Abstract not found

This paper continues the investigation of Giles and Williams (2000) on export-led growth (ELG). In the first part, we surveyed the empirical export-led growth literature; it was evident that Granger noncausality tests are commonly applied as a test for ELG. In this paper, we explore the sensitivity of the test for exclusions restrictions often used as the Granger noncauality test for ELG by reconsidering two applications: Oxley’s (1993) study for Portugal and Henriques and Sadorsky’s (1996) analysis for Canada. We focus on robustness to the method adopted to deal with nonstationarity, including the choice of deterministic trend degree. We show that different noncausality outcomes are easy to obtain, and consequently we recommend that readers interpret the empirical ELG literature with care. Our analysis also highlights the importance of examining the robustness of Granger noncauality test results to avoid spurious outcomes in applications.

This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, di Mauro, Pesaran, and Smith (2007), is used to generate out-of-sample one quarter and four quarters ahead forecasts of real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions made up of 33 countries covering about 90 % of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of economies considered — industrialised, emerging, and less developed countries — as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed the double-averaged GVAR forecasts perform better

Abstract: This paper analyses three Granger noncausality hypotheses within a conditionally Gaussian MS-VAR model. Noncausality in mean is based on Granger’s original concept for linear predictors by defining noncausality from the 1-step ahead forecast error variance for the conditional expectation. Noncausality in mean-variance concerns the conditional forecast error variance, while noncausality in distribution refers to the conditional distribution of the forecast errors. Necessary and sufficient parametric conditions for noncausality are presented for all hypotheses. As an illustration, the hypotheses are tested using monthly postwar U. S. data on money and income. We find that money is not Granger causal in mean for income, but Granger causal in mean-variance, i.e. there is unique information in money for predicting the next period regime and the regime affects the uncertainty about the income forecast.

This paper employs systems-based cointegration techniques developed by Johansen (1988, 1995) to determine which European Union countries would form a successful Economic and Monetary Union (EMU), based on long-run behavior of the nominal convergence criteria laid down in the Maastricht treaty. The original 12 European Union countries are analyzed together. Nominal exchange rates, real exchange rates, longterm interest rates, and government budget deficits are each analyzed for co-movements among the 12 countries and various subgroups of them. The results suggest that not all of the 12 original countries of the European Union could possibly form a successful EMU over time, unless several countries made significant adjustments.

Abstract not found

The purpose of this paper is to empirically analyse the revenue-expenditure models of public finance by considering the possibility of non-linear and asymmetric adjustment. A long-run relationship between general government expenditure and revenues is identified for Italy. Following system-wide shocks, the estimated relationship adjusts slowly to equilibrium, mainly due to complex administrative procedures that add to the sluggishness of tax collection and undermine the effective monitoring of public spending. Exogeneity of public expenditure implies that taxes rather than spending, carry the burden of short-run adjustment to correct budgetary disequilibria. Allowing for non-linear adjustment and the possibility of multiple equilibria, our findings show evidence of asymmetric adjustment around a unique equilibrium. In particular, we find that when government expenditure is too high, adjustment of taxes takes places at a faster rate than when it is too low. Further, there is evidence of a faster adjustment when deviations from the equilibrium level get larger, pointing to a Leviathan-style, revenue-maximiser government.

We propose new tests for panel cointegration by extending the panel unit root tests of Choi [2001] and Maddala and Wu [1999] to the panel cointegration case. The tests are flexible, intuitively appealing and relatively easy to compute. We investigate the finite sample behavior in a simulation study. Several variants of the tests compare favorably in terms of both size and power with other widely used panel cointegration tests.

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applying them, selected techniques are applied to a demand for money dataset. Comparisons of the results from this illustrative case study are presented, and conclusions are drawn that should aid practitioners in applied time-series econometrics.

On the exploration of causal relationships between energy and the economy