Effect on Different Economic Factors on NDP(National Domestic

A vision into the economic structure of India

EFFECT ON DIFFERENT ECONOMIC FACTORS ON NDP(NATIONAL DOMESTIC PRODUCT)

Motivation Of The Project.The global economic structure of India is changing from the post independence era. Though it mainly relies on Agriculture in the beginning , but nowadays it relies more on other economic factors. The main objective of my project is to see how the different factors incomes impact the Net Domestic Product of Our Country and also predict some future values. Also we will see that How much the factor incomes effect NDP in the future.

Methodology for the Analysis

For the analysis we shall follow the following process:

Data Collection Data Representation

Data AnalysisBring out our Inferences

Data Collection:

For this Analysis we collected the data on factor Incomes from different websites. Some of Them are given below:

• http://mospi.nic.in / (Indian Planning Commission)• http://www.rbi.org.in/ (Reserve Bank Of India Website)• http://databank.worldbank.org/(World Bank Data Website)

Data Analysis:

For the Analysis we shall use statistical techniques like1. Time Series Analysis: This includes fitting of time series models (like ARIMA,GARCH,etc) and predicting future values through this model.2. Multivariate Time Series Analysis: This method helps in seeing the effects of the time series covariates all at a time.

Before Carrying Our Usual Analysis we have to get in aquaintance with the following terms.

Definition Of Certain Terms: GDP (Gross Domestic Product):It is defined as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs).It is measured in three different approaches:

GDP

Product Approach

Income Approach

Expenditure Approach

But we will use the Income Approach

• GDP: Income Approach: It is the sum of primary incomes distributed by resident producer units .It is measured as

GDP = compensation of employees + gross operating surplus + gross mixed income+ taxes less subsidies on production and imports

Compensation of employees (COE) measures the total remuneration to employees for work done. It includes wages and salaries, as well as employer contributions to social security and other such programs.Gross operating surplus (GOS) is the surplus due to owners of incorporated businesses. Often called profits, although only a subset of total costs are subtracted from gross output to calculate GOS.Gross mixed income (GMI) is the same measure as GOS, but for unincorporated businesses. This often includes most small businesses.

The sum of COE, GOS and GMI is called total factor income. Adding taxes less subsidies on production and imports converts GDP at factor cost to GDP(I).Total factor income is also sometimes expressed as:

Total factor income = employee compensation + corporate profits + proprietor's income + rental income + net interest

Definition of certain terms

Definition of certain terms

NDP:The full form of NDP is National Domestic Product. It is the gross domestic product (GDP) minus depreciation on a country's capital goods.Net domestic product accounts for capital that has been consumed over the year in the form of housing, vehicle, or machinery deterioration. The depreciation accounted for is often referred to as "capital consumption allowance" and represents the amount of capital that would be needed to replace those depreciated assets.If the country is not able to replace the capital stock lost through depreciation, then GDP will fall. In addition, a growing gap between GDP and NDP indicates increasing obsolescence of capital goods, while a narrowing gap means that the condition of capital stock in the country is improving. It reduces the value of capital that is why it is separated from GDP to get NDP.

Factor Income Covariates:It includes Agriculture ,Manufacturing ,forestry ,logging , mining &fishing, Construction ,Electricity ,gas & watersupply, Finance, Real Estate, Insurance & Business Services, Social & Personal Services, Trade ,Hotels ,Transport & Communication.

Data:

Year Agricultureforestry and logging and fishing

Mining & Quarrying

Manufacturing

Electricity, gas and water supply Construction

Trade,Hotels,Transport&Communication

Finance, Insurance, Real Estate & Business Services

Social & Personal Services

Net Domestic Product

1980 40056 4035 1474 18698 912 5771 18045 9264 12084 1103391981 44971 4764 3020 21743 1012 6575 22213 10633 13825 1287561982 47376 5404 3692 24100 1177 7598 25276 11852 16035 1425101983 57806 5919 4021 28553 1417 8878 29635 12903 18362 1674941984 61257 6462 4386 32159 1772 10479 34359 14484 21084 1864421985 65387 6901 4823 35804 2116 12227 40061 16333 23910 2075621986 69446 7586 5155 39447 2300 14426 45328 18170 28349 2302071987 78065 8373 5184 45508 2458 16780 51903 20167 33072 2615101988 97969 9370 6911 54991 2789 19690 61361 23151 38364 3145961989 108387 10894 7469 65045 3301 22606 69814 28097 44644 3602571990 131781 12309 11173 71335 5231 26831 55218 55547 62202 4316271991 155246 13104 11893 75283 6243 30112 63265 67837 72040 4950231992 173652 15026 13663 86822 8405 34349 74697 74878 83804 5652961993 201528 17778 16098 101585 10383 37997 86914 90542 94217 6570421994 231717 20848 17724 127434 13924 43690 101788 101931 105092 7641481995 251674 22249 19518 160554 16369 51398 119665 121983 124432 8878421996 304904 25150 21196 181486 16662 58588 140985 133931 148468 10313701997 319427 29715 26331 185017 20528 72945 170674 152588 173072 11502971998 369790 31497 27957 199460 27351 86464 201723 178239 213106 13355871999 390591 34361 33001 207186 24217 99364 227871 213443 246688 14767222000 439432 21176 45868 306296 48129 119897 252527 274940 317513 18257782001 467815 17265 48055 318496 49692 129390 399094 315689 341496 20869922002 429752 17802 62982 348534 56993 144894 490290 356089 371288 22786242003 476324 48058 64121 391190 59293 168386 623246 400056 411361 26420352004 501415 51007 84776 453603 59892 212807 706073 448952 452436 29709612005 512363 58509 94462 521669 69107 268634 846606 493102 505121 33695732006 585053 81312 106787 634828 76153 322429 998379 586595 573790 39653262007 609024 120242 124812 732720 83830 388908 1150044 691464 703895 46049392008 716276 136559 139828 818322 91070 451033 1310846 845368 883033 53923352009 806646 154928 159304 922151 113883 500458 1481623 964937 1015850 61197802010 928586 176169 204866 1072489 119560 571535 1779630 1165243 1154432 71725102011 1143517 198529 222716 1236182 135670 689797 2072272 1381524 1314734 83949412012 1300569 201135 222417 1320907 157132 759990 2324696 1617076 1522899 94268212013 1417468 211452 222652 1350039 203049 818431 2509908 1939482 1723458 10395939

Comparison of % Share of Different Economic Factors in 2 years

Agriculture14%

forestry and logging and

fishing2%

Mining & Quarrying

2%Manufactur-

ing

13%

Electricity, gas and wa-ter supply

2%

Construction8%

Trade,Hotels,Transport&Communication

24%

Finance, In-

surance, Real Estate & Business Services

19%


2013

We can see the impact of Agriculture and Manufacturing Industry is more than other factors

We can see the impact of Other factors is more than Agriculture and Manufacturing Industry.

Agriculture36%

forestry and logging and

fishing4%

Mining & Quarrying

1%

Manufacturing

17%

Electricity, gas and wa-ter supply

1%

Construction5%

Trade,Hotels,Transport&Communica-

tion16%

Finance,

Insurance, Real Estate & Business

Services8%


1980

Data Representation in terms Of Time Series Curves:

1980 1985 1990 1995 2000 2005 2010

0e+0

02e

+06

4e+0

66e

+06

8e+0

61e

+07

time

Net

dom

estic

Pro

duct

1980 1985 1990 1995 2000 2005 2010

1525

35

time

Agric

ultur

e %

shar

e

1980 1985 1990 1995 2000 2005 2010

1.02.0

3.0

time

Fore

stry

& log

ging &

fishin

g % s

hare

1980 1985 1990 1995 2000 2005 2010

1.52.0

2.5

time

Minin

g &

Quar

rying

% s

hare

1980 1985 1990 1995 2000 2005 2010

1315

17

time

Manu

factu

ring

% sh

are

1980 1985 1990 1995 2000 2005 2010

1.01.5

2.02.5

timeElect

ricity

,gas

and

wat

er su

pply

% sh

are

1980 1985 1990 1995 2000 2005 2010

5.06.0

7.08.0

time

Cons

tructi

on %

shar

e

1980 1985 1990 1995 2000 2005 2010

1418

22

time

Trad

e,Hot

els,T

rans

port

and C

ommu

nicati

on %

shar

e

1980 1985 1990 1995 2000 2005 2010

812

16

time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es %

shar

e

1980 1985 1990 1995 2000 2005 201011

1315

17

time

Publi

c.adm

inist

ratio

n.and

.Def

ence

,soc

ial an

d per

sona

l sha

res %

shar

e

From the diagram given above we have the following observations:

• Here the Agriculture share decreases and falls steeply from 1996 till 2007 after wards becomes stable• The Forestry, logging and fishing share decreases suddenly after 2000-2003 and after that again

rises.• In case of Mining & Quarrying the share falls and rises quite frequently but it has an increasing

trend.• The manufacturing industry has steep rise and falls in the end finally falls steeply after 2007.• In case of Electricity, gas and water supply we see an increasing trend till 2000 and a decreasing

trend after 2000. • In case of Construction the percentage share curve has a steep rise so we can say that construction

industry has an increasing impact on Indian Economy .• Though there is a steep depression from the years 1990-2000 ,the trade ,hotels , transport and

communication industry has recovered from this phase and now has a great position. • The Banking , Finance ,Insurance , Real Estate and Business Services percentage share here

increases and we can interpret it by saying people are using banks and are interested in financial services which is a very good sign.

• In case of public administration , defence , social and personal services also we can see an upward trend with a boom in the year 2000.

Interpretation from the diagrams:

Analysis Of The Data

As far as the analysis is concerned we shall firstly use the usual time series analysis. We shall do it in the following steps:

Step-1Test for

stationarity using Augmented

Dickey Fueller Test

Removing Stationarity:We

shall remove stationarity by

differencing

Step-2Fitting ARMA

Model: We Shall Fit ARMA model on the data and check the goodness of fit

by AIC.

We shall check for the

volatility of the residuals

Step-3 Check For Volatility

If Volatility presents we shall

go for trhe GARCH modell

Step-4 PredictionUsing the given

time series predict for the next 5

years.

Testing for Stationarity(Step-1)Variables ADF Test p-

ValuesNo of differencing

ADF Test p-Values after differencing

Agriculture 0.9821 3 0.03591

Forestry,logging &fishing

0.9677 3 <0.01

Mining & Quarrying >0.99 2 0.02759

Manufacturing >0.99 2 0.04569

Electricity,gas and water supply

>0.99 3 0.01219

Construction >0.99 4 0.01296

Trade,Hotels,Transport and Communication

>0.99 2 <0.01

Banking ,Finance,Insurance,Real Estate and Business Services

>0.99 3 0.0125

Public.administration.and.Defence,social and personal Services

>0.99 3 <0.01

Net domestic Product >0.99 3 <0.01

Analysis of the Data:

Where is the observed value of the variable, is the part of the time series variable explained by the mean part with E()=0 is the residual part .If the process has volatility becomes and has a specific form whereas if = (in both cases is a White Noise process).Thus in the former case we have to go for ARCH or GARCH model. is modelled using the ARMA ,ARIMA or SARIMA to get

Consider a given time series given over time t

Where evolves over time.𝒀 𝒕=𝝁𝒕+𝑿 𝒕

Variable Notations:

For the model we take the following notations:

:Incomes from Agriculture at time t:Incomes from Forestry & logging &fishing at time t: Incomes from Mining & Quarrying at time t: Incomes from Manufacturing at time t: Incomes from Electricity,gas and water supply at time t: Incomes from Construction at time t: Incomes from Trade,Hotels,Transport and Communication at time t: Incomes from Banking ,Finance,Insurance,Real Estate and Business Services at time t: Incomes from Public administration and Defence,social and personal services at time t:National Domestic Product values at time t

For the model we take the following notations:

:Incomes from Agriculture at time t:Incomes from Forestry & logging &fishing at time t: Incomes from Mining & Quarrying at time t: Incomes from Manufacturing at time t: Incomes from Electricity,gas and water supply at time t: Incomes from Construction at time t: Incomes from Trade,Hotels,Transport and Communication at time t: Incomes from Banking ,Finance,Insurance,Real Estate and Business Services at time t: Incomes from Public administration and Defence,social and personal services at time t:National Domestic Product values at time t

ARMA ESTIMATION

For Agriculture:ARMA(2,2) is the best model for the Agriculture stationary data with AIC= 742.01.Thus the model is given below:

For Forestry ,Logging & Fishing :ARMA(3,0) is the best model for the Forestry , Logging & Fishing stationary data with AIC= 657.04.Thus the model is given below:

For Mining & Quarrying :ARMA(0,1) is the best model for the Mining & Quarrying stationary data with AIC= 674.55.Thus the model is given below:

The ARMA model is given as :,where is the AR component and is the MA Component of a Time Series.

For Manufacturing :ARMA(0,0) is the best model for the Manufacturing stationary data with AIC=756.08.Thus the model is given below:

For Electricity,gas and water supply :ARMA(1,1) is the best model for the Electricity,gas and water supply stationary data with AIC= 652.81.Thus the model is given below:

For Construction :ARMA(0,2) is the best model for the Construction stationary data with AIC= 682.7.Thus the model is given below:

For Trade,Hotels,Transport and Communication :ARMA(0,0) is the best model for the Trade,Hotels,Transport and Communication stationary data with AIC= 769.83.Thus the model is given below:

For Banking ,Finance,Insurance,Real Estate and Business Services :ARMA(1,1) is the best model for the Banking ,Finance,Insurance,Real Estate and Business Services stationary data with AIC= 713.53.Thus the model is given below:

For Public administration and Defence,social and personal services :ARMA(0,1) is the best model for the Public administration and Defence,social and personal services stationary data with AIC= 714.43.Thus the model is given below:

Variables X-Squared(Test Statistic)

df P-Value

Agriculture 7.536 6 0.2741

Forestry,logging &fishing

6.3936 7 0.4946

Mining & Quarrying 3.8832 9 0.9189

Manufacturing 7.0025 10 0.7252

Electricity,gas and water supply

6.0192 8 0.6451

Construction 9.5938 8 0.2947

Trade,Hotels,Transport and Communication

18.9396 10 0.4104


7.3738 8 0.4969

Public.administration.and.Defence,social and personal Services

13.2637 9 0.151

Diagnostic Testing (for the Goodness Of fit):Ljung-Box TestHere we perform Ljung-box test on the residuals of the ARMA model.In this test the null hypothesis is the independence of the error components .It is a type of Portmanteau test.

time

Agric

ultur

e

1980 1985 1990 1995 2000 2005 2010

060

0000

1400

000

time

Fore

stry &

logg

ing &

fishin

g

1980 1985 1990 1995 2000 2005 2010

010

0000

2000

00

time

Minin

g & Q

uarry

ing

1980 1985 1990 1995 2000 2005 2010

010

0000

time

Manu

factur

ing

1980 1985 1990 1995 2000 2005 2010

060

0000

1400

000

time

Electr

icity,

gas a

nd w

ater s

upply

1980 1985 1990 1995 2000 2005 2010

010

0000

2000

00

time

Cons

tructi

on

1980 1985 1990 1995 2000 2005 2010

0e+0

04e

+05

8e+0

5

time

Trad

e,Hote

ls,Tr

ansp

ort a

nd C

ommu

nicati

on

1980 1985 1990 1995 2000 2005 2010

010

0000

025

0000

0

time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es

1980 1985 1990 1995 2000 2005 2010

010

0000

0

time

Publi

c adm

inistr

ation

and D

efenc

e,soc

ial an

d per

sona

l ser

vices

1980 1985 1990 1995 2000 2005 20100

1000

000

Fitting of the ARMA model on the dataset

Here some points are missing the line

time

Agric

ultur

e

1980 1985 1990 1995 2000 2005 2010

-500

000

5000

0

time

Fore

stry &

logg

ing &

fishin

g

1980 1985 1990 1995 2000 2005 2010

-100

0020

000

time

Minin

g & Q

uarry

ing

1980 1985 1990 1995 2000 2005 2010

-200

000

2000

0

time

Manu

factur

ing

1980 1985 1990 1995 2000 2005 2010

-500

0050

000

time

Electr

icity,

gas a

nd w

ater s

upply

1980 1985 1990 1995 2000 2005 2010

-200

000

2000

0

time

Cons

tructi

on

1980 1985 1990 1995 2000 2005 2010

-600

000

4000

0time

Trad

e,Hote

ls,Tr

ansp

ort a

nd C

ommu

nicati

on

1980 1985 1990 1995 2000 2005 2010

-500

0050

000

time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es

1980 1985 1990 1995 2000 2005 2010

-400

000

4000

0

time

Publi

c adm

inistr

ation

and D

efenc

e,soc

ial an

d per

sona

l ser

vices

1980 1985 1990 1995 2000 2005 2010

-600

000

4000

0

Now we study the residuals:

Thus we can see that there exists volatility in the data.

GARCH MODELThe Garch model is given as where 𝑿 𝒕=𝝈𝒕∗𝝐𝒕

For Agriculture:GARCH(1,1) is the best model for the Agriculture stationary data . Thus the model is given below:

For Forestry ,Logging & Fishing :GARCH(1,1) is the best model for the Forestry ,Logging & Fishing stationary data . Thus the model is given below:

For Mining & Quarrying :GARCH(1,1) is the best model for the Mining & Quarrying stationary data . Thus the model is given below:

For Manufacturing :GARCH(1,1) is the best model for the Manufacturing stationary data . Thus the model is given below:

For Electricity,gas and water supply :GARCH(1,1) is the best model for the Electricity,gas and water supply stationary data . Thus the model is given below:

For Construction :GARCH(1,1) is the best model for the Construction stationary data . Thus the model is given below:

For Trade,Hotels,Transport and Communication :GARCH(1,1) is the best model for the Trade,Hotels,Transport and Communication stationary data . Thus the model is given below:

For Banking ,Finance,Insurance,Real Estate and Business Services :GARCH(1,1) is the best model for the Banking ,Finance,Insurance,Real Estate and Business Services stationary data . Thus the model is given below:

For Public administration and Defence,social and personal services :GARCH(1,1) is the best model for the Public administration and Defence,social and personal services stationary data . Thus the model is given below:

Residuals before Garch Estimation

time

Agric

ultur

e

1980 1985 1990 1995 2000 2005 2010

-500

000

5000

0Residuals after Garch Estimation

time

Agric

ultur

e

1980 1985 1990 1995 2000 2005 2010

-2-1

01

2


time

Fore

stry &

logg

ing &

fishin

g

1980 1985 1990 1995 2000 2005 2010

-100

000

1000

020

000

3000


time

Fore

stry &

logg

ing &

fishin

g

1980 1985 1990 1995 2000 2005 2010

-2-1

01

23


time

Minin

g & Q

uarry

ing

1980 1985 1990 1995 2000 2005 2010

-200

00-1

0000

010

000

2000

030

000

Residuals after Garch Estimation

time

Minin

g & Q

uarry

ing

1980 1985 1990 1995 2000 2005 2010

-2-1

01

23


time

Manu

factur

ing

1980 1985 1990 1995 2000 2005 2010

-500

000

5000


time

Manu

factur

ing

1980 1985 1990 1995 2000 2005 2010

-2-1

01

23


time

Cons

tructi

on

1980 1985 1990 1995 2000 2005 2010

-600

00-4

0000

-200

000

2000

040

000


time

Cons

tructi

on

1980 1985 1990 1995 2000 2005 2010

-2-1

01

2


time

Elec

tricity

,gas a

nd w

ater s

upply

1980 1985 1990 1995 2000 2005 2010

-100

00-5

000

050

0010

000

1500

020

000

2500

0Residuals before Garch Estimation

time

Elec

tricity

,gas a

nd w

ater s

upply

1980 1985 1990 1995 2000 2005 2010

-1.0

-0.5

0.00.5

1.01.5

2.0


time

Trad

e,Hote

ls,Tr

ansp

ort a

nd C

ommu

nicati

on

1980 1985 1990 1995 2000 2005 2010

-500

000

5000

010

0000


time

Trad

e,Hote

ls,Tr

ansp

ort a

nd C

ommu

nicati

on

1980 1985 1990 1995 2000 2005 2010

-10

12

3


time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es

1980 1985 1990 1995 2000 2005 2010

-400

00-2

0000

020

000

4000

060

000


time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es

1980 1985 1990 1995 2000 2005 2010

-1.0

-0.5

0.00.5

1.01.5

2.0


time

Publi

c adm

inistr

ation

and D

efenc

e,soc

ial an

d per

sona

l ser

vices

1980 1985 1990 1995 2000 2005 2010

-600

00-4

0000

-200

000

2000

040

000

6000


time

Publi

c adm

inistr

ation

and D

efenc

e,soc

ial an

d per

sona

l ser

vices

1980 1985 1990 1995 2000 2005 2010

-10

12

A Multivariate Approach to the Analysis

A multivariate analogue to our current analysis is to use the VAR model.The usual Vector Autoregressive model (VAR) of order p is given below:

𝑿 𝒕=𝑨𝟏∗𝑿 ( 𝒕−𝟏 )+𝑨𝟐∗ 𝑿 (𝒕−𝟐 )+…+𝑨𝒑∗𝑿 ( 𝒕−𝒑 )+∈𝒕

Where is the vector of time series variables and ’s are coefficient matrices.

A Second form of the model is the Error Correction Model which is given below:

+

Where π=G*H and H is the cointegrating matrix

Cointegration and VAR model fitting

Here we perform Johansen’s Test which gives the following results:

test 10pct 5pct 1pct

r<=8 | 2.48 10.49 12.25 16.26

r<=7 | 18.79 16.85 18.96 23.65

r<=6 | 26.6 23.11 25.54 30.34

r<=5 | 60.41 29.12 31.46 36.65

r<=4 | 61.96 34.75 37.52 42.36

r<=3 | 98.59 40.91 43.97 49.51

r<=2 | 128.19 46.32 49.42 54.71

r<=1 | 139.6 52.16 55.5 62.46

r<=0 | 203.03 57.87 61.29 67.88

Eigenvalues (lambda): 9.982438e-01 9.872533e-01 9.817916e-01 9.540891e-01 8.557612e-01 8.486086e-01 5.645060e-01 4.441629e-01 7.448209e-02 -2.081668e-16

Values of test statistic and critical values of test:

Since the rank of π is 8 <10 we can say that the model has unit roots and we have to fit an EC Model with Co integrating Matrix.

=(-3.238e+03 ,5.687e+02 ,-7.172e+01 ,1.524e+03 , 5.683e+02 ,-5.766e+02 ,-5.648e+02 ,5.690e+02 ,1.778e+03)

𝑨𝟏′ =¿-0.0107 1.711 2.26 0.6542 -3.788 -3.025 0.4509 0.0499 0.8652

0.1137 0.54 2.19 -0.0962 0.488 -1.164 -0.0757 -0.1508 -0.1776

0.0236 0.317 0.687 0.0603 0.143 -0.194 -0.1418 -0.3362 0.0032

-0.4633 0.687 1.861 1.3537 -3.689 -1.483 -0.5694 -1.1127 0.2657

-0.0479 -0.375 -0.442 0.0972 -0.817 0.19 -0.0698 -0.0466 0.1946

0.1601 0.432 1.368 -0.0214 0.16 0.365 0.2542 -0.3114 -0.0951

-0.0635 -0.656 3.048 0.407 2.581 0.51 0.3938 -0.7854 -0.0378

-0.1771 0.719 -1.082 0.1728 -1.43 0.561 0.3848 0.7461 -0.0203

-0.2172 1.201 -1.889 0.303 -1.936 2.504 -0.2558 0.2963 0.5503

1.4186 0.00348 5.302 -0.6288 -5.11 0.4663 -0.1388 -0.8322 -0.0128

-0.0707 -0.11726 1.016 0.2962 -0.804 1.2232 0.1243 -0.1032 -0.1797

-0.0674 -0.03084 0.624 -0.1106 1.088 0.4343 0.0926 -0.0119 0.2639

0.6912 0.74337 7.535 -1.4054 5.047 2.0921 0.403 -0.581 0.7703

0.1196 -0.02824 0.322 -0.207 1.006 0.0885 0.0641 -0.0165 -0.0137

0.0243 0.23226 -0.881 -0.2746 0.651 0.6107 -0.0236 0.0805 -0.0758

-0.4014 -0.3115 -1.818 0.0164 -0.121 0.1251 0.1219 0.6366 0.6204

0.5338 -1.25299 -1.675 -0.5942 -2.267 0.8548 -0.2309 0.1949 0.341

0.3126 -2.0733 -2.391 -0.2038 0.952 -0.4908 0.0262 -0.2423 0.6013

Estimated Values of the VAR Coefficients:

G=

-2.51E+00 -1.41E-01 -5.91E-02 -2.19E-01 1.44E-01 -3.06E+00 -4.63E-01 -1.37E-01 6.20E-02 4.55E-011.78E+00 -1.51E+00 4.29E-01 4.46E-01 -3.99E-01 9.34E-01 -9.27E-01 1.03E-01 -2.81E+00 -1.44E+004.91E+00 -3.65E-01 8.77E-02 2.57E+00 -1.24E+00 7.87E+00 -6.57E-01 -3.80E-01 1.34E+00 -3.34E+005.72E-01 1.07E-01 1.63E-01 -4.55E-01 -1.38E-01 -6.66E-01 5.06E-01 -8.49E-02 -2.30E+00 -4.22E-01

-1.83E+00 3.25E-01 -2.58E-01 -4.07E+00 -2.66E+00 -1.61E+00 -6.10E-01 2.29E-01 -5.77E+00 -4.55E+002.94E-01 -5.96E-01 2.59E-02 1.27E-01 1.49E-01 1.18E+00 -7.84E-01 2.80E-01 9.42E-01 3.82E-01

-3.33E-01 -2.91E-02 -6.25E-02 3.49E-01 4.48E-01 6.98E-02 -1.92E-01 1.02E-01 6.43E-01 2.81E-01-5.77E-01 2.52E-01 -2.91E-01 2.40E-01 4.69E-01 -1.39E+00 4.38E-01 -4.06E-01 3.57E-01 6.41E-011.06E+00 -8.06E-01 8.45E-02 8.39E-01 3.29E-01 2.70E+00 -1.32E+00 5.90E-01 2.40E+00 4.97E-01

H=-3.06E+00 -4.63E-01 -1.37E-01 6.20E-02 4.55E-01 -3.18E-02 -3.00E-01 4.42E-01 3.39E-01

9.34E-01 -9.27E-01 1.03E-01 -2.81E+00 -1.44E+00 -4.07E-02 -3.36E-01 -2.50E+00 6.68E-01

7.87E+00 -6.57E-01 -3.80E-01 1.34E+00 -3.34E+00 -1.89E+00 2.29E+00 -2.96E+00 -4.93E-01

-6.66E-01 5.06E-01 -8.49E-02 -2.30E+00 -4.22E-01 -4.71E-01 5.51E-01 -8.40E-01 7.69E-02

-1.61E+00 -6.10E-01 2.29E-01 -5.77E+00 -4.55E+00 -6.53E-04 1.86E+00 5.11E-01 -3.07E+00

1.18E+00 -7.84E-01 2.80E-01 9.42E-01 3.82E-01 -1.75E+00 1.87E-01 4.79E-01 -6.01E-01

6.98E-02 -1.92E-01 1.02E-01 6.43E-01 2.81E-01 3.41E-01 -4.37E-01 7.18E-01 -1.73E-01

-1.39E+00 4.38E-01 -4.06E-01 3.57E-01 6.41E-01 -1.17E-01 -8.80E-02 -1.44E+00 9.57E-01

2.70E+00 -1.32E+00 5.90E-01 2.40E+00 4.97E-01 4.48E-01 1.47E-01 1.06E+00 -2.00E+00

PredictionHere we have a five year prediction based on the time series model that we have obtained. These values are given below:

2014 2015 2016 2017 2018

Agriculture 1593810 1745342 1907452 2092458 2263180

Forestry,Logging & Fishing 222161.1 231791.4 227308.5 228772.1 228979.8

Minning &Qyarrying 233073.7 243495.3 253917 264338.6 274760.3

Manufacturing 1379171 1408303 1437435 1466567 1495699

Trade,Hotels,Transport & Communication 2695120 2880332 3065544 3250756 3435968

Public.administration.and.Defence,social and personal services 1932909 2151252 2378487 2614613 2859632

Electricity,gas and water supply 239603.8 287786.8 334569.2 388037.9 443173.5

Construction 882073.2 950550.6 1023497 1100547 1181333

Banking ,Finance,Insurance,Real Estate and Business Services 2271865 2659905 3076458 3537654 4033909

Comparison Of Contribution of Factor Incomes to NDPThe predicted value also shows that the effect of agriculture becomes insignificant

The other factors are rising but still very slowly

Agriculture14%

Forestry,Logging & Fish-

ing2%

Minning &Qyarrying

2%

Manufactur-ing12%

Trade,Hotels,Transport & Commu-

nication24%

Public.ad-ministra-

tion.and.De-fence,social

and per-sonal ser-

vices17%

Electricity,gas and wa-ter supply

2%

Construction8%

Banking ,Finance,Insurance,Real Es-

tate and Business Services

20%

Factor Incomes in 2014

Agriculture14% Forestry,Lo

gging & Fishing

1%

Minning &Qyarrying

2%

Manufactur-ing9%

Trade,Hotels,Transport & Commu-

nication21%

Public.ad-ministra-

tion.and.Defence,social

and per-sonal ser-

vices18%

Electricity,gas and wa-ter supply

3%

Construc-tion7%


25%

Factor Incomes on 2018

2014 2015 2016 2017 2018

13.90

13.94

13.98

time

Agric

ultur

e % sh

are

2014 2015 2016 2017 2018

1.41.6

1.8

time

Fore

stry &

logg

ing &

fishin

g % sh

are

2014 2015 2016 2017 2018

1.70

1.85

2.00

time

Minin

g & Q

uarry

ing %

shar

e

2014 2015 2016 2017 2018

9.510

.511

.5

time

Manu

factur

ing %

shar

e

2014 2015 2016 2017 2018

2.12.3

2.52.7

timeElectr

icity,

gas a

nd w

ater s

upply

% sh

are

2014 2015 2016 2017 2018

7.37.5

7.7

time

Cons

tructi

on %

shar

e

2014 2015 2016 2017 2018

21.5

22.5

23.5

time

Trad

e,Hote

ls,Tr

ansp

ort a

nd C

ommu

nicati

on %

shar

e

2014 2015 2016 2017 2018

2022

24

time

Bank

ing ,F

inanc

e,Ins

uran

ce,R

eal E

state

and B

usine

ss S

ervic

es %

shar

e

2014 2015 2016 2017 201817

.017

.4

time

Publi

c.adm

inistr

ation

.and.D

efenc

e,soc

ial an

d per

sona

l sha

res %

shar

e

Forecasted percentage shares

• We can see that the share in the Agriculture decreases till 2015 ,rises from there and attains maximum in 2017 after which it again decreases.

• We see that Mining ,Forestry ,Manufacturing , Construction ,Trade & Hotels & Transport industry have a decreasing percentage shares and the government should look upon it in future

• We also find out that Financial Services and Electricity, Gas and water supply has an increasing percentage share and government should invest upon these factors as they provide them more incomes.

Findings from the forcasted figures:

• Here we have data on factor incomes like Agriculture , Forestry & logging & fishing , Mining & Quarrying ,Manufacturing Electricity , gas and water supply ,Construction ,Trade,Hotels,Transport ,Banking ,Finance ,Insurance , Real Estate and Business Services Public administration and Defence,social and personal services and we have to study its effects on NDP and see how these are effecting NDP in future

• Here we see though Agriculture was the main determinant of Income at the first few years but later we find that other factors have more effect at the succeeding years and these play important roles in future.

• We see that Public administration and Defence , social and personal services , Banking , Finance , Insurance, Real Estate and Business Services, Construction ,Mining & Quarrying , Electricity , gas and water supply has an increasing trend though they are harmonic rising in some years and falling in some years

• We see the remaining factors have a decreasing percentage share of incomes.

• We have fitted ARMA Models on the entire data see that the fitting is good except for some points

• We have tested for Volatility factor and find that we have to fit GARCH Model on the data

• Here the most appropriate model is the GARCH (1,1) model and see that the residuals after fitting is not volatile

• We have used Augmented Dickey Fuller test before ARMA fitting to remove trend component by finding the no of unit roots, Portmanteau tests for diagnostic checking (goodness of fit of the ARMA model),Lagrange's test for the volatility checking.

• In case of ADF test we found the presence of unit roots in all the covariates and have to difference to remove trend .

• We have also fitted a Multivariate model to see the effects of the covariates at the same time on different years. Since Johansen’s Test suggests the presence of cointegrating effects we used Error Correction Model form of the usual VAR model having cointegrated effects(i.e Cointegrating matrix)

• Now we get predictions from the fitted model and can infer that under the continuing conditions the share in the Agriculture decreases till 2015 ,rises from there and attains maximum in 2017 after which it again decreases. Mining ,Forestry ,Manufacturing , Construction ,Trade & Hotels & Transport industry have a decreasing percentage shares and Financial Services and Electricity, Gas and water supply has an increasing percentage share.

Theory Sources:

• Economics: Paul & Samuelson , William D Nordhaus• Chris Chatfield: Analysis of Time Series: An Introduction. • Use R : Time Series And Cointegration **Springer

Statistical Softwares:

• R Statistical Software• Minitab

Other Softwares:

• Ms-Excel• MS-Word(For Documentation)• MS-Powepoint(ForPresentation)

Project submitted by

Subhodeep Mukherjee

Roll No: 91/STS/131032 Reg No: A03-1112-0103-10

University Of Calcutta

Department Of Statistics

Effect on Different Economic Factors on NDP(National Domestic

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