Showing papers on "Multidimensional panel data published in 2005"
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TL;DR: In this paper, the authors explain the proliferation of panel data studies in terms of data availability, the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow, and challenging methodology.
Abstract: We explain the proliferation of panel data studies in terms of (i) data availability, (ii) the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow, and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.
143 citations
TL;DR: In this article, the two stage least squares estimator for the linear panel data model is shown to have different characterizations depending on the choice of instrument matrix and the issue of efficient estimation is also treated.
Abstract: The system two stage least squares estimator for the linear panel data model is shown to have different characterizations depending on the choice of instrument matrix. The more general estimator, where, in effect, separate reduced form linear projections are estimated for each time period, also has the advantage of being applicable when the number of instruments changes across time periods. The issue of efficient estimation is also treated.
44 citations
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TL;DR: In this paper, the authors present a general model assembling time-series, spatial cross-section and panel data econometrics within one framework, and derive the unconditional maximum likelihood function, present the conditions under which the model is stationary, and give an overview of parameter restrictions that lead to simpler models.
Abstract: One of the central research questions in modelling space-time data is the right econometric model. Three potential problems one must deal with are serial dependence between the observations on each spatial unit over time, spatial dependence between the observations on the spatial units at each point in time, and unobservable spatial and time period specific effects. As we have no a priori reasons to believe that one problem is more important than another, this paper presents a general model assembling time-series, spatial cross-section and panel data econometrics within one framework. We also express an economic explanation for this model, derive the unconditional maximum likelihood function, present the conditions under which the model is stationary, and give an overview of parameter restrictions that lead to simpler models. As an application, the relationship between the unemployment rate, the labor force participation rate and the employment growth rate is investigated based on spacetime data of 113 regions across 9 countries of the EU over the period 1989-1996.
15 citations
TL;DR: A modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times series and many missing data is proposed.
13 citations
TL;DR: This article proposed a model for panel no-cointegration using an unobserved common factor structure, following the work on Bai and Ng (2004) for panel unit roots, and examined the properties of residual-based tests for nocointegration applied to defactored data from which the common factors and individual components have been extracted.
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 e.g. been shown by Banerjee, Marcellino and Osbat (2004, 2005) via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit root test 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 work on Bai and Ng (2004) for panel unit roots. The model enables us to 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 study the asymptotic behavior of some existing, residual-based panel no-cointegration, as suggested by Kao (1999) and Pedroni (1999, 2004). Under the DGP used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than √ NT as for independent panels. We then examine the properties of residual-based tests for nocointegration applied to defactored data from which the common factors and individual components have been extracted.