MICRO LEVEL FORECASTS FOR INDIA’S EXPORT SECTOR SPECIFIC COUNTRIES AND SPECFIC COMMODITIES

MICRO LEVEL FORECASTS FOR INDIA’S EXPORT SECTOR

SPECIFIC COUNTRIES AND SPECFIC COMMODITIES

Analytics & Modelling Division

NATIONAL INFORMATICS CENTRE

Department of Information Technology

Ministry of Communication & Information Technology

New Delhi-110003

Major input to “India’s export model” for a financial year

•Input to an econometric model to derive macro-level forecasts for strategic planning for India’s export – RIS Study

• NIC has developed micro-level forecasts for a financial year for specific country and specific commodities (Total variables: 319)

• Monthly time series behavior is captured through Neural network methodology.

• Final model selected has been simulated with-in and outside sample and once stabilized with regard to error statistics forecasts are generated .

• 4Thought/Freefore is the state-of-the-art software tool from COGNOS which has been used to simulate and generate micro-level forecast India’s export for a financial year.

• The reliability of the forecasts and the degree of confidence are part of the final model

Tools and technologies used :

Table A: SUMMARY OF COUNTRY WISE DATA-SETS(Time Series Forecasting Carried for the listed number of

data sets)

Country List Com- Codes

Var. for

each Code

Total Vbls

Exports Imports UVI ROW

Canada 13 4 52 +2(rest) +1(all)

=55

Apr 1996 to June 2003

Jan 1995 to Nov 2003


Jan 95 to Nov 2003

USA 17 4 68 Apr 1996 to May 2003

Jan 1993 to Oct 2003

Jan 1993 to Oct 2003

Jan93 to Oct 2003

China 10 4 40 Apr 1996 to May 2003




Japan 11 4 44 Apr 1996 to June 2003

Jan 1994 to Nov. 2003

Jan 1994 to Nov. 2003


Table A: Contd.


Var. for

each Code

Total Vbls


Malaysia * 1 1 1 Apr 1996 to Aug 2002

NA NA NA

Singapore* 1 1 1 Apr 1996 to Aug 2002

NA NA NA

Thailand* 1 1 1 Apr 1996 to Aug 2002

NA NA NA

Hong Kong* 1 1 1 Apr 1996 to Aug 2002

NA NA NA

Rest of World*

1 1 1 Apr 1996 to Aug 2002

NA NA NA

* Only single variable total export of “All Commodities” from India is considered


Var. for

each Code

Total Vbls


European Union

26 4 104

+2 (rest)

+1(all)

=107


Jan 1996 to June 2003



TOTAL 319

No. of Obs

(Range)

77-92 90-130 90-130 90-130

Period Range





Table A: Contd.

Includes both the series- monthly as well as annual - with 26 items in each series.

Univariate ARIMA MODEL

1. In regression analysis, if the error terms are not independent i.e. autocorrelated, the efficiency of the ordinary least-square (OLS) parameter estimates gets adversely affected and the standard error estimates are biased.

2. Auto Regressive Integrated Moving Average (ARIMA) model is fit for data with autocorrelated errors. This happens frequently with time series data.

3. The ARIMA procedure analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data using the autoregressive moving-average or the more general autoregressive integrated moving-average (ARIMA) model.

4. An ARIMA model predicts a value in a response time series as a linear combination of its own past values, past errors, and current and past values of other time series.

An ARIMA model contains three different kinds of parameters:

† the p AR-parameters;

† the q MA-parameters;

† and the variance of the error term.

This amount to a total of p + q + 1 parameters to be estimated. These parameters are always estimated on using the stationary time series (a time series which is stationary with respect to it’s variance and mean).

Univariate ARIMA MODEL – Contd.

NEURAL NETWORK

Neural networks cannot do anything that cannot be done using traditional computing techniques, BUT they can do some things which would otherwise be very difficult (time consuming).

Neural networks form a model from training data (or possibly input data) alone.

This is particularly useful when time series behavior is complex, and forecasts for a period is input for the next period forecast.

In a time series, behavior is complex, follows an unknown pattern, has large number of variables, Neural networks learns from the past behavior to develop corresponding complex algorithm and then predicts. (ARIMA: Univariate, Multivariate)

NEURAL NETWORK

-Neural networks are a form of multiprocessor computer system, with

simple processing elements

a high degree of interconnection

simple scalar messages

adaptive interaction between elements

A biological neuron may have as many as 10,000 different inputs, and may send its output (the presence or absence of a short-duration spike) to many other neurons.

Neurons are wired up in a 3-dimensional pattern.

Example

A simple single unit adaptive network: The network has 2 inputs I0 and I1, and one output. All are binary.

If W0 *I0 + W1 * I1 + Wb > 0, then Output is 1

If W0 *I0 + W1 * I1 + Wb <= 0, then Output is 0

We want it to learn simple : output is 1 if either I0 or I1 is 1.

The network adapts as follows: change the weight by an amount proportional to the difference between the desired output and the actual output. As an equation:

Δ Wi = η * (D-Y).Ii

where η is the learning rate, D is the desired output, and Y is the actual output.

Feed Forward Neural Network

1. EU

050

100150200250

Model 1 of Column_1 forecast

Jan

ua

ry 19

96

Jan

ua

ry 19

97

Jan

ua

ry 19

98

Jan

ua

ry 19

99

Jan

ua

ry 20

00

Jan

ua

ry 20

01

Jan

ua

ry 20

02

Jan

ua

ry 20

03

Jan

ua

ry 20

04

Jan

ua

ry 20

05

Row

ActualModelForecast

Model Statistics

Model fit: 75.5004Test fit: 78.4198Overall fit: 76.4137Adjusted fit: 65.3762Iterations:69RMS error: 16.0265Standard deviation: 16.116395% confidence interval: 32.2326Mean absolute error: 12.5406Mean absolute error (%): 8.7764F-Statistic: 20.7884Durbin-Watson Statistic: 1.0007

A. 30613 (Import of Shrimps and prawns frozen )

STATISTICAL MEASURES

Model fit

A measure of how well the model fits to the original data used in modeling. 100% represents a perfect fit. The model fit would approach 0% if you guessed the average value for the target. If the value is negative, the fit is worse than if you had guessed the average value for the target (that is, you had a naive model). The model fit is based on an adaptation of the standard R^2 statistic (that is, the proportion of the relationship explained between two variables).

Adjusted fit

The overall fit adjusted for the number of factors, and the number of rows of data contained in the model. This assumes that a more complex model or less data will produce a less predictive model.

Test fit

The percentage of variation in the test set explained by the model. Test fit (or percent test fit) is a measure of how well the model predicts the test data, and is the best measure of the genuine predictive performance of the model.

The test fit is an adaptation of the standard R^2 statistic. Unlike the model fit, the test fit can be negative. This happens if the current model yields a less accurate prediction of the test set than the naive model.

Overall fit

An indicator of the model quality, and is a combination of the model fit and the test fit. The overall fit is the percentage of the variation explained in the dependent variable.

B. 90111 (Export of Coffee neither roasted nor decaffeinated

010203040


Ap

ril 19

96

Ap

ril 19

97

Ap

ril 19

98

Ap

ril 19

99

Ap

ril 20

00

Ap

ril 20

01

Ap

ril 20

02

Ap

ril 20

03

Ap

ril 20

04

Row

ActualModelForecast

Model Statistics

Model fit: 75.6046Test fit: 73.7038Overall fit: 75.2571Adjusted fit: 64.0117Iterations: 54RMS error: 4.4336Standard deviation: 4.459395% confidence interval: 8.9186Mean absolute error:3.1465Mean absolute error (%): 34.767F-Statistic: 18.7563Durbin-Watson Statistic: 0.5446

0204060


Ja

nu

ary

19

96

Ja

nu

ary

19

97

Ja

nu

ary

19

98

Ja

nu

ary

19

99

Ja

nu

ary

20

00

Ja

nu

ary

20

01

Ja

nu

ary

20

02

Ja

nu

ary

20

03

Ja

nu

ary

20

04

Ja

nu

ary

20

05

Row

ActualModelForecast

C. 251611 (Import of Granite,crude/rough )

Model Statistics

Model fit: 67.3539Test fit: 61.8533Overall fit: 66.0773Adjusted fit: 56.5328Iterations: 66RMS error: 3.4094Standard deviation: 3.428595% confidence interval: 6.857Mean absolute error:2.7858Mean absolute error (%): 6.6183F-Statistic: 12.4989Durbin-Watson Statistic: 2.122

2. CHINAA. 670300 (Import of Human Hair, dressed, thinned, bleached or otherwise worked; wool or other animal hair or other textile materials, prepared for use in making wigs or the like )

05

101520


Ja

nu

ary

19

95

Ja

nu

ary

19

96

Ja

nu

ary

19

97

Ja

nu

ary

19

98

Ja

nu

ary

19

99

Ja

nu

ary

20

00

Ja

nu

ary

20

01

Ja

nu

ary

20

02

Ja

nu

ary

20

03

Ja

nu

ary

20

04

Ja

nu

ary

20

05Row

ActualModelForecast

Model StatisticsModel fit: 85.0775Test fit: 84.3229Overall fit: 84.9804Adjusted fit: 74.6557Iterations: 30RMS error: 1.0522Standard deviation: 1.057195% confidence interval: 2.1143Mean absolute error: 0.7224Mean absolute error (%): 24.07F-Statistic: 44.3208Durbin-Watson Statistic: 1.2491

B. CHINA (Import of rest of the codes)

010,00020,00030,00040,00050,000


Ja

nu

ary

19

95

Ja

nu

ary

19

96

Ja

nu

ary

19

97

Ja

nu

ary

19

98

Ja

nu

ary

19

99

Ja

nu

ary

20

00

Ja

nu

ary

20

01

Ja

nu

ary

20

02

Ja

nu

ary

20

03

Ja

nu

ary

20

04

Ja

nu

ary

20

05

Row

ActualModelForecast


C. CHINA (Unit value index for rest of the codes)

050

100150


Ja

nu

ary

19

95

Ja

nu

ary

19

96

Ja

nu

ary

19

97

Ja

nu

ary

19

98

Ja

nu

ary

19

99

Ja

nu

ary

20

00

Ja

nu

ary

20

01

Ja

nu

ary

20

02

Ja

nu

ary

20

03

Ja

nu

ary

20

04

Ja

nu

ary

20

05Row

ActualModelForecast


3. USA

MODEL STATISTICS IN TERMS OF THE ORIGINAL DATANumber of Residuals (R) =n 70Number of Degrees of Freedom =n-m 62Residual Mean =Sum R / n .683103E-02Sum of Squares =Sum R**2 121.321Variance var=SOS/(n) 1.73316Adjusted Variance =SOS/(n-m) 1.95679Standard Deviation =SQRT(Adj Var) 1.39885Standard Error of the Mean =Standard Dev/ .177655Mean / its Standard Error =Mean/SEM .384512E-01Mean Absolute Deviation =Sum(ABS(R))/n.992518AIC Value ( Uses var ) =nln +2m 54.4962SBC Value ( Uses var ) =nln +m*lnn 72.4841BIC Value ( Uses var ) =see Wei p153 -95.0882R Square = .887551Durbin-Watson Statistic =[A-A(T-1)]**2/A**2 1.95492D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1

A. 420310 (Import of Articles of apparel )

MODEL STATISTICS IN TERMS OF THE ORIGINAL DATANumber of Residuals (R) =n 103Number of Degrees of Freedom =n-m 97Residual Mean =Sum R / n -.783408E-14Sum of Squares =Sum R**2 1578.37Variance var=SOS/(n) 15.3239Adjusted Variance =SOS/(n-m) 16.2718Standard Deviation =SQRT(Adj Var) 4.03383Standard Error of the Mean =Standard Dev/ .409574Mean / its Standard Error =Mean/SEM -.191274E-13Mean Absolute Deviation =Sum(ABS(R))/n3.10562AIC Value ( Uses var ) =nln +2m 293.130SBC Value ( Uses var ) =nln +m*lnn 308.938BIC Value ( Uses var ) =see Wei p153 -26.2750R Square = .858561Durbin-Watson Statistic =[A-A(T-1)]**2/A**2 1.88808D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.

B. 570110 ( Import of Carpets and other textile coverings of wool or fine animal hair

MODEL STATISTICS IN TERMS OF THE ORIGINAL DATA Number of Residuals (R) =n 105 Number of Degrees of Freedom =n-m 99Residual Mean =Sum R / n -.708456E-01 Sum of Squares =Sum R**2 10575.5 Variance var=SOS/(n) 100.719 Adjusted Variance =SOS/(n-m) 106.824 Standard Deviation =SQRT(Adj Var) 10.3355 Standard Error of the Mean =Standard Dev/ 1.03876 Mean / its Standard Error =Mean/SEM -.682020E-01 Mean Absolute Deviation =Sum(ABS(R))/n7.73821 AIC Value ( Uses var ) =nln +2m 496.295 SBC Value ( Uses var ) =nln +m*lnn 512.219 BIC Value ( Uses var ) =see Wei p153 165.540 R Square = .848765 Durbin-Watson Statistic =[A-A(T-1)]**2/A**2 2.04567 D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.

C. 610510 (Import of Men's or boys' shirts of cotton, knitted or crocheted )

Conclusion :

Time Constraint :• No. of independent variable for which forecast are to be generated is approximately 319.

•As the time series data keep coming over time and forecasts are to be generated based on the latest monthly time series data within a period of approximately 2 weeks forecasts are to be generated for 319 independent variables.

•Each variable forecast is an independent exercise.

•Existing software tools arte not fully automated and the subject and tool specialist intervention is a must.

•Traditional Statistical/Econometric model techniques/software tools are major constraint in terms of automation.

What is Required :

NIC can develop fully automated forecasting system by developing algorithms and testing with state-of-the-art tools available with limited interface.

The state of the art software tool and techniques will require funding. Manpower and resource mobilization to the tune of Rs. 10 lakhs and for a period of 8 months.

Documents

MICRO LEVEL FORECASTS FOR INDIA’S EXPORT SECTOR SPECIFIC COUNTRIES AND SPECFIC COMMODITIES