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- Westerlund, Joakim, et al.
(författare)
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New tools for understanding the local asymptotic power of panel unit root tests
- 2015
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Ingår i: Journal of Econometrics. - : Elsevier BV. - 0304-4076 .- 1872-6895. ; 188:1, s. 59-93
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Tidskriftsartikel (refereegranskat)abstract
- Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form rho(i)= 1 + ciN(-k)T(-tau), where i = 1,..., N indexes the cross-sectional units, T is the number of time periods and ci is a random local-to-unity parameter. A limit theory for the sample moments of such panel data is developed and is shown to involve infinite-order series expansions in the moments of ch in which existing theories can be seen as mere first-order approximations. The new theory is applied to study the asymptotic local power functions of some known test statistics for a unit root. These functions can be expressed in terms of the expansions in the moments of ci, and include existing local power functions as special cases. Monte Carlo evidence is provided to suggest that the new results go a long way toward bridging the gap between actual and predicted power.
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| 4. |
- Westerlund, Joakim
(författare)
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Essays on Panel Cointegration
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis develops new techniques for analyzing cointegrated relationships in panel data. The first chapter is introductory while the remaining six contain the main contributions. The second chapter is concerned with the estimation of a cointegrated panel relation with endogenous regressors, in which case the least squares estimator is biased unless it is conditioned on the lags and leads of the differences of the regressors. The problem is how to choose the appropriate number of lags and leads. This issue is illuminated by examining the performance of several information criteria that facilitate a data dependent choice. The Monte Carlo evidence suggests that the criterion with the best performance also leads to the best performing estimator. Although cointegration is usually considered to be the most natural choice of null hypothesis, most existing panel tests are based on the null hypothesis of no cointegration. The third chapter therefore develops a new test with cointegration as the null. Asymptotic properties of the test are derived and verified in small samples via Monte Carlo simulations, and implementation is illustrated through an application of the test to international R&D spillovers. Relationships that span extensive periods of time are prone to structural breaks. Yet, there is presently no test that is general enough to allow for such breaks. The fourth chapter takes a step in this general direction by proposing a test that allows for multiple structural breaks in the cointegration relation. Asymptotic distribution of the test is derived and critical values are provided to permit accurate testing even in small samples, which is verified using Monte Carlo simulations. An application of the test to the Feldstein-Horioka puzzle is also provided. Empirical evidence suggests that the Fisher hypothesis does not hold, which seems at odds with many theoretical models. The fifth chapter argues that these results can be attributed to the low power of conventional time series tests and that the use of panel data can generate more powerful tests. For this purpose, two panel cointegration tests are developed that allow for cross-sectional dependence, and are shown to be more powerful than existing tests. The empirical results suggest that, based on the new tests, the Fisher hypothesis cannot be rejected. In the sixth chapter, four new error correction based tests for the null hypothesis of no cointegration are proposed. These tests are less restrictive than most existing tests and are therefore more widely applicable, which implies that they are also expected to be more powerful. This is illustrated via simulations. The empirical application shows evidence of cointegration between health care expenditures and GDP. The seventh chapter develops two panel cointegration tests that allow for very general forms of serial correlation structures without the need for any kind of adjustment. This makes them very simple in comparison to existing tests, which do not share this invariance property. Asymptotic distributions are derived and Monte Carlo evidence suggests that the new tests compare favorably with several other popular tests.
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- Blomquist, Johan, et al.
(författare)
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Panel bootstrap tests of slope homogeneity
- 2016
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Ingår i: Empirical Economics. - : Springer Science and Business Media LLC. - 0377-7332 .- 1435-8921. ; 50:4, s. 1359-1381
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes two bootstrap-based tests that can be used to infer whether the individual slopes in a panel regression model are homogenous. The first test is suitable when wanting to infer the null of homogeneity versus the general alternative, while the second is suitable when wanting to infer the units of the panel that can be pooled. Both approaches are shown to be asymptotically valid, a property that is verified in small samples using Monte Carlo simulation.
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- De Vos, Ignace, et al.
(författare)
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On CCE estimation of factor-augmented models when regressors are not linear in the factors
- 2019
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Ingår i: Economics Letters. - : Elsevier BV. - 0165-1765. ; 178, s. 5-7
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Tidskriftsartikel (refereegranskat)abstract
- In empirical research it is often of interest to include non-linear functions of the explanatory variables, such as squares or interactions, in the specification. A popular technique to estimate such models in the presence of common factors is the Common Correlated Effects (CCE) methodology. However, this approach assumes that the regressors are linear in the factors, which is not the case if variables enter non-linearly. In this note we show how CCE should be implemented when some regressors violate the linear factor model assumption.
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| 7. |
- Westerlund, Joakim
(författare)
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An IV Test for a Unit Root in Generally Trending and Correlated Panels
- 2016
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Ingår i: Oxford Bulletin of Economics and Statistics. - : Wiley. - 0305-9049. ; 78:5, s. 752-764
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes an IV-based panel unit root test that is general enough to accommodate general error serial and cross-section dependence, and a potentially nonlinear deterministic trend function. These allowances make the new test one of the most general around. It is also very simple to implement. Indeed, the IV statistic is asymptotically invariant to not only to all nuisance parameters characterizing the dependence of the errors and the true trend function, but also the deterministic specification of the fitted test regression.
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| 8. |
- Westerlund, Joakim, et al.
(författare)
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Breaks in persistence in fixed-T panel data
- 2021
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Ingår i: Economics Letters. - : Elsevier BV. - 0165-1765. ; 205
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers an autoregressive panel data model in which the autoregressive coefficient has undergone a structural break. The object of interest is the unknown breakpoint. A least squares-based estimator is proposed that is shown to be consistent when only the number of cross-section units, N, is large and the number of time periods, T, is small, thereby enabling quick detection of the onset of a new regime.
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| 9. |
- Westerlund, Joakim, et al.
(författare)
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CCE in fixed-T panels
- 2019
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Ingår i: Journal of Applied Econometrics. - : Wiley. - 0883-7252 .- 1099-1255. ; 34:5, s. 746-761
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Tidskriftsartikel (refereegranskat)abstract
- The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor-augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross-section averages of the observables, and then applying ordinary least squares conditional on the first-step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross-section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large-T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.
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| 10. |
- Westerlund, Joakim, et al.
(författare)
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Interactive Effects Panel Data Models with General Factors and Regressors
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Ingår i: Econometric Theory. - 1469-4360.
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough, unbiasedness comes at no cost at all. The approach does not require any knowledge of how many factors there are, or whether they are deterministic or stochastic. The order of integration of the factors is also treated as unknown, as is the order of integration of the regressors, which means that there is no need to pre-test for unit roots, or to decide on which deterministic terms to include in the model.
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