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 InfoArticle provided by Elsevier in its journal Journal of Macroeconomics.
Volume (Year): 26 (2004)
Issue (Month): 4 (December)
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Web page: http://www.elsevier.com/locate/inca/622617
Other versions of this item:
- Xiao, Qifang & Xiao, Zhijie, 2003. "Estimating Average Economic Growth in Time Series Data with Persistency," Working Papers 03-0111, University of Illinois at Urbana-Champaign, College of Business.
- 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; Spatio-temporal Models
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- Granger, E.J. & Swanson, N.R., 1996.
"An introduction to stochastic Unit Root Processes,"
4-96-3, Pennsylvania State - Department of Economics.
- Xiao, Zhijie & Phillips, Peter C.B., 1999. "Efficient Detrending In Cointegrating Regression," Econometric Theory, Cambridge University Press, vol. 15(04), pages 519-548, August.
- Phillips, Peter C.B., 1995.
"Robust Nonstationary Regression,"
Cambridge University Press, vol. 11(05), pages 912-951, October.
- Lucas, Andre, 1995. "An outlier robust unit root test with an application to the extended Nelson-Plosser data," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 153-173.
- Eugene Canjels & Mark W. Watson, 1997.
"Estimating Deterministic Trends In The Presence Of Serially Correlated Errors,"
The Review of Economics and Statistics,
MIT Press, vol. 79(2), pages 184-200, May.
- Eugene Canjels & Mark W. Watson, 1994. "Estimating Deterministic Trends in the Presence of Serially Correlated Errors," NBER Technical Working Papers 0165, National Bureau of Economic Research, Inc.
- Eugene Canjels & Mark W. Watson, 1994. "Estimating deterministic trends in the presence of serially correlated errors," Working Paper Series, Macroeconomic Issues 94-19, Federal Reserve Bank of Chicago.
- Lucas, André, 1995. "Unit Root Tests Based on M Estimators," Econometric Theory, Cambridge University Press, vol. 11(02), pages 331-346, February.
- Knight, Keith, 1991. "Limit Theory for M-Estimates in an Integrated Infinite Variance," Econometric Theory, Cambridge University Press, vol. 7(02), pages 200-212, June.
- Steven N. Durlauf & Peter C.B. Phillips, 1986.
"Trends Versus Random Walks in Time Series Analysis,"
Cowles Foundation Discussion Papers
788, Cowles Foundation for Research in Economics, Yale University.
- Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-54, November.
- Chipman, John S, 1979. "Efficiency of Least-Squares Estimation of Linear Trend when Residuals are Autocorrelated," Econometrica, Econometric Society, vol. 47(1), pages 115-28, January.
- Beach, Charles M & MacKinnon, James G, 1978. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society, vol. 46(1), pages 51-58, January.
- Potscher, Benedikt M. & Prucha, Ingmar R., 1986. "A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations," Journal of Econometrics, Elsevier, vol. 32(2), pages 219-251, July.
- Phillips, Peter C B & Xiao, Zhijie, 1998.
" A Primer on Unit Root Testing,"
Journal of Economic Surveys,
Wiley Blackwell, vol. 12(5), pages 423-69, December.
- Magee, L., 1985.
"A note on Cochrane - Orcutt estimation,"
CORE Discussion Papers
1985019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-61, January.
- Peter C.B. Phillips & Chin Chin Lee, 1996. "Efficiency Gains from Quasi-Differencing Under Nonstationarity," Cowles Foundation Discussion Papers 1134, Cowles Foundation for Research in Economics, Yale University.
- Rothenberg, Thomas J. & Stock, James H., 1997. "Inference in a nearly integrated autoregressive model with nonnormal innovations," Journal of Econometrics, Elsevier, vol. 80(2), pages 269-286, October.
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