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; Spatio-temporal Models
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- Durlauf, Steven N & Phillips, Peter C B, 1988.
"Trends versus Random Walks in Time Series Analysis,"
Econometric Society, vol. 56(6), pages 1333-54, November.
- 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.
- Phillips, Peter C.B., 1995.
"Robust Nonstationary Regression,"
Cambridge University Press, vol. 11(05), pages 912-951, October.
- Magee, L., 1985.
"A note on Cochrane - Orcutt estimation,"
CORE Discussion Papers
1985019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Lucas, André, 1995. "Unit Root Tests Based on M Estimators," Econometric Theory, Cambridge University Press, vol. 11(02), pages 331-346, February.
- Peter C.B. Phillips & Zhijie Xiao, 1998.
"A Primer on Unit Root Testing,"
Cowles Foundation Discussion Papers
1189, Cowles Foundation for Research in Economics, Yale University.
- White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-61, January.
- 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.
- Xiao, Zhijie & Phillips, Peter C.B., 1999. "Efficient Detrending In Cointegrating Regression," Econometric Theory, Cambridge University Press, vol. 15(04), pages 519-548, August.
- 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.
- 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, 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," Working Paper Series, Macroeconomic Issues 94-19, Federal Reserve Bank of Chicago.
- 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.
- 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.
- 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.
- 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.
- Granger, Clive W. J. & Swanson, Norman R., 1997.
"An introduction to stochastic unit-root processes,"
Journal of Econometrics,
Elsevier, vol. 80(1), pages 35-62, September.
- 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.
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