A Monte Carlo comparison of panel unit root tests under factor structure

Lin, Jianhua; Hu, Yi; Wang, M. J.; Xia, X.H.

doi:10.1080/13504851.2012.695067

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Three main second generation panel unit root tests are proposed by Moon and Perron [38], Bai and Ng (hereafter, BN) [39] and Pesaran [40], respectively. According to the Monte Carlo comparisons by Lin et al [41], the tests proposed by Pesaran are relatively robust, so we choose them in our empirical study.…”

Section: Panel Unit Root Testsmentioning

confidence: 99%

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Guo

Wang

et al. 2015

Energies

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Abstract:In this article, the relationship between energy consumption and economic growth is examined from the viewpoint of China's industrial sectors. Panel data from 37 industrial sectors in China covering the period from 1998 to 2010 was used in this study. Not only first generation panel unit root tests and panel cointegration tests, but also second generation tests that account for dependence between cross-sectional units were employed. The empirical results reveal that both energy consumption and economic growth are integrated as order one, and they are cointegrated. Panel fully modified ordinary least squares estimators show that a 1% increase in energy consumption increases the real value added of industrial sectors by 0.871%, and a 1% increase in real value added of industrial sectors increases energy consumption by 1.103%. The panel vector error correction models for causality tests are estimated by a system generalized moment method. We find a unidirectional causal relation running from economic growth to energy consumption in the shortrun. In the long run, however, there is evidence of a unidirectional causality running from energy consumption to economic growth. OPEN ACCESSEnergies 2015, 8 9393

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Section: Panel Unit Root Testsmentioning

confidence: 99%

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Guo

Wang

et al. 2015

Energies

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“…We check the stationarity of the variables using nonstationary panel data methods [22][23][24][25][26][27]; in particular, according to [22,24], stationarity tests for series should be conducted before performing the panel regression; otherwise the regression may be spurious and this will result in misleading conclusions. And according to the Monte Carlo comparisons by [25,26], the second-generation panel unit root tests proposed by Bai and Ng [24] and Pesaran [28] are robust relatively, so we choose them in our empirical study. We found that both tests reject the null of unit root for the variables ℎ, , , V , , , , , 2 , 3 , , , and and are statistically significant at the 10% significance level, so we conclude that these variables are stationary.…”

Section: Analysis Of Empirical Study Resultsmentioning

confidence: 99%

Tax Arrangement and Regional Industrial Restructuring: Evidence from Panel Data in China

Zhang

Tao

et al. 2016

Mathematical Problems in Engineering

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Regional industrial restructuring has been one of the major items in the transformation of economic development mode. An exploration was made into the influence of tax arrangement on the regional industrial structure by setting up a panel data econometric model based on the evaluation and analysis of the regional industrial structure in China. It was shown that tax arrangement influenced the regional industrial restructuring in terms of three aspects. Microlevel: the turnover tax and income tax appeared with a U-path of influence on upgrading of the industrial structure while appearing with an inverted U-path of influence on rationalization of the industrial structure. In addition, the levy of resource tax had a negative impact on both upgrading and rationalization of the industrial structure. Mesolevel: taxation in the secondary and tertiary industries appeared with a U-path of influence on upgrading of the industrial structure. An increase of taxation in the secondary industry had a negative impact on rationalization of the industrial structure. The taxation in the tertiary industry appeared with an inverted U-path of influence on rationalization of the industrial structure. Macrolevel: the macrotax burden had a U-path of influence on both upgrading and rationalization of the industrial structure.

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“…This would also present a problem for the competing DE and AR(1) factor specific models. While much work has been done on the development of unit root tests for time series data (Im et al, 2003;Baltagi et al, 2007;Moon and Perron 2012;Westerlund and Larsson, 2012;Lin, 2013), to our knowledge, no tests have been developed for our context. That is, none are applicable to unbalanced, inconsistently-spaced multivariate repeated measures data modeled with a Kronecker product covariance structure.…”

Section: Data Applications and Resultsmentioning

confidence: 99%

Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures

et al. 2014

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Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure.

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A Monte Carlo comparison of panel unit root tests under factor structure

Cited by 6 publications

References 9 publications

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Tax Arrangement and Regional Industrial Restructuring: Evidence from Panel Data in China

Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures

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