Testing for unit roots in short panels allowing for a structural break

Karavias, Yiannis; Tzavalis, Elias

doi:10.1016/j.csda.2012.10.014

Cited by 126 publications

(45 citation statements)

References 36 publications

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“…Therefore, the higher the variance ofˇi the greater the denominator of k WGT 235 and, hence, the lesser the power of the test. However, we do not provide simulations forˇi different than zero, here, as their impact on power is minimal (see also Karavias and Tzavalis (2014b)). Additional simulations (not reported here) have shown that the results are similar for non-zero values of these parameters even for N as small as 50.…”

Section: Simulation Resultsmentioning

confidence: 98%

Local Power of Fixed‐T Panel Unit Root Tests With Serially Correlated Errors and Incidental Trends

Karavias

Tzavalis

2015

Journal Time Series Analysis

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The asymptotic local power properties of various fixed T panel unit root tests with serially correlated errors and incidental trends are studied. Asymptotic (over N) local power functions are analytically derived, and through them, the effects of general forms of serial correlation are examined. We find that a test based on an instrumental variables (IV) estimator dominates the tests based on the within‐groups (WG) estimator. These functions also show that in the presence of incidental trends, an instrumental variables test based on the first differences of the model has non‐trivial local power in an N−1/2 neighbourhood of unity. Furthermore, for a test based on the within‐groups estimator, although it is found that it has trivial power in the presence of incidental trends, this ceases to be the case if there is serial correlation as well.

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Section: Simulation Resultsmentioning

confidence: 98%

Local Power of Fixed‐T Panel Unit Root Tests With Serially Correlated Errors and Incidental Trends

Karavias

Tzavalis

2015

Journal Time Series Analysis

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“…Furthermore, while heteroscedasticity and autocorrelation consistent estimators estimators are known to be biased and have issues with their performance (see, e.g., Kiefer, Vogelsang, & Bunzel, 2000, and the remark below in Moon & Perron, 2004, Theorem 2), our method results in excellent size control, as will be shown later, because of the way we use the cross-section dimension. In the fixed-T literature, Karavias and Tzavalis (2014b) allow for AR(2) errors with a single break. However, that method cannot be further extended to allow for more breaks, trends, or heteroscedasticity.…”

Section: Assumptionsmentioning

confidence: 99%

“…Note that̂( ) is only adjusted for the bias of its numerator (Kruiniger & Tzavalis, 2002;Phillips & Hansen, 1990). This method is different from that in Karavias and Tzavalis (2014b) and is much more flexible.…”

Section: Asymptotic Biasmentioning

confidence: 99%

“…This happens because T is considered finite; cross‐section information must be exploited to bypass time series econometric problems such as nonstationarity, structural change, and serial correlation in the errors; and it is not always clear how this can be done. Papers in the literature that consider fixed‐ T panel unit root testing with structural breaks are those of Hadri, Larsson, and Rao (), Karavias and Tzavalis (), and Karavias and Tzavalis (). The first two papers allow for a single structural break in the intercept and the linear trend of the model, whereas Karavias and Tzavalis () derive the local power functions of tests with one known structural break.…”

Section: Introductionmentioning

confidence: 99%

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Generalized fixed‐T panel unit root tests

Karavias

Tzavalis

2019

Scandinavian J Statistics

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Panel data unit root tests, which can be applied to data that do not have many time series observations, are based on very restrictive error and deterministic component specification assumptions. In this paper, we develop a new, doubly modified estimator, based on which we propose a panel unit root test that allows for multiple structural breaks, linear and nonlinear trends, heteroscedasticity, serial correlation, and error cross‐section heterogeneity, when the number of time series observations is finite. The test has the additional perk that it is invariant to the initial condition.

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“…Their test requires a trend variable in the model and cannot be used for models with only fixed effects. Karavias and Tzavalis (2014a,b) assume a spatial dependence structure for errors to deal with cross‐sectional dependency of micro units. However the variance–covariance matrix of the limiting distribution of their estimator cannot be estimated consistently, which requires using bootstrapping.…”

Section: Introductionmentioning

confidence: 99%

Unit Root Tests for Dependent Micropanels

Choi

2017

Jpn Econ Rev

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This paper proposes a new test for the null hypothesis of panel unit roots for micropanels with short time dimensions (T) and large cross‐sections (N). There are several distinctive features of this test. First, the test is based on a panel AR(1) model allowing for cross‐sectional dependency, which is introduced by a factor structure of the initial condition. Second, the test employs the panel AR(1) model with AR(1) coefficients that are heterogeneous for finite N. Third, the test can be used both for the alternative hypothesis of stationarity and for that of explosive roots. Fourth, the test does not use the AR(1) coefficient estimator. The effectiveness of the test rests on the fact that the initial condition has permanent effects on the trajectory of a time series in the presence of a unit root. To measure the effects of the initial condition, the present paper employs cross‐sectional regressions using the first time‐series observations as a regressor and the last as a dependent variable. If there is a unit root in every individual time series, the coefficient of the regressor is equal to one. The t‐ratios for the coefficient are this paper's test statistics and have a standard normal distribution in the limit. The t‐ratios are based on the OLS estimator and the instrumental variables estimator that uses reshuffled regressors as instruments. The test proposed in this paper makes it possible to test for a unit root even at T = 2 as long as N is large. Simulation results show that test statistics have reasonable empirical size and power. The test is applied to college graduates' monthly real wage in South Korea. The number of time‐series observations for this data is only two. The null hypothesis of a unit root is rejected against the alternative of stationarity.

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Testing for unit roots in short panels allowing for a structural break

Cited by 126 publications

References 36 publications

Local Power of Fixed‐T Panel Unit Root Tests With Serially Correlated Errors and Incidental Trends

Local Power of Fixed‐T Panel Unit Root Tests With Serially Correlated Errors and Incidental Trends

Generalized fixed‐T panel unit root tests

Unit Root Tests for Dependent Micropanels

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