Band Spectral Regression with Trending Data
AbstractBand spectral regression with both deterministic and stochastic trends is considered. It is shown that trend removal by regression in the time domain prior to band spectral regression can lead to biased and inconsistent estimates in models with frequency dependent coefficients. Both semiparametric and nonparametric regression formulations are considered, the latter including general systems of two-sided distributed lags such as those arising in lead and lag regressions. The bias problem arises through omitted variables and is avoided by careful specification of the regression equation. Trend removal in the frequency domain is shown to be a convenient option in practice. An asymptotic theory is developed and the two cases of stationary data and cointegrated nonstationary data are compared. In the latter case, a levels and differences regression formulation is shown to be useful in estimating the frequency response function at nonzero as well as zero frequencies. Copyright The Econometric Society 2002.
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Bibliographic InfoArticle provided by Econometric Society in its journal Econometrica.
Volume (Year): 70 (2002)
Issue (Month): 3 (May)
Other versions of this item:
- Corbae, D. & Ouliaris, S. & Phillips, P.C.B., 1997. "Band Spectral Regression with Trending Data," Working Papers 97-09, University of Iowa, Department of Economics.
- Dean Corbae & Sam Ouliaris & Peter C.B. Phillips, 1997. "Band Spectral Regression with Trending Data," Cowles Foundation Discussion Papers 1163, Cowles Foundation for Research in Economics, Yale University.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peter C.B. Phillips & Victor Solo, 1989. "Asymptotics for Linear Processes," Cowles Foundation Discussion Papers 932, Cowles Foundation for Research in Economics, Yale University.
- 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.
- 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.
- Phillips, Peter C B & Hansen, Bruce E, 1990.
"Statistical Inference in Instrumental Variables Regression with I(1) Processes,"
Review of Economic Studies,
Wiley Blackwell, vol. 57(1), pages 99-125, January.
- Tom Doan, . "FM: RATS procedure to estimate cointegrating vectors using Fully Modified Least Squares," Statistical Software Components RTS00069, Boston College Department of Economics.
- Xiao, Zhijie & Phillips, Peter C. B., 1998. "Higher-order approximations for frequency domain time series regression," Journal of Econometrics, Elsevier, vol. 86(2), pages 297-336, June.
- Peter C.B. Phillips, 1988. "Spectral Regression for Cointegrated Time Series," Cowles Foundation Discussion Papers 872, Cowles Foundation for Research in Economics, Yale University.
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