We consider the problem of testing for a structural break in the spatial lag parameter in a panel model (spatial autoregressive). We propose a likelihood ratio test of the null hypothesis of no break against the alternative hypothesis of a single break. The limiting distribution of the test is...

In the first half of the paper we study spurious regressions in panel data when the cross-section and time-series dimensions are comparable. Asymptotic properties of the least-squares dummy variable (LSDV) estimator and other conventional statistics are examined. We show that the LSDV...

This paper surveys recent developments and provides Monte Carlo comparison on various tests proposed for cointegration in panel data. In particular, tests for two panel models, varying intercepts and varying slopes and varying intercepts and common slopes, are presented from the literature with...

This paper proposes a residual-based Lagrange Multiplier (LM) test for the null of cointegration in panel data. The test is analogous to the locally best unbiased invariant (LBUI) for a moving average (MA) unit root. The asymptotic distribution of the test is derived under the null. Monte Carlo...

This paper enlarges on Karlsoon and Löthgreen’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...

In this paper, we study the limiting distributions for the ordinary least squares (OLS), the fixed effects (FE), first difference (FD), and the generalized least squares (GLS) estimators in a linear time trend regression with a one-way error component model in the presence of serially correlated...

In the paper we extend Gregory and Hansen’s (1996)ADF, Za, Zt cointegration tests to panel data, using the method proposed in Maddala and Wu (1999). We test the null hypothesis of no cointegration for all the units in the panel against the alternative hypothesis of cointegration, while...

The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek...

The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is fixed. We...

This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of...