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MICRO LEVEL FORECASTS FOR INDIA

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Title: PowerPoint Presentation Author: nic Last modified by: nic Created Date: 7/2/2004 4:19:52 AM Document presentation format: On-screen Show Company – PowerPoint PPT presentation

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Title: MICRO LEVEL FORECASTS FOR INDIA


1
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
2
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 (Total variables 319)

3
Tools and technologies used
  • 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
    Indias export for a financial year.
  • The reliability of the forecasts and the degree
    of confidence are
  • part of the final model

4
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 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 Jan 1995 to Nov 2003 Jan 1995 to Nov 2003 Jan 1995 to Nov 2003
Japan 11 4 44 Apr 1996 to June 2003 Jan 1994 to Nov. 2003 Jan 1994 to Nov. 2003 Jan 1994 to Nov 2003
5
Table A Contd.
Country List Com- Codes Var. for each Code Total Vbls Exports Imports UVI ROW
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
6
Table A Contd.
Country List Com- Codes Var. for each Code Total Vbls Exports Imports UVI ROW
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
Period Range Apr 1996 to June 2003 Jan 1993 to Nov 2003 Jan 1993 to Nov 2003 Jan 1993 to Nov 2003
? Includes both the series- monthly as well as
annual - with 26 items in each series.
7
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.

8
Univariate ARIMA MODEL Contd.
  • 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).

9
  • 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)

10
  • 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.

11
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 gt 0, then Output is 1 If W0 I0 W1
I1 Wb lt 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 ? Wi ?
(D-Y).Ii where ? is the learning rate, D is the
desired output, and Y is the actual output.
12
Feed Forward Neural Network
13
1. EU
A. 30613 (Import of Shrimps and prawns frozen )
Model Statistics   Model fit 75.5004 Test
fit 78.4198 Overall fit 76.4137 Adjusted
fit 65.3762 Iterations 69 RMS
error 16.0265 Standard deviation 16.1163 95
confidence interval 32.2326 Mean absolute
error 12.5406 Mean absolute error
() 8.7764 F-Statistic 20.7884 Durbin-Watson
Statistic 1.0007
14
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 R2 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. 
15
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 R2 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. 
16
B. 90111 (Export of Coffee neither roasted nor
decaffeinated
Model Statistics   Model fit 75.6046 Test
fit 73.7038 Overall fit 75.2571 Adjusted
fit 64.0117 Iterations 54 RMS
error 4.4336 Standard deviation 4.4593 95
confidence interval 8.9186 Mean absolute
error 3.1465 Mean absolute error
() 34.767 F-Statistic 18.7563 Durbin-Watson
Statistic 0.5446
17
C. 251611 (Import of Granite,crude/rough )
 
Model Statistics   Model fit 67.3539 Test
fit 61.8533 Overall fit 66.0773 Adjusted
fit 56.5328 Iterations 66 RMS
error 3.4094 Standard deviation 3.4285 95
confidence interval 6.857 Mean absolute
error 2.7858 Mean absolute error
() 6.6183 F-Statistic 12.4989 Durbin-Watson
Statistic 2.122  
18
2. CHINA
A. 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 Statistics Model fit 85.0775 Test
fit 84.3229 Overall fit 84.9804 Adjusted
fit 74.6557 Iterations 30 RMS
error 1.0522 Standard deviation 1.0571 95
confidence interval 2.1143 Mean absolute
error 0.7224 Mean absolute error
() 24.07 F-Statistic 44.3208 Durbin-Watson
Statistic 1.2491
19
B. CHINA (Import of rest of the codes)  
Model Statistics Model fit 87.8544 Test
fit 82.4129 Overall fit 87.1099 Adjusted
fit 76.5264 Iterations 126 RMS
error 2828.6593 Standard deviation 2841.9707 9
5 confidence interval 5683.9414 Mean absolute
error 2114.0386 Mean absolute error
() 12.5192 F-Statistic 52.9366 Durbin-Watson
Statistic 0.8763
20
C. CHINA (Unit value index for rest of the
codes)  
Model Statistics Model fit 61.607 Test
fit 76.4597 Overall fit 66.02 Adjusted
fit 57.6874 Iterations 46 RMS
error 6.1855 Standard deviation 6.2157 95
confidence interval 12.4314 Mean absolute
error 4.2899 Mean absolute error
() 4.5121 F-Statistic 14.5718 Durbin-Watson
Statistic 0.9655
21
3. USA
A. 420310 (Import of Articles of apparel )
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
R2 121.321 Variance varSOS/(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 mlnn 72.4841 BIC Value ( Uses
var ) see Wei p153 -95.0882 R Square .887551 Du
rbin-Watson Statistic A-A(T-1)2/A2
1.95492 D-W STATISTIC SUGGESTS NO SIGNIFICANT
AUTOCORRELATION for lag1
22
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 103 Number of
Degrees of Freedom n-m 97 Residual Mean Sum R /
n -.783408E-14 Sum of Squares Sum
R2 1578.37 Variance varSOS/(n) 15.3239 Adjusted
Variance SOS/(n-m) 16.2718 Standard
Deviation SQRT(Adj Var) 4.03383 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 AIC
Value ( Uses var ) nln 2m 293.130 SBC Value (
Uses var ) nln mlnn 308.938 BIC Value ( Uses
var ) see Wei p153 -26.2750 R Square .858561 Du
rbin-Watson Statistic A-A(T-1)2/A2
1.88808 D-W STATISTIC SUGGESTS NO SIGNIFICANT
AUTOCORRELATION for lag1.
23
C. 610510 (Import of Men's or boys' shirts of
cotton, knitted or crocheted )
MODEL STATISTICS IN TERMS OF THE ORIGINAL DATA
Number of Residuals (R) n 105 Number of
Degrees of Freedom n-m 99 Residual Mean Sum R /
n -.708456E-01 Sum of Squares Sum R2 10575.5
Variance varSOS/(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))/n 7.73821 AIC
Value ( Uses var ) nln 2m 496.295 SBC Value (
Uses var ) nln mlnn 512.219 BIC Value ( Uses
var ) see Wei p153 165.540 R Square .848765
Durbin-Watson Statistic A-A(T-1)2/A2
2.04567 D-W STATISTIC SUGGESTS NO SIGNIFICANT
AUTOCORRELATION for lag1.
24
  • 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.

25
  • 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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