Testing for seasonal unit roots in heterogeneous panels in the presence of cross section dependence (original) (raw)
Related papers
2014
Finite T panel data unit root tests allowing for structural breaks, spatial cross section dependence, heteroscedasticity, serial correlation, heterogeneity and non-linear trends are proposed. The structural breaks can be at known or unknown dates. For the latter, analytic probability density functions of the asymptotic distributions of the tests are provided based on a minimum order statistic. The tests can accommodate general forms of spatial dependence for which the spatial weights matrix does not have to be de…ned due to the utilization of a non-parametric estimator. A set of su¢ cient conditions which determines admissible deterministic trend functions is also provided. Finally, extensive Monte Carlo experiments show the usefulness of the new tests.
Testing for seasonal unit roots in monthly panels of
2015
We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly CHEGY test to the monthly case. This parametric test is contrasted with a new nonparametric test, which is the panel counterpart to the univariate RURS test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data.
Testing for unit roots in heterogeneous panels
Journal of Econometrics, 2003
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
Testing for Seasonal Unit Roots in Monthly Panels of Time Series*
Oxford Bulletin of Economics and Statistics, 2011
We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly CHEGY test to the monthly case. This parametric test is contrasted with a new nonparametric test, which is the panel counterpart to the univariate RURS test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data.
Testing for unit roots on heterogeneous panels: A sequential approach
2001
There is a growing trend of criticism against the use of panel data unit root test for assessing hypotheses such as the purchasing power parity. The usual argument of a gain in power with respect to univariate unit root tests is not relevant as di¤erent nulls are involved when testing on panel data.. In the context of a comparative, multicountry, study, inference based on individual unit root tests su¤ers mainly from a huge size distortion, even more than from low power.
Testing for Unit Roots in Small Panels with Short-run and Long-run Cross-sectional Dependencies 1
2015
International Economic Policy An IV approach, using as instruments nonlinear transformations of the lagged levels, is explored to test for unit roots in panels with general dependency and heterogeneity across cross-sectional units. We allow not only for the cross-sectional dependencies of innovations, but also for the presence of cointegration across cross-sectional levels. Unbalanced panels and panels with differing individual short-run dynamics and cross-sectionally related dy-namics are also permitted. We also more carefully formulate the unit root hypotheses in panels. In particular, using order statistics we make it possible to test for and against the presence of unit roots in some of the individual units for a given panel. The individual IV t-ratios, which are the bases of our tests, are asymptotically normally distributed and cross-sectionally independent. Therefore, the critical values of the order statistics as well as the usual average statistic can be easily obtained fro...
SEASONAL PANEL UNIT ROOT ESTIMATION: THEORY and INFERENCE
ekonometridernegi.org
Bu çalışmada HEGY yaklaşımının panel veriye genişletilmesi ile elde edilen EKK tabanlı bir panel mevsimsel birim kök testi önerilmektedir. Önerilen test, seride mevsimsel ve mevsimsel olmayan birim kökün varlığı altında türetilmiştir. Testin asimptotik özellikleri hem birleştirilmiş EKK hem de sabit etki modeli için incelenmiştir. Test istatistiğinin kritik değerleri ve testin gücü Monte Carlo teknikleri kullanılarak elde edilmiştir. Analizler heterojen veri üretim süreçlerine de genelleştirilmiş ve yatay kesit boyutunda ilişki varken kullanılabilecek ortalama Wald istatistiği önerilmiştir.
Testing for Unit Roots in Small Panels with Short-run and Long-run Cross-sectional Dependencies
Review of Economic Studies, 2009
An IV approach, using as instruments nonlinear transformations of the lagged levels, is explored to test for unit roots in panels with general dependency and heterogeneity across cross-sectional units. We allow not only for the cross-sectional dependencies of innovations, but also for the presence of cointegration across cross-sectional levels. Unbalanced panels and panels with differing individual short-run dynamics and cross-sectionally related dynamics are also permitted. We also more carefully formulate the unit root hypotheses in panels. In particular, using order statistics we make it possible to test for and against the presence of unit roots in some of the individual units for a given panel. The individual IV t-ratios, which are the bases of our tests, are asymptotically normally distributed and cross-sectionally independent. Therefore, the critical values of the order statistics as well as the usual average statistic can be easily obtained from simple elementary probability computations. We show via a set of simulations that our tests work well, while other existing tests fail to perform properly. As an illustration, we apply our tests to the panels of real exchange rates, and find no evidence for the purchasing power parity hypothesis, which is in sharp contrast with the previous studies.
Panel Unit Roots Tests for Cross' Sectionally Cor'related Panels: A Monte Carlo Comparison
2003
This paper deals with the finite sample performance of a set of unit root tests for cross correlated panels. As is well known, univariate tests are not powerful to reject the null of a unit root for the usual economic variables while panel tests, by exploiting the large number of cross-section units, provide a device to increase 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 (2002), Bai and Ng's (2003), Moon and Perron's (2003), and Phillips and Sul's (2003) tests are analyzed by a Monte Carlo simulation study. In synthesis, Moon and Perron's (2003) tests show good size and power for different values of T and N and model specifications. Focusing on Bai and Ng's (2003) procedure, the simulation study highlights first that the suggested ADF test for the nonstationary analysis of the common factor lack of power, and secondly the simulation shows that the pooled Dickey-Fuller-GLS test provides higher power than the pooled ADF test for the analysis of nonstationary properties of the idiosyncratic components. Choi's (2002) tests are strongly oversized when the common factor influences the cross-section units heterogeneously. Finally, all the tests lack power when a deterministic trend is included in the data generating process.