goverment deficit impact on the stock price by blue123






                                     (1990 – 2006).

                             Miltiades N Georgiou PhD


In the present paper author will try to point out that government debt as a percentage
of GDP has a negative impact on stock prices in the long run, while entrepreneurial
caliber has a positive effect on stock prices in the long run. Data are annual and cover
Western European economies for the period 1990 – 2006. The elaboration of data is
made feasible through the Eviews software package. This paper is an extension of
author’s previous works.

Keywords: Consumption, Economic growth, Taxation, Econometric Model with
Panel data (single equation), Government debt, Interest rates, Stock market,
JEL classification: E21, F43, H2, C23, E62, G12, L26.

Dr. M. N. Georgiou has an M.Sc. (Economics) from Stirling University, and a Ph.D.
(Economics) from the University of Thessaly in Greece. He left in July 2008
Emporiki Bank as a Department Head on Market Analysis. He has contributed papers
to: “Hellenic Bank Association”, “Economic Review of Commercial Bank of
Greece”, “Applied Research Review”, “Applied Financial Economics Letters”,
“Max Planck Institute of Economics” (4) and “Social Science Research Network
(SSRN)” (30). He is familiar with Eviews software package

Author confirms that this article has never been published by any other journal.
Further, this article expresses only author’s personal ideas and of nobody else.

              Electronic copy available at:


        According to Georgiou (2009a) short term interest rates have a negative

impact on stock prices. It is also claimed by (Afonso and Sousa, 2009) that fiscal

deficit (through government spending) has a negative impact on stock prices, since

fiscal deficit tends to increase interest rates. In the present paper it will be shown with

a panel data single equation model that government debt as a percentage of GDP has

in the long run a negative impact on stock prices. This topic is nowadays the main

preoccupation of governments that face a debt exceeding a certain percentage of their


        In fact, government debt is an obstacle to economic growth (Georgiou, 2009c)

as well as to entrepreneurship (Georgiou, 2009d). Further, since entrepreneurship has

a positive impact on stock prices (Georgiou, 2009b), it becomes apparent that

government debt will yield a lower stock prices.


Hence our model is shown in equation (1).

Stock1it = c0 + c1 rEM it + c2 debtgdp it + error it                       (1)

ex-ante c1 > 0 and c2 < 0.

The variable Stock1 is average annual country share prices index (1995 = 100).

Variable rEM expresses the country average annual entrepreneurial remuneration

growth rate. Finally, variable debtgdp stands for the government debt as a percentage

of GDP. Source of all data is Eurostat except for rEM (IMF). The subscripts (i) and (t)

stand for the country and year respectively. Data are annual and cover in alphabetical

order the following countries: Austria (2003 – 2006), Belgium (1990 – 2006),

               Electronic copy available at:

Denmark (1990 – 2002), Finland (2001 – 2006), France (1990 – 2006), Germany

(1991 – 2006), Greece (2001 – 2006), Ireland (1990 – 2005), Italy (1995 – 2006),

Netherlands (1990 – 2000), Portugal (2001 – 2006), Spain (1990 – 2006) and UK

(1990 – 2006). Thus, the sample has 158 observations in total. It should be noted that

all afore-mentioned countries do not cover all period (1990 – 2006), for author

excluded some outliers in order to make the model robust. The equation (1) will be

estimated through the software package Eviews using the method of GLS period

SUR weights. The detailed results are shown in table 1, while the diagnostics (based

on Halkos 2003) in table 2.

             For Equation (1) there are basically two types of model, the fixed and random

effects. The appropriate choice depends on whether one treats αi’s as some fixed

numbers or ‘random drawings’ from a specific distribution. As the correlation

structure of the error term is ignored, a more efficient estimation method would be the

Generalized Least Squares (GLS) provided that there is no correlation between the x’s

and the α’s. GLS requires weighting the observations of y and x by Σ –(1/2):

                 1       1 − ϑ ΄ 
    −1 / 2
             =     IT − 
                          T ii 
                                 
where θ =
                   σ 2 + Tσ α

First one obtains an estimate θ by estimating the equation:

yit − yi. = β ΄ ( xit − xi. ) + (uit − ui. )                    (2)

Once the component variances have been estimated, one forms an estimator of the

composite residual covariance and GLS transforms the dependent and regressor data

(Baltagi, 2001; Davis, 2002). We observe that the estimated equation (1) meets the

three required criteria of homoskedasticity, specification and normality. Further, there

is no serial correlation. Hence, the above model is robust. The constant term is

positive and statistically significant. The coefficient of          rEM is positive and

statistically significant. Further, the coefficient of debtgdp is negative and statistically



        It is pointed out in the present paper that government debt has a negative

impact on stock prices. On the contrary, it is shown that entrepreneurial caliber has a

positive impact on stock prices. Thus, policy makers in order to reduce debt should

better not resort to taxation, since taxation is not only an obstacle to economic growth

(Georgiou, 2010), but also an obstacle to entrepreneurship (from which economies

expect a rise in stock prices). Thus, any additional taxation will end up with lower

stock prices.


     1.      Afonso, A. and. Sousa, R. M. 2009. The Macroeconomic Effects of

  Fiscal Policy. European Central Bank. Working Paper No.991. Available at:

     2.      Baltagi, B. H., 2001. Econometric Analysis of Panel Data, 2nd edn,

  John Wiley and Sons, Chichester.

     3.      Davis, P., 2002. Estimating multi-way error components models with

  unbalanced data structures. Journal of Econometrics, 106, 67–95.

     4.      Georgiou, M. N. 2009a. Short term interest rates have a negative

  impact on Stock Returns: A Panel data Analysis for many European economies.

  Social     Science     Research      Network      (SSRN).          Available   at:

     5.      Georgiou, M. N. 2009b. Entrepreneurship and Stock Price Index.

  Social     Science     Research      Network      (SSRN).          Available   at:

     6.      Georgiou, M. N. 2009c. Government Debt and Economic Growth. A

  Panel Data Analysis for Western Europe (1988 – 2006). Social Science Research

  Network                  (SSRN).                   Available                   at:

     7.      Georgiou, M. N. 2009d. Government debt impacts on interest rates as

  well as entrepreneurship. A panel data analysis for Western Europe, Japan and the

  United States (1990 – 2006). Social Science Research Network (SSRN). Available


     8.      Georgiou, M. N. 2010. Taxation is an Obstacle to Economic Growth.
  An Empirical Analysis for Western Europe and Japan (1999 – 2007). Social

Science       Research       Network         (SSRN).         Available   at:
   9.     Halkos, G. E., 2003. Environmental Kuznets Curve for Sulphur:

Evidence Using GMM Estimation and Random Coefficient Panel Data Models.

Environment and Development Economics 8: 581-601.


Table 1. Results

Method                                              GLS Period SUR weights

c0                                                                                 295,801
c1                                                                               1.064,050
c2                                                                                   -0,469
Adjusted R                                                                            0,546
Durbin-Watson                                                                         1,839
     1. For n = 158 (at 99%) dU = 1,656.
     2. As for the above mentioned term “SUR”, it does not refer at all to the term
        “seemingly unrelated regressions”, but according to the manual of EVIEWS the term
        “SUR” is used only because it applies for the covariance estimation the same formula
        as for the estimation of “seemingly unrelated regressions”.

Table 2. Diagnostic tests1

TESTS                                             GLS Period SUR               Critical values
                                                     weights                       (at 99%)
Heteroskedasticity                                    0,489                          4,605
Heteroskedasticity                                    1,447                          4,605
Heteroskedasticity                                    0,969                          6,340
Heteroskedasticity                                    1,064                          9,210
Heteroskedasticity                                    7,198                         11,350
RESET1                                                1,831                          6,340
RESET2                                                   1,572                       9,210
RESET3                                                   1,273                      11,350
Normality                                                7,820                       9,210

Test 1: Regression of the squared residuals on X. That is, u 2 = x′t γ1 + v t,1

Test 2: Regression of absolute residuals on X. That is, | u t |= x′ γ 2 + v t,2 (a Glejser test)

Test 3: Regression of the squared residuals on Y

                                               ˆ     ˆ
Test 4: Regression of the squared residuals on Y and Y 2

Test 5: Regression of the log of squared residuals on X (a Harvey test)

Test 6: Regression of residuals on Y 2

Test 7: Regression of residuals on Y 3

Test 8: Regression of residuals on Y 4

Test 9: Normality test (Jarque Bera)

    The diagnostic tests are based on Halkos (2003)

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