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Panel Unit Root (panel + unit_root)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting75%

Terms modified by Panel Unit Root

  • panel unit root test
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    Selected Abstracts

    Heterogeneity and cross section dependence in panel data models: theory and applications introduction

    JOURNAL OF APPLIED ECONOMETRICS, Issue 2 2007
    Badi H. Baltagi
    The papers included in this special issue are primarily concerned with the problem of cross section dependence and heterogeneity in the analysis of panel data models and their relevance in applied econometric research. Cross section dependence can arise due to spatial or spill over effects, or could be due to unobserved (or unobservable) common factors. Much of the recent research on non-stationary panel data have focussed on this problem. It was clear that the first generation panel unit root and cointegration tests developed in the 1990's, which assumed cross-sectional independence, are inadequate and could lead to significant size distortions in the presence of neglected cross-section dependence. Second generation panel unit root and cointegration tests that take account of possible cross-section dependence in the data have been developed, see the recent surveys by Choi (2006) and Breitung and Pesaran (2007). The papers by Baltagi, Bresson and Pirotte, Choi and Chue, Kapetanios, and Pesaran in this special issue are further contributions to this literature. The papers by Fachin, and Moon and Perron are empirical studies in this area. Controlling for heterogeneity has also been an important concern for empirical researchers with panel data methods promising better handle on heterogeneity than cross-section data methods. The papers by Hsiao, Shen, Wang and Weeks, Pedroni and Serlenga and Shin are empirical contributions to this area. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Evidence from panel unit root and cointegration tests that the Environmental Kuznets Curve does not exist

    AUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 3 2003
    Roger Perman
    The Environmental Kuznets Curve (EKC) hypothesis , an inverted U-shape relation between various indicators of environmental degradation and income per capita , has become one of the ,stylised facts' of environmental and resource economics. This is despite considerable criticism on both theoretical and empirical grounds. Cointegration analysis can be used to test the validity of such stylised facts when the data involved contain stochastic trends. In the present paper, we use cointegration analysis to test the EKC hypothesis using a panel dataset of sulfur emissions and GDP data for 74 countries over a span of 31 years. We find that the data is stochastically trending in the time-series dimension. Given this, and interpreting the EKC as a long run equilibrium relationship, support for the hypothesis requires that an appropriate model cointegrates and that sulfur emissions are a concave function of income. Individual and panel cointegration tests cast doubt on the general applicability of the hypothesised relationship. Even when we find cointegration, many of the relationships for individual countries are not concave. The results show that the EKC is a problematic concept, at least in the case of sulfur emissions. [source]

    Is There a Natural Rate of Crime?

    AMERICAN JOURNAL OF ECONOMICS AND SOCIOLOGY, Issue 2 2010
    Paresh Kumar Narayan
    Studies in the economics of crime literature have reached mixed conclusions on the deterrence hypothesis. One explanation that has been offered for the failure to find evidence of a deterrent effect in the long run is the natural rate of crime. This article applies univariate unit root tests to crime series for the United Kingdom and United States and panel unit roots to crime rates for a panel of G7 countries to examine whether there is a natural rate of crime. Our main finding is that when we allow for two structural breaks in the univariate unit root test and a structural break in the panel data unit root test, there is strong evidence of a natural rate of crime. The policy implications of our findings is that governments should focus on altering the economic and social structural profile that determines crime in the long run rather than increasing expenditure on law enforcement that will at best reduce crime rates in the short run. [source]

    Cointegration Testing in Panels with Common Factors,

    OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2006
    Christian Gengenbach
    Abstract Panel unit-root and no-cointegration tests that rely on cross-sectional independence of the panel unit experience severe size distortions when this assumption is violated, as has, for example, been shown by Banerjee, Marcellino and Osbat [Econometrics Journal (2004), Vol. 7, pp. 322,340; Empirical Economics (2005), Vol. 30, pp. 77,91] via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit-root tests using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel no-cointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the study by Bai and Ng [Econometrica (2004), Vol. 72, pp. 1127,1177] for panel unit roots. We distinguish two important cases: (i) the case when the non-stationarity in the data is driven by a reduced number of common stochastic trends, and (ii) the case where we have common and idiosyncratic stochastic trends present in the data. We discuss the homogeneity restrictions on the cointegrating vectors resulting from the presence of common factor cointegration. Furthermore, we study the asymptotic behaviour of some existing residual-based panel no-cointegration tests, as suggested by Kao [Journal of Econometrics (1999), Vol. 90, pp. 1,44] and Pedroni [Econometric Theory (2004a), Vol. 20, pp. 597,625]. Under the data-generating processes (DGP) used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than as for independent panels. We then examine the possibilities of testing for various forms of no-cointegration by extracting the common factors and individual components from the observed data directly and then testing for no-cointegration using residual-based panel tests applied to the defactored data. [source]