it will imply that there is

multicollinearity.

. vif

Variable | VIF 1/VIF

-------------+----------------------

gfcfgr | 1.00 1.000000

-------------+----------------------

Mean VIF | 1.00

The result shows that mean vif is 1. Since this is clearly less than 10, then the

model does not suffer from multicollinearity.

Breusch-Pagan Test for Heteroscedasticity

One of the assumptions of Classical Linear Regression Model (CLRM) is

homoscedasticity which means that the variance of each disturbance term is constant

(Gujarati & Porter, 2009). To find out if the variance is constant or not, the Breusch-Pagan

heteroscedasticity test will be used.

. hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance

Variables: fitted values of gdpgr

chi2(1) = 2.24

Prob > chi2 = 0.1347

The p-value is 0.1347 which is clearly greater than 0.05. Given this, there is no

evidence against the null hypothesis that the variance is constant. The null hypothesis

will be accepted and thus, there is homoscedasticity.

Breusch-Godfrey Test for Autocorrelation

Another assumption under the Classical Linear Regression Model (CLRM) is that

there is no autocorrelation which means that the disturbance term of an observation is

not affected by the disturbance term of another observation (Gujarati & Porter, 2009). To

find out if autocorrelation exists, the Breusch-Godfrey test will be used.

. bgodfrey

Breusch-Godfrey LM test for autocorrelation

---------------------------------------------------------------------------

lags(p) | chi2 df Prob > chi2

-------------+-------------------------------------------------------------

1 | 6.135 1 0.0133

---------------------------------------------------------------------------

H0: no serial correlation

The test generated a p-value which is equal to 0.0133. This implies that the null

hypothesis that there is no serial correlation or autocorrelation is rejected. Therefore,

there exists autocorrelation and there is a need to correct it.

Final Static Model

Prais-Winsten Robust Regression for Corrective Measures

One way to solve autocorrelation is to use Prais-Winsten Robust Regression. The

result of this regression is shown below.

. prais gdpgr gfcfgr, robust

Iteration 0: rho = 0.0000









Prais-Winsten AR(1) regression -- iterated estimates

Linear regression Number of obs = 53

F( 1, 51) = 25.74

Prob > F = 0.0000

R-squared = 0.5080

Root MSE = 1.901

------------------------------------------------------------------------------

| Semirobust


-------------+----------------------------------------------------------------

gfcfgr | .1764056 .0347717 5.07 0.000 .1065986 .2462126

_cons | 3.229319 .4859764 6.65 0.000 2.25368 4.204957

-------------+----------------------------------------------------------------

rho | .3868144

------------------------------------------------------------------------------

Durbin-Watson statistic (original) 1.332861

Durbin-Watson statistic (transformed) 1.918059

Now that the problem of autocorrelation is solved, the results can be used for the

final static model.

Final Static Model

𝑔𝑑𝑝𝑔𝑟 = 3.229319 + 0.1764056(𝑔𝑓𝑐𝑓𝑔𝑟)

The results show that gross fixed capital formation growth is statistically significant

at 1% significance level. In addition, the results show that there is a positive relationship

between gross fixed capital formation growth and GDP growth which means that the

effect of an increase in gross fixed capital formation growth will increase GDP growth by

0.1764056, ceteris paribus. Lastly, the R-squared is 50.80% which shows the goodness

of fit of the model.

DYNAMIC MODELS

General Distributed Lag Model

Initial General Distributed Lag Model

The following are the initial regression of the General Distributed Lag Model and

the CLRM tests.

Initial Regression



-------------+------------------------------ F( 2, 49) = 31.35

Model | 266.272143 2 133.136071 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5434

Total | 474.378076 51 9.30153089 Root MSE = 2.0608

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr |

--. | .1987499 .0270498 7.35 0.000 .1443913 .2531084

L1. | .0222847 .0269705 0.83 0.413 -.0319145 .0764838

|

_cons | 2.975012 .3336207 8.92 0.000 2.304576 3.645448

------------------------------------------------------------------------------

Test for Multicollinearity

. vif


-------------+----------------------

gfcfgr |

L1. | 1.08 0.925014

--. | 1.08 0.925014

-------------+----------------------

Mean VIF | 1.08

Test for Heteroscedasticity

. hettest




chi2(1) = 1.35

Prob > chi2 = 0.2456

Test for Autocorrelation

. bgodfrey


---------------------------------------------------------------------------


-------------+-------------------------------------------------------------

1 | 6.753 1 0.0094

---------------------------------------------------------------------------


It can be seen that the null hypothesis that there is no serial correlation will be

rejected since the p-value that was generated from the Breusch-Godfrey test for

autocorrelation (0.0094) is less than 0.05. Therefore, there is autocorrelation and it should

be corrected.

Prais-Winsten Robust Regression for Corrective Measures

. prais gdpgr gfcfgr l1.gfcfgr, robust










F( 2, 49) = 13.00

Prob > F = 0.0000

R-squared = 0.5148

Root MSE = 1.9202

------------------------------------------------------------------------------

| Semirobust


-------------+----------------------------------------------------------------

gfcfgr |

--. | .1803885 .0353781 5.10 0.000 .1092936 .2514833

L1. | .0209369 .0288574 0.73 0.472 -.0370542 .0789281

|

_cons | 3.122936 .5023444 6.22 0.000 2.113438 4.132435

-------------+----------------------------------------------------------------

rho | .3744517

------------------------------------------------------------------------------



Now that the problem of autocorrelation is corrected, the results can now be used

for the final General Distributed Lag Model.

Final General Distributed Lag Model

After the corrective measure using the Prais-Winsten Robust Regression, the final

General Distributed Lag Model is

𝑔𝑑𝑝𝑔𝑟𝑡 = 3.122936 + 0.1803885(𝑔𝑓𝑐𝑓𝑔𝑟𝑡) + 0.0209369(𝑔𝑓𝑐𝑓𝑔𝑟𝑡−1)

The final General Distributed Lag Model has an optimal lag of 1 as shown in the

Hendry top-down approach and the Alt and Tinbergen approach. The results show that

as the current gross fixed capital growth increases by 1%, current GDP growth also

increases by 0.1803885%, ceteris paribus. In addition, although the past gross fixed

capital formation growth is insignificant, its coefficient is in accordance with the a-priori

expectation. Thus, as the past gross fixed capital formation growth increases by 1%, the

current GDP growth also increases by 0.0209369%. Furthermore, its R-squared is

51.48% which means that the goodness of fit of the final General Distributed Lag Model

is better than the static model.

Koyck Infinite Distributed Lag Model

The Koyck Model has an assumption that there is a “main beta” called the impact

multiplier that decreases geometrically with the lambda lag as time passes by. In simple

words, it assumes that the policy variable from the past has an effect to the current value

of the dependent variable. According to Rufino (2008), this model has a good reputation

because of the idea that the effect of the policy variable is greatest at the year that it is

implemented while it continuously declines as years go by. The effect of the policy

variable expands infinitely which makes it an infinite distributed lag model. Furthermore,

the model is an autoregressive model because the lagged dependent variable is on the

right-hand side of the equation.

There are two ways to estimate the model. One is by using Ordinary Least Squares

estimation. The other is by using Two-Stage Least Squares estimation. The Koyck model

that will use OLS estimation will be written as:

𝑔𝑑𝑝𝑔𝑟𝑡 = 𝛼(1 − 𝜆) + 𝛽0(𝑔𝑓𝑐𝑓𝑔𝑟𝑡) + 𝜆(𝑔𝑓𝑐𝑓𝑔𝑟𝑡) + (𝑢𝑡 − 𝜆𝑢𝑡−1)

OLS Estimation

. regress gdpgr gfcfgr l1.gdpgr


-------------+------------------------------ F( 2, 49) = 37.23

Model | 286.098169 2 143.049084 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5869

Total | 474.378076 51 9.30153089 Root MSE = 1.9602

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr | .1802705 .0267331 6.74 0.000 .1265483 .2339928

|

gdpgr |

L1. | .238248 .0979662 2.43 0.019 .0413776 .4351184

|

_cons | 2.211521 .4614323 4.79 0.000 1.284238 3.138804

------------------------------------------------------------------------------


. vif


-------------+----------------------

gdpgr |

L1. | 1.17 0.856830

gfcfgr | 1.17 0.856830

-------------+----------------------

Mean VIF | 1.17


. hettest




chi2(1) = 0.89

Prob > chi2 = 0.3456


. bgodfrey


---------------------------------------------------------------------------


-------------+-------------------------------------------------------------

1 | 2.503 1 0.1136

---------------------------------------------------------------------------


Final Koyck Infinite Distributed Lag Model Using OLS 𝑔𝑑𝑝𝑔𝑟𝑡 = 2.11521 + 0.1802705(𝑔𝑓𝑐𝑓𝑔𝑟𝑡) + 0.238248(𝑔𝑑𝑝𝑔𝑟𝑡−1)

The initial regression did not violate multicollinearity, homoscedasticity and non-

autocorrelation assumptions which is why the results of the OLS initial estimation will be

used for the final Koyck Infinite Distributed Lag Model. The coefficients of the Koyck model

are all significant and in accordance with the a-priori expectations. The impact multiplier

is .1802705. On the other hand, the rate of decay is .238248. Therefore, a percent

increase in gross fixed capital formation growth will lead to a 0.1802705% increase in

current GDP growth. In addition, a percent increase in the past year’s GDP growth will

lead to a 0.238248% increase in the current GDP growth. Lastly, the goodness of fit of

the model is 60.31%

It must be noted that the results cannot be used for the conclusion because the

lagged GDP growth is endogenous which violates the exogeneity assumption of CLRM.

Thus, these results are biased and inconsistent.

For Koyck models, there are other values that can be interpreted. These are mean

lag, median lag, alpha and the long run multiplier. The computations for these are shown

below.

Alpha = 𝛼(1 − 𝜆)

= 𝛼(1 −.238248) = 2.211521

= 𝛼 = 2.9032034

Mean lag = 𝜆

1−𝜆

= 0.238348

1−0.238248= 0.3127632

Median lag = −𝑙𝑜𝑔2

𝑙𝑜𝑔𝜆=

𝑙𝑜𝑔2

log(0.238248)= 0.4832169

Long-run multiplier = 𝛽 =𝛽0

1−𝜆=

.0267331

1−.0.238248= 0.03509423

The OLS estimates will be used for the Distributed Lag Model written below which

can be interpreted as the effect of gross fixed capital formation growth increases GDP

growth by 0.03409423 of the increase gross fixed capital formation.

𝑔𝑑𝑝𝑔𝑟𝑡 = 𝛼 + 𝛽𝑔𝑓𝑐𝑓𝑔𝑟𝑡 + 𝑢𝑡

𝑔𝑑𝑝𝑔𝑟𝑡 = 2.9032034 + 0.03409423𝑔𝑓𝑐𝑓𝑔𝑟𝑡

Two-Stage Least Squares (2SLS) Estimation

As previously seen from the OLS estimation, there is high R-squared that suggests

the goodness of fit of the model. Furthermore, the OLS estimation shows that the current

gross fixed capital formation growth and the lagged GDP growth are significant and

intuitive. However, because of the possibility of endogeneity in the right-hand side of the

equation due to the inclusion of the lagged GDP growth as an independent variable, then

it means that the assumption of exogeneity in CLRM must have been violated which will

lead to spurious results since the estimates will become biased and inconsistent.

Therefore, this issue should be addressed. In order to address the problem of

endogeneity, Two-Stage Least Squares (2SLS) estimation will be used by using an

instrumental variable to replace the endogenous lagged dependent variable in the right-

hand side of the equation.

Shown below are the initial 2SLS regression and the test for autocorrelation.

. ivreg gdpgr gfcfgr (l1.gdpgr=gfcfgr l1.gfcfgr), first

First-stage regressions

-----------------------


-------------+------------------------------ F( 2, 49) = 34.87

Model | 274.431837 2 137.215919 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5705

Total | 467.262369 51 9.16200723 Root MSE = 1.9838

------------------------------------------------------------------------------

L.gdpgr | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

gfcfgr |

--. | .051473 .0260381 1.98 0.054 -.0008525 .1037985

L1. | .1885336 .0259617 7.26 0.000 .1363615 .2407057

|

_cons | 2.837465 .3211432 8.84 0.000 2.192104 3.482826

------------------------------------------------------------------------------

Instrumental variables (2SLS) regression


-------------+------------------------------ F( 2, 49) = 33.62

Model | 280.328307 2 140.164153 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5742

Total | 474.378076 51 9.30153089 Root MSE = 1.99

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gdpgr |

L1. | .1182 .1381382 0.86 0.396 -.1593991 .3957991

|

gfcfgr | .1926658 .0288885 6.67 0.000 .1346122 .2507193

_cons | 2.639624 .5799338 4.55 0.000 1.474203 3.805044

------------------------------------------------------------------------------

Instrumented: L.gdpgr

Instruments: gfcfgr L.gfcfgr

------------------------------------------------------------------------------


. dwstat

Durbin-Watson d-statistic( 3, 52) = 1.447762

The d-statistic is equal to 1.447762. Because it is closer to 2 than 0, then there is

no autocorrelation. Therefore, the initial 2SLS regression results can be used for the final

Koyck Infinite Distributed Lag Model.

Final Koyck Infinite Distributed Lag Model Using 2SLS

𝑔𝑑𝑝𝑔𝑟𝑡 = 2.639624 + 0.1926658(𝑔𝑓𝑐𝑓𝑔𝑟𝑡) + 0.1182(𝑔𝑑𝑝𝑔𝑟𝑡−1)

The final Koyck Infinite Distributed Lag Model that was estimated

using 2SLS now has the best linear unbiased estimates (BLUE)

because it does not violate the assumptions CLRM. Given the regression

results, the impact multiplier is 0.1926658 while the rate of decay is 0.1182.

It can also be seen that the lagged GDP growth is insignificant, its coefficient

is consistent with the a-priori expectation that it positively affects the current

GDP growth. Therefore, a percent increase in gross fixed capital formation

growth will increase the current GDP growth by 0.1926658% while a percent

increase in the lagged GDP growth will increase the current GDP growth by

0.1182%, ceteris paribus.

Alpha = 𝛼(1 − 𝜆)

= 𝛼(1 −0.1182) = 2.639624

= 𝛼 = 2.99344976

Mean lag = 𝜆

1−𝜆

= 0.1182

1−0.1182= 0.134044

Median lag = −𝑙𝑜𝑔2

𝑙𝑜𝑔𝜆=

𝑙𝑜𝑔2

log(0.1182)= 0.3246018

Long-run multiplier = 𝛽 =𝛽0

1−𝜆=

0.1926658

1−0.1182= 0.21849149

The long-run equation of the Koyck Distributed Lag Model that was estimated

using 2SLS is shown below. It can be said that effect of the increase in gross fixed

capital formation growth increases GDP growth by 0.21849149 of the increase in

gross fixed capital formation. The long-run equations are not evaluated for the

standard errors and significance.

𝑔𝑑𝑝𝑔𝑟𝑡 = 𝛼 + 𝛽𝑔𝑓𝑐𝑓𝑔𝑟𝑡 + 𝑢𝑡

𝑔𝑑𝑝𝑔𝑟𝑡 = 2.99344976 + 0.21849149𝑔𝑓𝑐𝑓𝑔𝑟𝑡

Almon Polynomial Distributed Lag (PDL) Model

The Koyck model has coefficients the geometrically decreases as the number of

lags increase which makes it restrictive. Given this and that the restrictions are valid, the

estimates become more efficient but biased and inconsistent if the restrictions are invalid.

To avoid this issue, Almon Polynomial Distributed Lag Model may be used. This model

assumes that the optimal number of lags and the polynomial degree are arbitrarily chosen

(Rufino, 2008).

In this model, the lagged values of the independent variable are located on the

right-hand side of the equation. Given that the Hendry top-down approach showed that

the optimal lag should be 1, then it will be used.

Initial Regression



-------------+------------------------------ F( 2, 49) = 31.35

Model | 266.272143 2 133.136071 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5434

Total | 474.378076 51 9.30153089 Root MSE = 2.0608

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr |

--. | .1987499 .0270498 7.35 0.000 .1443913 .2531084

L1. | .0222847 .0269705 0.83 0.413 -.0319145 .0764838

|

_cons | 2.975012 .3336207 8.92 0.000 2.304576 3.645448

------------------------------------------------------------------------------

Test for Multicollinearity . vif


-------------+----------------------

gfcfgr |

L1. | 1.08 0.925014

--. | 1.08 0.925014

-------------+----------------------

Mean VIF | 1.08


. hettest




chi2(1) = 1.35

Prob > chi2 = 0.2456

Test for Autocorrelation . bgodfrey


---------------------------------------------------------------------------


-------------+-------------------------------------------------------------

1 | 6.753 1 0.0094

---------------------------------------------------------------------------


Prais-Winsten Robust Regression for Corrective Measures . prais gdpgr gfcfgr l1.gfcfgr, robust










F( 2, 49) = 13.00

Prob > F = 0.0000

R-squared = 0.5148

Root MSE = 1.9202

------------------------------------------------------------------------------

| Semirobust


-------------+----------------------------------------------------------------

gfcfgr |

--. | .1803885 .0353781 5.10 0.000 .1092936 .2514833

L1. | .0209369 .0288574 0.73 0.472 -.0370542 .0789281

|

_cons | 3.122936 .5023444 6.22 0.000 2.113438 4.132435

-------------+----------------------------------------------------------------

rho | .3744517

------------------------------------------------------------------------------



Now that the model has undergone the necessary corrective measure, the results

from the Prais-Winsten Robust Regression can now be used for the final Almon

Polynomial Distributed Lag Model.

Final Almon Polynomial Distributed Lag Model 𝑔𝑑𝑝𝑔𝑟𝑡 = 3.122936 + 0.1803885(𝑔𝑓𝑐𝑓𝑔𝑟)𝑡 + 0.0209369(𝑔𝑓𝑐𝑓𝑔𝑟)𝑡−1

It can be seen that the current and the lagged gross fixed capital formation growth

positively affects the current GDP growth. Although the p-value of the lagged gross fixed

capital formation growth is 0.472 is greater than 0.05 which makes the variable

insignificant, its coefficient is consistent with the a-priori expectation. Thus, a percent

increase in the current gross fixed capital formation and lagged gross fixed capital

formation growth will increase the current GDP growth by 0.1803885% and 0.0209369%,

respectively. In addition, it has an R-squared of 51.48% which represents the goodness

of fit of the model.

Autoregressive Distributed Lag Model (ARDL)

After determining the results of AR and DL models, what will be determined now

is the combination of the two, called the ARDL model. In this model, the dependent and

independent variables are lagged. Because the Hendry top-down approach and Alt and

Tinbergen estimation resulted to having 1 as the optimal number of lag, then it will be

employed in this model.

Initial Regression

. regress gdpgr gfcfgr l1.gfcfgr l1.gdpgr


-------------+------------------------------ F( 3, 48) = 25.69

Model | 292.307989 3 97.4359962 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.5922

Total | 474.378076 51 9.30153089 Root MSE = 1.9476

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

gfcfgr |

--. | .1798361 .0265632 6.77 0.000 .1264273 .233245

L1. | -.0469919 .0367268 -1.28 0.207 -.120836 .0268522

|

gdpgr |

L1. | .3674497 .1402526 2.62 0.012 .0854531 .6494464

|

_cons | 1.932386 .5077205 3.81 0.000 .9115458 2.953227

------------------------------------------------------------------------------


. vif


-------------+----------------------

gdpgr |

L1. | 2.42 0.412681

gfcfgr |

L1. | 2.24 0.445521

--. | 1.17 0.856690

-------------+----------------------

Mean VIF | 1.95


. hettest




chi2(1) = 0.46

Prob > chi2 = 0.4972


. bgodfrey


---------------------------------------------------------------------------


-------------+-------------------------------------------------------------

1 | 1.084 1 0.2979

---------------------------------------------------------------------------


Final Autoregressive Distributed Lag Model

Because the initial regression did not violate the tests for the CLRM assumptions,

then its results can be used for the final Autoregressive Distributed Lag Model. Thus, the

final model is:

𝑔𝑑𝑝𝑔𝑟𝑡 = 1.932386 + 0.1798361(𝑔𝑓𝑐𝑓𝑔𝑟)𝑡 − 0.0469919(𝑔𝑓𝑐𝑓𝑔𝑟)𝑡−1

+ 0.3674497(𝑔𝑑𝑝𝑔𝑟)𝑡−1

The results show that the current gross fixed capital formation growth and the

lagged GDP growth have significant relationship to the current GDP growth. However,

this is not the case for the lagged gross fixed capital formation growth. To interpret the

results, a percent increase in the current gross fixed capital formation growth will increase

GDP growth by 0.1798361% and a percent increase in the lagged GDP growth will

increase the current GDP by 0.3674497%. In addition, the model has an R-square which

is equal to 61.62% which represents the goodness of fit.

CONCLUSIONS AND RECOMMENDATIONS

The significance of this paper was shown through determining the effect of gross

fixed capital formation growth on GDP growth. After using various dynamic models such

General Distributed Lag Model, Koyck Infinite Distributed Lag Model estimated through

OLS and 2SLS and Autoregressive Distributed Lag Model, the results showed that the

current value of the gross fixed capital formation growth really has a positive

relationship with GDP growth. However, the result regarding the effect of the lagged

gross fixed capital formation growth is ambiguous because it is only significant in the

Almon Polynomial Distributed Lag Model. In addition, the lagged GDP growth also

poses ambiguous results because it is only significant at the Autoregressive Distributed

Lag Model and the Koyck Infinite Distributed Lag Model estimated using OLS.

The result that gross fixed capital formation growth positively affects GDP growth

but does nog Granger-cause GDP growth is consistent with most empirical literature.

One of which is the study of Blomstrom, Zejan and Lipsey (1993) that determined

causality through using five-year periods after the World War II. In the said study, it was

concluded that “fixed capital formation is not the key to economic growth.”

For future researches regarding this topic, it is suggested that the number of

observations may be increased since it may cause changes in the methodology since

unit roots and lags may change. Another recommendation is the use of another

functional form which may yield more meaning results.

BIBLIOGRAPHY

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http://ijmtpublication.com/files/IJMT_volume%2019_2_15.pdf

Pettinger, T. (n.d.). Fixed capital formation. Retrieved on April 24, 2015, from

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Robertson, P. (2010). Investment led growth in India: Hindu fact or mythology?

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