Estimating Average Economic Growth in Time Series Data with Persistency
AbstractThis paper studies estimation of deterministic trends in time series models with persistency. In particular, a joint estimation of the trend coefficient and the autoregressive parametere is proposed and asympototic analysis on the nonlinear estimator is provided. The joint estimator is compared with several conventional trend estimators. Monte Carlo experiments indicate that the proposed estimators have good finite sample performance. We use these procedures to estimate growth rates for real GNP and consumer price index in 40 countries.
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Bibliographic InfoPaper provided by University of Illinois at Urbana-Champaign, College of Business in its series Working Papers with number 03-0111.
Date of creation: 2003
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Web page: http://www.business.uiuc.edu/Working_Papers/Main.asp
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Other versions of this item:
- Xiao, Zhijie, 2004. "Estimating average economic growth in time series data with persistency," Journal of Macroeconomics, Elsevier, vol. 26(4), pages 699-724, December.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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