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T.C.

EGE UNIVERSITY

FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES

Department of Economics

Project Title

The Impacts of Research & Development Expenditures and Public

Investment To Economic Growth in U.S.A

Student Namekke KORKMAZ

Number

15 06 960

ZMR 2011

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CONTENTS

1.INTRODUCTION

2.LITERATURE REVIEW

3.FORMULATION OF THE MODEL

3.1 R & D VARIABLE

3.2 PUBLIC INVESTMENTS VARIABLE

3.3 GDP VARIABLE

3.4 THE METHOD USED

3.5 DATA SET

4.DATA SOURCES AND DESCRIPTION

5.ESTIMATION OF THE MODEL

Jargue-Bera Test

Whites Heteroscedasticity Test

Durbin-Watson d Test

Breusch-Godfrey Test

Ramseys Reset Test

6.INTERPRETATION OF THE RESULTS

7.CONCLUSION

APPENDIX

A-The Data Set

B- Computer Printouts

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1.INTRODUCTION

This project is research and development expenditures and public investments in the

United States between 1981-2008 and was prepared to interpret the relationship between Gross

Domestic Product. Americas research and development spending than other countries have

started earlier, has provided us the opportunity to look at a broader perspective. Research anddevelopment expenditures, R & D, the Gross Domestic Product GDP has been shortened to.

Aim of the study on the effect of R & D expenditure and public investments to GDP is

calculated and if there is such a domain to analyze and interpret the degree of impact. R & D

expenditures for this project to us whether it is necessary, it will show the long-run recovery. The

total cost incurred for the work of our R & D will be variable. Our expectation, R & D

expenditures and public investments on GDP is effective, the increase in GDP growth also affects

the public investments and R & D expenditures.

2.LITERATURE REVIEW

R&D to economic growth model based on the first time, Romer(1990) analyzed by.

Subsequently, this approach Rivera-Betiz & Romer, Grossman & Helpman and Agihon & Howitt

developed by .

Then, in this regard Ege University Faculty of Economics and Administrative Sciences,

Department of Economics, Prof. Dr. Aysen Kaya and her student Onur Altn (2009), Turkey, theCausal Relationship Between R & D Expenditures and Economic Growth Analysis, study from1990 to 2005 period, R & D expenditures within the scope of work includes the analysis of causality

between growth and have made. In this study, VEC(vector error correction) model was used. The

test result of R & D expenditure to economic growth for Turkey toward a long-run causal

relationship was found.

Another study in this subject Balkesir University, Department of Economics instructor,Asst.Assoc. Dr. Suna Korkmaz his econometric series(2009). Data between the years 1990-2008, in

Turkey Have The Relationship Between R & D Investment and Economic Growth Model with

Analysis by the Granger causality test results of the study could not have found a short-run

relationship.

Impulse response analysis, an increase in R & D spending affects GDP and suggested that

this effect continued throughout the 10 periods.

Ram (1987), internationally comparable income and government expenditure data from theyears 1950-1980 for 155 countries and used to prove the validty of Wagners Hypothesis. Theresults of the study, time series data, according to a horizontal-sectional data suggest that support for

more than Wagner Hypothesis.

Gnalp and Gr (2002), using the techniques of panel data set of more recent data for 34developing countries, ram (1986) s two sector growth model to estimate re-tried. Estimation resultsof Ram (1986) in confirming the findings of a horizontal-sectional analysis, with the size of the state

of developing countries is that a positive relation between economic growth performance.

Given the positive relationship in public investment, but were not statistically significant

according to Barro (1960), the size of public investment in GDP affect the growth rate significantly.

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3.FORMULATION OF THE MODEL

In this study, the relationship between economic growth, public investment and R & D

expenditures following the model used in the investigation:

GDPt= 0+ 1.RD + 2.PINV + ut

GDP: Gross Domestic Product

RD: Research and Development

PINV: Public Investments

3.1 R & D VARIABLE : Engineers and scientists employed by companies, in line with

working space to perform a new invention. Than the aim of carrying forward an existing invention

take advantage of the maximum level of technology. Thus the high profits to the company and itworks in order to ensure continuity. In addition, in the form of increased efficiency in production. That

is, the same amount of input, more output can be achieved R & D investments.

3.2 PUBLIC INVESTMENTS VARIABLE: Public investment, government

expenditures on higher investment costs, is done by the government. There are al lot of impact of

public investments, especially to open the way for technological innovations. Human capital

accumulation, labor productivity growth increases so. Study of a very high education level and

growth of public spending on education or have found a positive correlation between the growth.

3.3 GDP VARIABLE: Gross Domestic Product (GDP), in the economy the monetary

value of goods and services produced within a time frame means. Is one of the important indicators ofmacroeconomics. The size of the GDP, the economy also depends on the number of people who live

and work. Therefore, during a long period of time, or to make a comparison between countries is

necessary to distinguish the impact of population growth. GDP per person is used as a criterion. In

addition, there is an effect over the years, after adjusting for inflation and the real GDP deflator

should be used.

3.4 THE METHOD USED: The testing will occur between the years 1981-2008 for the

U.S. data will be used for the OLS method. Examined the effect of GDP on R & D expenditures and

public investments, the model will be tested. The model that we will be establishing a point, the data

is to be estimated by taking the logarithm. Thus deviations from the data and the forecast periods, the

minimum level will be affected.

3.5 DATA SET: In this study used U.S. data Organization for economic Co-operation

And Development (OECD), the National Science Foundation (NSF) and Bureau of Economic

Analysis (BEA) National Economic Accounts web pages have been obtained.

4.DATA SOURCES AND DESCRIPTION

The reason we prefer the United states, is the owner of the worlds largest GDP, the country isengaged in research and development investment and public investments for most. According to the

information we get from the data before using it for sustainable growth, R & D expenditure and public

investments in GDP we expect to be even higher rate. That is not our figures for the criteria, should berates.

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As mentioned at the begining of this section, we figure we will as a criterion in R & D, which

is the ratio of GDP. As a result; rates, right estimation and we will take you to the right conclusion. If

we look at the numbers in the United States of R & D expenditures in 1990 152.502.200.000$, in 2007

372.687.280.000$(OECD) ie R & D expenditure increased by 2,44 times. GDP in 1990

5.754.800.000.000$, in 2007 14.010.800.000.000$(BEA) ie GDP increased by 2,43 times.

In developed countires, GDP growth rates range from 0 to 1 percent. Developing countries can

grow or shrink by 10 percent every year. Of a developed country , the United States, GDP of them

out two times in 50 years. For a devoloping country, this time for 13 years.

If we look at the numbers in the United States of public investment in 1990

90.400.000.0000 $, in 2007 162.000.000.000$ ie public investment expenditures increased by 1.8

times. GDP in 1990 5.754.800.000.000$, in 2007 14.010.800.000.000$(BEA) ie GDP increased by

2,43 times. Numbers as it shows us the basic dynamics of growth, public investment and R & D

expenditures. R & D expenditures in the long-run recycling is less efficient than public investment

may seem, to R & D driven growth will be a more effective result. To R & D based growth model,

technological progress and its consequences such as increased efficiency in production as a result.

5.ESTIMATION OF THE MODEL

Firstly, the model must be estimated. Independent variables, the impact on the dependent

variable , has been tested and the following results were obtained with the help of the

econometric package program.

GDP = C(1) + C(2)*RD + C(3)*PINV

GDP= -2918.2 + 4.52*RD + 33.7*PINV

t-is Prob.

1 3.795716 0.0008

2 3.795716 0.0004

As shown in the tables, public investments and R & D expenditures was significantly,

acceptable. In the other words this variable, United States has an impact on GDP.

In model variables, the dependent variable yielded effective results statement. As a result

estimated models R2 value 0.97 and this value is very big value for estimation model.

(Note: See Appendix A-1 and A-2 for the computer printouts)

We used the diagnostic test to check whether the model satisfies the OLS assumptions. So, we

performed the tests of normality, homoscedasticity, autocorrelation and specification error. The results

are given respectively;

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Jarque-Bera Test: Normality Test

We carried out the Jarque-Bera test for the purpose of testing whether residuals are

normally distributed or not and we obtained the following results;

H0 :Residuals are normally distributed at 5% significance level

H1 : Residuals are not normally distributed

If JBcalc>JBcritic ,we reject the null hypothesis and p-value> dont reject the null hypothesis

JB statistics is asymptotically distributed chi-square with 2 df.

p-value> dont reject the null hypothesis p-value=0.86 =0.05

0.86>0.05 we dont reject the null hypothesis and our residuals are normally distributed.

Jargue-Bera Test is carried out is explained by Gujarati, D.N, fifth edition

Whites General heteroscedasticity Test is carried out is explained by Gujarati, D.N, fifthedition p:387

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Whites General Heteroscedasticty Test

Another important point is whether there is heteroscedasticty in data or not. By applying

Whites heteroscedasticity test to residuals of the regression.

The Steps Of Whites Test

1) Estimate the equation above and obtain the residuals i2) Run the following auxiliary regression and obtain R2 from this auxiliary regression.

3) H0 : there is homoscedasticty

H1 : there is heteroscedasticty

4) n.R22 df

t2=-27828.26-0.04*RD-1.15*RD^2+0.001*RD*PINV+35997.54*PINV-785.82*PINV^2

R2 n*R2 P-value0.2438 6.8264 0.2337

H0 :There is homoscedasticty at 5% significance level

H1: There is no homoscedasticty

n*R2~ 2 (chi-square distribution) 2(m-1), 0.05 2

5, 0.05=11.0705

m: number of variable+u

n*R2> 2crictical Dont reject the null, there is homoscedasticity (Critical > Calculate)

11.00705 > 6.8264

So,Dont reject the null, there is homoscedasticity

(Note: See Appendix A-3 for the computer printouts)

Test For First Order Autocorrelation: Durbin Watsond- Test

Assumptions Underlying The d-Statistic

1) The regression model including an intercept term.

2) The explanatory variables, the Xs are fixed in repeated sampling.

3) The disturbances ut are generated by the first-order autoregressive scheme: ut =put-1+t4) The error terms ut are assumed to be normally distributed.

5) The model doesnt include the lagged value(s) of the depended variable as one of theexplanatory variable.

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2

2

1)(

t

tt

u

uud

Positive

autocorrelation

area

indecision No autocorrelation

area

=0

indecision Negative

autocorrelation

area

0 dL du 2 4-du 4-dL 4

dL: DW lower level

du: DW upper level

We use Durbin-Watson d test to test whether there is first order autocorrelation or not.

The Steps Of DW Test

1) Run the OLS and obtain residuals.

2) Compute d-statistic

3) For the given sample size and given number of explanatory variables find out critical dL and

dU values.

4) Make your decision about your hypotesis.

H0:=0 there is no autocorrelation

H1:0 there is autocorrelation

t =-2918.21+4.51 RD+33.72 PINV

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413.0

)(2

2

1

t

tt

u

uud

n=28 k=2 dL=1.255 du=1.560 (5%) significance level

n:number of observation

k:independent variable

Positive

autocorrelation

area

indecision No autocorrelation

area

=0

indecision Negative

autocorrelation

area

0 0.413 1.255 1.560 2 2.44 2.745 4

Finally we reject the null hypothesis. There is a autocorrelation problem and we must use

higher autocorrelation test for autocorrelation problem.

(Note: See Appendix A-2 for the computer printouts)

The Breusch-Godfrey (BG) Test: Test For Higher Order Autocorrelation

1) Lagged values of the regression y

2) Higher order autoregressive scheme, such AR(1), AR(2),

The BG Test Steps:

1) Estimate the regression by O and obtain residuals, t2) t on t , t-1 t-p obtain R

2

3) fthe sample siz is large: (n-p)*R2 2p

Estimated Regression Model : Y=1+2X2+ 3X3+U

Auxiliary Model: Ut=1+ 2 X2+3X3+1 ut-1+ 2 ut-2+Vt

Durbin Watson-d- Test is carried out is explained by Gujarati, D.N, fifth edition p:434

Breusch-Godfrey Test is carried out is explained by Gujarati, D.N, fifth edition p:438

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H0:1= 2=0 B-G=(n-s)*R2 s:degrees of freedom

H1: 1 20 n: number of observation

B-G> 2critic reject H0

Prob.

Prob. F(2,19) 0.3089

Prob. Chi- square(2) 0.2204

Finally if we use prob value ; if Fprob value>0.05 reject H1 hypothesis and solving the

autocorrelation problem.

0.3089>0.05 our p-value is bigger than 0.05 and we accept H0 Hypothesis.

(Note: See Appendix A-4, A-5 and A-6 for the computer printouts)

Regression Specification Error Test: Ramseys RESET Test

We use Ramseys Reset Test whether there is specification error or not.

The Steps Of Ramseys Reset Test

1) Estimated i2) Run the regression

3) Use restricted F test

4) If the computed F value is significant, one can say that the model is misspecified.

t=-1778.27+2.49*RD+30.96*PINV+3.07FITTED^2

RD PINV FITTED2

Prob. 0.1259 0.0007 0.0757

F statistic 3.4467F Prob. 0.0757

H0:There is no specification error at 5% significance level

H1:There is specification error

Fcritc=F, new added variable number, total variable-new model variable number

Fcritc=4.24 from F table Fcalc

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6.INTERPRETATION OF THE RESULTS

According to the results, statistically significant variables in our model we face the 1and 2we see coming. This is our R & D expenditures and public investments, the relationship suggests

that the United tates GDP. Coefficient proved to be positive as we expect. Because of economic

forecasting, in fact, is that the public investment and R & D expenditures to increase nationalincome. However, this model has a major impact on public investment, public investment in R & D

expenditures have an impact until we see that. The reason for this may be the R & D expenditures in

a long time to gain economic value. However, public investment in private sector investments,

triggering creates a multiplier effect and the economy in the shortrun will have an effect quickly.

When we look at the value obtained with the prediction of our model R2 we see that a

very high value was obtained. This value (0.97) public expenditures and R & D expenditures in

a way, the real impact of the United States GDP interpret the ratio of approximately 97%. In

other words, independent variables, dependent variables, a statement was effective in 97%.

In general, our results were in accordance with the economic literature. But to actuallymodel the more accurate results, consumption, foreign trade, such as a fact that should add many

more variables.

7.CONCLUSION

The objective of this project is to explain the relationship between R & D, public investment

and GDP in U.S.A in the period 1981-2008. In other words in the study we tried to present some

empirical results about this relationship in U.S.A. Our results were in line with expectations. This

relationship in the economic projections for the period 1981-2008 is conformity with UnitedStates tested.

Public investment in infrastructure makes the economy , private sector R & D activities

and forwarding these to the economy can contribute to greater public investment. This

contribution, employment, income and productivity is seen as the GDP increases as a result. As we

can see the results of public investments is greater than the impact on national income. In addition,

research and development activities, the economy in a longer-term impact on our results for the effect

of R & D to the GDP, according to public investments has been smaller.

As a result of the study before the results yielded similar results. In addition to the

United States of the R & D investments, Turkey and other developing countries than in theearly to begin the recycling of R & D has led us to see more clearly.

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APPENDIX

DATA SET

YEARSPUBLIC INVES

BILLION $ YEARS R & D MILLION $ GDP BILLION $

1981 51.9 1981 72628,92 $3.103,80

1982 50.9 1982 81015,27 $3.227,70

1983 58 1983 90478,02 $3.506,90

1984 62.7 1984 102970,56 $3.900,40

1985 67.7 1985 115082 $4.184,80

1986 70 1986 120360 $4.425,00

1987 77.6 1987 126873 $4.699,00

1988 74.3 1988 134108,55 $5.060,701989 81 1989 141973,56 $5.439,60

1990 90.4 1990 152502,2 $5.754,80

1991 99.4 1991 161655,04 $5.943,20

1992 108.8 1992 166095,6 $6.291,50

1993 113.1 1993 166018,93 $6.614,30

1994 99.6 1994 169435,05 $7.030,50

1995 107.2 1995 183982,5 $7.359,30

1996 115.1 1996 197711,06 $7.783,90

1997 122.4 1997 212767,73 $8.278,90

1998 122.6 1998 227266 $8.741,00

1999 133.8 1999 245546,4 $9.301,00

2000 123.4 2000 268257,48 $9.898,80

2001 122.4 2001 278362,08 $10.233,90

2002 132.4 2002 277463,24 $10.590,20

2003 133.8 2003 289428,12 $11.089,20

2004 134.8 2004 300032,42 $11.812,30

2005 145 2005 323298,29 $12.579,70

2006 158.8 2006 348074,82 $13.336,20

2007 162 2007 372687,28 $14.010,80

2008 174.3 2008 398032,38 $14.369,40

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A-1

Estimation Command:

=========================LS GDP C RD PINV

Estimation Equation:=========================GDP = C(1) + C(2)*RD + C(3)*PINV

Substituted Coefficients:=========================GDP = -2918.21713716 + 4.51135262358e-05*RD + 33.7254767997*PINV

A-2

Dependent Variable: GDP

Method: Least Squares

Date: 05/17/11 Time: 23:51

Sample: 1981 2008

Included observations: 28

Variable Coefficient Std. Error t-Statistic Prob.

C -2918.217 273.7028 -10.66199 0.0000

RD 4.51E-05 1.19E-05 3.795716 0.0008

PINV 33.72548 8.205033 3.795716 0.0004

R-squared 0.965567 Mean dependent var 3994.839

Adjusted R-squared 0.962812 S.D. dependent var 2250.301

S.E. of regression 433.9519 Akaike info criterion 15.08470

Sum squared resid 4707857. Schwarz criterion 15.22744

Log likelihood -208.1858 Hannan-Quinn criter. 15.12834

F-statistic 350.5209 Durbin-Watson stat 0.413633

Prob(F-statistic) 0.000000

A-3

Heteroskedasticity Test: White

F-statistic 1.419022 Prob. F(5,22) 0.2564

Obs*R-squared 6.828058 Prob. Chi-Square(5) 0.2337

Scaled explained SS 4.158603 Prob. Chi-Square(5) 0.5268

Test Equation:

Dependent Variable: RESID^2

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Date: 05/18/11 Time: 00:13

Sample: 1981 2008



C -27828.26 414547.4 -0.067129 0.9471

RD -0.042010 0.036311 -1.156948 0.2597

RD^2 -1.15E-09 7.30E-10 -1.577690 0.1289

RD*PINV 0.001877 0.001036 1.810939 0.0838

PINV 35997.54 24197.91 1.487630 0.1510

PINV^2 -785.8282 401.1181 -1.959094 0.0629




Sum squared resid 9.15E+11 Schwarz criterion 27.76141




A-4

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A-5

A-6

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A-7

F-statistic 3.446714 Prob. F(1,24) 0.0757

Log likelihood ratio 3.757393 Prob. Chi-Square(1) 0.0526

Test Equation:Dependent Variable: GDP


Date: 05/18/11 Time: 00:17

Sample: 1981 2008



C -1778.274 667.2721 -2.664992 0.0135

RD 2.49E-05 1.57E-05 1.585777 0.1259

PINV 30.96862 7.970334 3.885486 0.0007

FITTED^2 3.07E-05 1.66E-05 1.856533 0.0757




Sum squared resid 4116652. Schwarz criterion 15.21225




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REFERENCES

Damodar, N. Gujarati Basic Econometrics

Journal Of Yasar University,joy.yasar.edu.tr/ARTICLE/no20_vol5/1_SunaKorkmaz.pdf

(20.01.2011)

NSF, http://www.nsf.gov/statistics/nsf10305/content.cfm?pub_id=3966&id=2

BEA,http://www.bea.gov for Current-Dolar Real Gross Domestic Product in U..

OECD,http://www.oecd.org for GDP on R & D

http://research.stlouisfed.org/fred2/data/NDGI.txt for Federal Nondefense Gross Investment

Jones, CharlesIntroduction To Economic Growth, W. W. Norton & Company, New York

City

GROSSMAN, G.M. andHELPMAN, E. (1991), Innovation and Growth in the EconomyMIT Press.

SALA-I MARTIN, X.,(1990) ecture Notes on Economic Growth (II): Five PrototypeModels of Endogenous Growth,

Prof. Dr. A. Ayen KAYA, EGE UNIVERSITY,FACULTY OF ECONOMICS AND ADMINISTRATIVE

SCIENCES,Department of Economics [email protected]
http://www.bea.gov/http://www.bea.gov/http://www.bea.gov/http://www.oecd.org/http://www.oecd.org/http://www.oecd.org/http://research.stlouisfed.org/fred2/data/NDGI.txthttp://research.stlouisfed.org/fred2/data/NDGI.txthttp://research.stlouisfed.org/fred2/data/NDGI.txthttp://www.oecd.org/http://www.bea.gov/

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