Luciano Gutierrez

On the power of panel cointegration tests: a Monte Carlo comparison

This paper enlarges on Karlsoon and Lothgreen’s (2000) results on panel unit root tests to panel cointegration tests. We show that for homogeneous panel, Kao’s (1999) tests have higher (lower) power than Pedroni’s (1999) tests when a small-T (high-T) number of observations are included in the panel and both tests show better performance than Larsson et al. (2001) test. In addition, depending on the T-dimension of the panel, cointegration tests can have high power when a small or high fraction of the relationships are cointegrated. This result suggests that when rejecting the null hypothesis of no cointegration for the whole panel not all the relationships can be really cointegrated.

Panel Unit‐root Tests for Cross‐sectionally Correlated Panels: A Monte Carlo Comparison*

This paper deals with the finite-sample performance of a set of unit-root tests for cross-correlated panels. Most of the available macroeconomic time series cover short time periods. The lack of information, in terms of time observations, implies that univariate tests are not powerful enough to reject the null of a unit-root while panel tests, by exploiting the large number of cross-sectional units, have been shown to be a promising way of increasing the power of unit-root tests. We investigate the finite sample properties of recently proposed panel unit-root tests for cross-sectionally correlated panels. Specifically, the size and power of Choi’s [Econometric Theory and Practice: Frontiers of Analysis and Applied Research: Essays in Honor of Peter C. B. Phillips, Cambridge University Press, Cambridge (2001)], Bai and Ng’s [Econometrica (2004), Vol. 72, p. 1127], Moon and Perron’s [Journal of Econometrics (2004), Vol. 122, p. 81], and Phillips and Sul’s [Econometrics Journal (2003), Vol. 6, p. 217] tests are analysed by a Monte Carlo simulation study. In synthesis, Moon and Perron’s tests show good size and power for different values of T and N, and model specifications. Focusing on Bai

A Global Vector Autoregression Model for the Analysis of Wheat Export Prices

Food commodity price fluctuations have an important impact on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, and there is an urgent need for appropriate policy responses. Perhaps new approaches are needed to better understand international spill-overs, the feedback between the real and the financial sectors, as well as the link between food and energy prices. In this article, we present the results from a new worldwide dynamic model that provides the short and long-run impulse responses of the international wheat price to various real and financial shocks.

Simple tests for cointegration in panels with structural breaks

In the article we propose new panel cointegration tests which allow for structural breaks. We show that the panel tests have good size and power. We apply the test statistics to the analysis of the Feldstein–Horioka puzzle for a sample of 16 OCDE countries. After allowing for breaks, we find strong evidence that investment and saving rates are cointegrated.

The Behavior of Relative Food Prices: An Analysis across the European Countries

We analyze the behavior of relative food prices for a set of 24 European countries observed during the period January 1996–December 2007. We use recent methods for the analysis of nonstationary panels to show that food price dynamics can be decomposed into a common component and an idiosyncratic component. From this decomposition we compute and analyze the real exchange rates for a set of food products. We find that countries included in the euro area are more market integrated; that is, real exchange rates tend to converge, than countries that did not adopt euro.

Convergence in the agricultural incomes: a comparison between the US and EU

In this paper we compare the changes in farm incomes in EU regions and US States between 1989 and 2002. The aim of this comparative analysis is highlight the patterns of convergence or divergence and how they d iffer over time. We use two recent analytical instruments: non-stationary panel analysis and dynamic distribution analysis. Both tools overcome the problems involved in using standard cross-section analysis. The results of the non-stationary panel analysis show that the EU regions are converging, and that family farm income is converging faster than net added value. In the US states the analysis shows that substantial differences in farm income persist, and there are no evident signs of convergence.