We address the issue of estimation and inference in dependent non-stationary panels of small cross-section dimensions. The main conclusion is that the best results are obtained applying bootstrap inference to single-equation estimators, such as FM-OLS and DOLS. SUR estimators perform badly, or...

Several empirical studies are concerned with measuring the effect of currency and current account crises on economic growth. Using different empirical models this paper serves two aspects. It provides an explicit assessment of country specific factors influencing the costs of crises in terms of...

Simulation estimation in the context of panel data, limited dependent-variable (LDV) models poses formidable problems that are not present in the crosssection case. Nevertheless, a number of practical simulation estimation methods have been proposed and implemented for panel data LDV models....

This paper presents unit-root test results for real exchange rates in ten Central and Eastern European transition countries during 1993:01-2003:12. Because of the shift from controlled to market economies and the accompanying crises, failed policy regimes and changes in exchange rate regimes,...

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power...

In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni (1999, 2004). Results based on both simulated and real data suggest that different adjustment...

We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...

Recently, there has been a growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a "treated" unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the...

We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...

Recently, there has been a growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the...