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

Karavias, Yiannis; Tzavalis, Elias

doi:10.1111/jtsa.12144

Cited by 16 publications

(29 citation statements)

References 33 publications

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“…The superscript denotes dependence on the trend function through the location of the structural breaks and H denotes dependence on the hypothesis being tested (H 1 or H 2 ). If structural breaks do not occur, then p ( ,H) max no longer depends on and H, as in Karavias and Tzavalis (2016). This condition provides the necessary moments for the estimation of Π N .…”

Section: Assumptionsmentioning

confidence: 96%

“…The first panel data unit root test was applied on disaggregated wage data with a small time dimension (see Breitung & Meyer, ). Other finite T panel unit root tests have been proposed by Harris and Tzavalis (), Kruiniger (), Han and Phillips (), De Blander and Dhaene (), Karavias and Tzavalis (), Choi (), Karavias and Tzavalis (), and Robertson, Sarafidis, and Westerlund () inter alia. Testing the unit root hypothesis in this type of data has attracted considerable attention, but existing tests rely on restrictive assumptions on the model specification.…”

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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Section: Assumptionsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Generalized fixed‐T panel unit root tests

Karavias

Tzavalis

2019

Scandinavian J Statistics

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“…In the absence of stationarity idiosyncratic region‐specific shocks would trigger divergence. Unit root tests are particularly useful in this respect (Harris & Tzavalis ; Karavias & Tzavalis ). The results of the Harris‐Tzavalis test (Table ) clearly indicate that the individual series of the panel reject the null hypothesis of unit roots in their level (implying divergence) against the alternative of stationarity around deterministic trends.…”

Section: Intangibles and Regional Convergence In Great Britainmentioning

confidence: 99%

The Spatial Footprint of the Knowledge Economy: The Role of Intangible Investment in Shaping Regional Inequalities in Great Britain

Melachroinos

Spence

2019

Tijd voor Econ & Soc Geog

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Since the advent of the knowledge economy it has become evident that the territorial footprint of investment in intangible assets is largely asymmetrical. First, these types of assets tend to be distributed unevenly across space. Second, intangible assets are an important source of productivity growth and competitiveness. Although significant advances have been made in measuring intangible assets and accounting for their effects in economic outcomes, their exact nature remains vague. Within the national accounts framework the majority of intangibles are, even now, still treated as intermediate expenditure. Consequently, intangibles are largely excluded from conventional measures of output and investment, making it difficult to account for their spatial effects. The present paper adjusts gross value added (GVA) data for NUTS 3 regions in Great Britain for intangibles. The adjusted series are then used to investigate trends in regional inequalities in GVA per employee in this country during the pre-recession period 1995-2007.

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“…The pooled estimator suffers from the incidental parameters problem of Neyman and Scott (1948) due to the presence of the predetermined regressors, see, e.g. Nickell (1981) and Karavias and Tzavalis (2016). This result implies that standard tests for pooled estimators do not control size asymptotically, unless N << T .…”

Section: Introductionmentioning

confidence: 99%

A homogeneous approach to testing for Granger non-causality in heterogeneous panels

2020

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This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a $$\sqrt{NT}$$ NT convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency.

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

Cited by 16 publications

References 33 publications

Generalized fixed‐T panel unit root tests

Generalized fixed‐T panel unit root tests

The Spatial Footprint of the Knowledge Economy: The Role of Intangible Investment in Shaping Regional Inequalities in Great Britain

A homogeneous approach to testing for Granger non-causality in heterogeneous panels

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