Abstract
In this paper, we propose an estimation and testing framework for parameter instability in cointegrated panel regressions with common and idiosyncratic trends. We develop tests for structural change for the slope parameters under the null hypothesis of no structural break against the alternative hypothesis of (at least) one common change point, which is possibly unknown. The limiting distributions of the proposed test statistics are derived. Monte Carlo simulations examine size and power of the proposed tests.
Highlights
Estimation and testing for structural changes is an important research topic in time series econometrics
The tests are for the null hypothesis of no structural break against the alternative hypothesis of one common change point which is possibly unknown
The asymptotic theory for ^ derived in the Section 3 is used to derive the limiting distribution for the Waldtype statistic under the null hypothesis of no structural change
Summary
Estimation and testing for structural changes is an important research topic in time series econometrics. This paper lls the gap in the literature by proposing an estimation and testing framework for parameter instability in cointegrated panel regression. We derive tests for structural change for the slope parameters in panel cointegration models with cross-sectional dependence that is captured by the common stochastic trends. We develop an asymptotic theory for the estimates of the parameters in the model We consider both the case of observed and unobserved common shocks. Ordinary large panels asymptotic theory (Phillips and Moon, 1999; Kao, 1999) cannot be applied in our framework due to the strong cross-sectional dependence introduced by the common shocks. Along similar lines as Andrews (1993), we derive the limiting distribution of a Wald-type test for the null hypothesis of no structural change at an unknown point in cointegrated panels where units are cross dependent.
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