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The author would like to thank Benedict Clements, Jorg Decressin and Thomas Krueger for helpful comments.
In April 1974, a revolution replaced “The New State” with a democratic regime.
Other approaches include the estimation of cost functions, cross-sectional studies using country level data, and calibrated structural models. For example, see Gaspar and Pereira (1995) for an application of a computable general equilibrium model to assess the growth effects of EU-financed capital expenditures in Portugal.
A number of authors have employed Johansen’s procedure to estimate production functions (e.g., Batina, 1998; Flores de Frutos, Gracia-Diez, and Pérez-Amaral, 1998; Ghali, 1998; and Mamatzakis, 1999), but most earlier studies employ ordinary least squares. See below for a further discussion of cointegration issues.
Arrow and Kurz (1970) were one of the first to study theoretically the implications of incorporating public capital in a neoclassical growth model.
Some studies (e.g., Tatom, 1991) also include the relative price of energy in the equation to capture supply shocks but it is not immediately clear why any price variable should be included in a production function.
The time series were kindly provided by the Bank of Portugal.
Issues of cointegration and the econometric validity of the ordinary least squares results will be discussed below.
The unemployment rate is obtained from das Neves (1994) and the IMF’s World Economic Outlook database.
The results are obtained using PcGive Version 9 (Doornik and Hendry, 1997) and Eviews Version 3.1.
Munnell (1992) concludes that the estimated returns on public capital for the United States are too large to be credible but stresses that these results should not be discarded altogether, since evidence from cost-benefit studies of individual projects and cross-sectional studies indicates that investment in public infrastructure may have a large payoff.
The sum of the two coefficients for public capital is negative.
The dummy may also capture somewhat the negative output effect of higher oil prices after the oil shocks of 1973 and 1979.
Owing to its simplicity, the Engle-Granger method has been widely applied in the literature. Sturm and de Haan (1995) employ it to argue that Aschauer’s model should be estimated in first differences because their test results could not identify cointegration.
The standard critical values cannot be used for present purposes because they were applicable to the actual values of the variable being tested, whereas here, only estimated values of the relevant process are available. MacKinnon (1991) has derived relevant critical values for finite samples from Monte Carlo simulations, which are used in the present case.
In addition, the Engle-Granger procedure imposes an invalid common factor restriction on the dynamics by performing the test on a single equation.
The capital utilization rate, 1975–85 dummy, and interaction term are not included.
In the absence of cointegration, but with nonstationary variables, the literature recommends taking first differences of the variables to obtain stationary time series. Table 9 includes the estimation results in first differences and shows that the coefficient on public capital appears to be similar to that from the ordinary least squares equation, as long as no interaction dummy for the 1975-85 period is included. A notable drawback of first differencing is that it discards information on the long-term relationship between the variables.
The system of equations is estimated by the maximum likelihood technique.
The VAR includes a constant, but the 1975-85 dummy and the capital utilization rate are not included.
This strategy involves no costs in terms of the consistency of the estimators but some costs are incurred in terms of reduced efficiency of estimation.
The results are derived by running a bivariate regression of the relevant pair of variables in the group of variables. Each equation contains lagged values of the left-hand-side variable plus lagged values of the other variable under consideration. In essence, this is equivalent to running a two-variable VAR. The Granger analysis tests whether the lags of the latter are significantly different from zero.
These were computed using the option of analytic asymptotic standard errors in Eviews 3.1.
The same variable order as in the case of the impulse-response analysis is employed.