Cointegration in panel data with breaks and cross-section dependence

The power of standard panel cointegration statistics may be affected by misspecification errors if proper account is not taken of the presence of structural breaks in the data. We propose modifications to allow for one structural break when testing the null hypothesis of no cointegration that retain good properties in terms of empirical size and power. Response surfaces to approximate the finite sample moments that are required to implement the statistics are provided. Since panel cointegration statistics rely on the assumption of cross-section independence, a generalisation of the tests to the common factor framework is carried out in order to allow for dependence among the units of the panel. JEL Classification: C12, C22common factors, cross-section dependence, Panel Cointegration, structural break

Cointegration in Panel Data with Breaks and Cross-Section Dependence

The power of standard panel cointegration statistics may be affected by misspecification errors if proper account is not taken of the presence of structural breaks in the data. We propose modifications to allow for one structural break when testing the null hypothesis of no cointegration that retain good properties in terms of empirical size and power. Response surfaces to approximate the finite sample moments that are required to implement the statistics are provided. Since panel cointegration statistics rely on the assumption of cross-section independence, a generalisation of the tests to the common factor framework is carried out in order to allow for dependence among the units of the panel.Panel cointegration, structural break, common factors, cross-section dependence

Testing the Null of Cointegration with Structural Breaks

In this paper we propose an LM-Type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break, in both the deterministic and the cointegration vector. Our proposal focuses on the presence of endogenous regressors and analyses which estimation method provides better results. The test has been designed to be used as a complement to the usual non-cointegration tests in order to obtain stronger evidence of cointegration. We consider the cases of known and unknown break date. In the latter case, we show that minimizing the SSR results in a super-consistent estimator of the break fraction. Finally, the behaviour of the tests is studied through Monte Carlo experiments.cointegration, strcutural breaks, KPSS test.

The KPSS Test with Two Structural Breaks

In this paper we generalize the KPSS-type test to allow for two structural breaks. Seven models have been de?ned depending on the way that the structural breaks a¤ect the time series behaviour. The paper derives the limit distribution of the test both under the null and the alternative hypotheses and conducts a set of simulation experiments to analyse the performance in finite samples.Stationary tests, structural breaks, unit root.

Multicointegration, polynomial cointegration and I(2) cointegration with structural breaks. An application to the sustainability of the US external deficit.

In this paper we model the multicointegration relation, allowing for one structural break. Since multicointegration is a particular case of polynomial or I(2) cointegration, our proposal can also be applied in these cases. The paper proposes the use of a residualbased Dickey-Fuller class of statistic that accounts for one known or unknown structural break. Finite sample performance of the proposed statistic is investigated by using Monte Carlo simulations, which reveals that the statistic shows good properties in terms of empirical size and power. We complete the study with an empirical application of the sustainability of the US external deficit. Contrary to existing evidence, the consideration of one structural break leads to conclude in favour of the sustainability of the US external deficit.I(2) processes, multicointegration, polynomial cointegration, structural break, sustainability of external deficit.

Another Look at the Null of Stationary RealExchange Rates. Panel Data with Structural Breaks and Cross-section Dependence

This paper re-examines the null of stationary of real exchange rate for a panel of seventeen OECD developed countries during the post-Bretton Woods era. Our analysis simultaneously considers both the presence of cross-section dependence and multiple structural breaks that have not received much attention in previous panel methods of long-run PPP. Empirical results indicate that there is little evidence in favor of PPP hypothesis when the analysis does not account for structural breaks. This conclusion is reversed when structural breaks are considered in computation of the panel statistics. We also compute point estimates of half-life separately for idiosyncratic and common factor components and find that it is always below one year.Purchasing power parity, Half-lives, Panel unit roottests, Multiple structural breaks, Cross-section dependence.

Panel Data Stochastic Convergence Analysis of the Mexican Regions

The stochastic convergence amongst Mexican Federal entities is analyzed in panel data framework. The joint consideration of cross-section dependence and multiple structural breaks is required to ensure that the statistical inference is based on statistics with good statistical properties. Once these features are accounted for, evidence in favour of stochastic convergence is found. Since stochastic convergence is a necessary, yet insufficient condition for convergence as predicted by economic growth models, the paper also investigates whether beta-convergence process has taken place. We found that the Mexican states have followed either heterogeneous convergence patterns or divergence process throughout the analyzed period.Stochastic convergence, beta-convergence, non-stationarity panel data tests, cross-section dependence, multiple structural breaks.

Stochastic Convergence amongst Mexican States

In this paper we investigate the convergence process experienced by the Mexican states covering the period 1940-2001. Our analysis indicates that misleading conclusions can be obtained if the presence of structural breaks is not taken into account when testing for the presence of (stochastic) convergence. Thus, after allowing for structural breaks evidence in favour of convergence, in terms of real per capita GDP, is found both using unit root and cointegration tests. Empirical evidence shows that economic convergence has changed along time with mixed effects, although changes were toward convergence in majority of cases, consistent with stochastic convergence

Panel data cointegration testing with structural instabilities

Spurious regression analysis in panel data when the time series are cross-section dependent is analysed in the paper. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings

Panel data cointegration testing with structural instabilities

Spurious regression analysis in panel data when the time series are cross-section dependent is analysed in the paper. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings