Another Look at what to do with Time-series Cross-section Data
AbstractOur study revisits Beck and Katz' (1995) comparison of the Parks and PCSE estimators using time-series, cross-sectional data (TSCS). Our innovation is that we construct simulated statistical environments that are designed to approximate actual TSCS data. We pattern our statistical environments after income and tax data on U.S. states from 1960-1999. While PCSE generally does a better job than Parks in estimating standard errors/confidence intervals, it too can be unreliable, sometimes producing standard errors/confidence intervals that are substantially off the mark. Further, we find that the benefits of PCSE can come at a large cost in estimator efficiency.
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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 06/04.
Length: 32 pages
Date of creation: 31 Mar 2006
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Panel data; Parks model; PCSE estimator; Monte Carlo methods;
Other versions of this item:
- Xiujian Chen & Shu Lin & W. Robert Reed, 2005. "Another Look At What To Do With Time-Series Cross-Section Data," Econometrics 0506004, EconWPA.
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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