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MICRO LEVEL FORECASTS FOR INDIAS 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


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Major input to Indias export model

for a financial year

Input to an econometric model to derive macro-level

forecasts for strategic planning for Indias export RIS

Study

NIC has developed micro-level forecasts for a financial

year for specific country and specific commodities (Totalvariables: 319)


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Monthly time series behavior is captured through Neural networkmethodology.

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 Indias 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 :


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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

VblsExports Imports UVI ROW

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

=55

Apr 1996to June

2003

Jan 1995to Nov

2003

Jan 1995to Nov

2003

Jan 95 toNov 2003

USA 17 4 68 Apr 1996to May

2003

Jan 1993

to Oct

2003

Jan 1993

to Oct

2003

Jan93 to

Oct 2003

China 10 4 40 Apr 1996to May

2003

Jan 1995

to Nov

2003

Jan 1995

to Nov

2003

Jan 1995

to Nov

2003

Japan 11 4 44 Apr 1996to June

2003

Jan 1994

to Nov.

2003

Jan 1994

to Nov.

2003

Jan 1994

to Nov

2003


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Table A: Contd.


Var.

for

each

Code

Total


Malaysia * 1 1 1 Apr 1996 toAug 2002

NA NA NA

Singapore* 1 1 1 Apr 1996 to

Aug 2002

NA NA NA

Thailand* 1 1 1 Apr 1996 toAug 2002

NA NA NA

Hong Kong* 1 1 1 Apr 1996 toAug 2002

NA NA NA

Rest ofWorld*

1 1 1 Apr 1996 to

Aug 2002

NA NA NA

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

considered


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Var.

for

each

Code

Total


European

Union

26 4 104

+2 (rest)

+1(all)

=107

Apr 1996

to June

2003

Jan 1996

to June

2003

Jan 1996

to June

2003

Jan 1996

to June

2003

TOTAL 319

No. of Obs

(Range)

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

PeriodRange

Apr 1996to June

2003

Jan 1993to Nov

2003

Jan 1993to Nov

2003

Jan 1993to Nov

2003

Table A: Contd.

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

series.


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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.


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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 its

variance and mean).

Univariate ARIMA MODELContd.


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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)


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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.


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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


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Feed Forward Neural Network


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1. EU

Model Statistics

Model fit: 75.5004Test fit: 78.4198

Overall 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: 20.7884

Durbin-Watson Statistic: 1.0007

A. 30613 (Import of Shrimps and prawns frozen )


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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 lessdata will produce a less predictive model.


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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 thedependent variable.


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B. 90111 (Export of Coffee neither roasted nor decaffeinated

Model Statistics

Model fit: 75.6046Test fit: 73.7038Overall fit: 75.2571

Adjusted fit: 64.0117Iterations: 54RMS error: 4.4336Standard deviation: 4.459395% confidence interval: 8.9186Mean absolute error: 3.1465Mean absolute error (%): 34.767F-Statistic: 18.7563



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

Model Statistics

Model fit: 67.3539Test fit: 61.8533Overall fit: 66.0773




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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 )

Model StatisticsModel fit: 85.0775Test fit: 84.3229Overall fit: 84.9804




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B. CHINA (Import of rest of the codes)

Model StatisticsModel fit: 87.8544Test fit: 82.4129Overall fit: 87.1099Adjusted fit: 76.5264

Iterations: 126RMS error: 2828.6593Standard deviation: 2841.970795% confidence interval: 5683.9414Mean absolute error: 2114.0386Mean absolute error (%): 12.5192F-Statistic: 52.9366



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

Model StatisticsModel fit: 61.607Test fit: 76.4597Overall fit: 66.02Adjusted fit: 57.6874

Iterations: 46RMS error: 6.1855Standard deviation: 6.215795% confidence interval: 12.4314Mean absolute error: 4.2899Mean absolute error (%): 4.5121F-Statistic: 14.5718


3 USA


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3. USA

MODEL STATISTICS IN TERMS OF THE ORIGINAL DATA

Number of Residuals (R) =n 70

Number of Degrees of Freedom =n-m 62

Residual Mean =Sum R / n .683103E-02

Sum of Squares =Sum R**2 121.321

Variance var=SOS/(n) 1.73316

Adjusted Variance =SOS/(n-m) 1.95679

Standard Deviation =SQRT(Adj Var) 1.39885

Standard Error of the Mean =Standard Dev/ .177655

Mean / its Standard Error =Mean/SEM .384512E-01

Mean Absolute Deviation =Sum(ABS(R))/n .992518

AIC Value ( Uses var ) =nln +2m 54.4962

SBC Value ( Uses var ) =nln +m*lnn 72.4841

BIC Value ( Uses var ) =see Wei p153 -95.0882

R Square = .887551

Durbin-Watson Statistic =[A-A(T-1)]**2/A**2 1.95492

D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1

A. 420310 (Import of Articles of apparel )


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Residual Mean =Sum R / n -.783408E-14





Standard Error of the Mean =Standard Dev/ .409574

Mean / its Standard Error =Mean/SEM -.191274E-13

Mean Absolute Deviation =Sum(ABS(R))/n 3.10562



BIC Value ( Uses var ) =see Wei p153 -26.2750

R Square = .858561


D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.

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


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Residual Mean =Sum R / n -.708456E-01





Standard Error of the Mean =Standard Dev/ 1.03876

Mean / its Standard Error =Mean/SEM -.682020E-01

Mean Absolute Deviation =Sum(ABS(R))/n 7.73821



BIC Value ( Uses var ) =see Wei p153 165.540

R Square = .848765


D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.

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


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Conclusion :

Time Constraint :

No. of independent variable for which forecast are to be generated isapproximately 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 independentvariables.

Each variable forecast is an independent exercise.

Existing software tools arte not fully automated and the subject and toolspecialist intervention is a must.

Traditional Statistical/Econometric model techniques/software tools are

major constraint in terms of automation.


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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.

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