Francq, Christian and Roy, Roch and Saidi, Abdessamad (2011): Asymptotic properties of weighted least squares estimation in weak parma models.
Preview |
PDF
MPRA_paper_28721.pdf Download (349kB) | Preview |
Abstract
The aim of this work is to investigate the asymptotic properties of weighted least squares (WLS) estimation for causal and invertible periodic autoregressive moving average (PARMA) models with uncorrelated but dependent errors. Under mild assumptions, it is shown that the WLS estimators of PARMA models are strongly consistent and asymptotically normal. It extends Theorem 3.1 of Basawa and Lund (2001) on least squares estimation of PARMA models with independent errors. It is seen that the asymptotic covariance matrix of the WLS estimators obtained under dependent errors is generally different from that obtained with independent errors. The impact can be dramatic on the standard inference methods based on independent errors when the latter are dependent. Examples and simulation results illustrate the practical relevance of our findings. An application to financial data is also presented.
Item Type: | MPRA Paper |
---|---|
Original Title: | Asymptotic properties of weighted least squares estimation in weak parma models |
Language: | English |
Keywords: | Weak periodic autoregressive moving average models; Seasonality; Weighted least squares; Asymptotic normality; Strong consistency; Weak periodic white noise; Strong mixing. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 28721 |
Depositing User: | Christian Francq |
Date Deposited: | 09 Feb 2011 19:42 |
Last Modified: | 01 Oct 2019 16:27 |
References: | Aknouche, A. and Bibi, A. (2009) Quasi-maximum likelihood estimation of periodic GARCH and periodic ARMA-GARCH processes. Journal of Time Series Analysis 30, 19-46. Anderson, T. W. (1971). The Statistical Analysis of Time Series. Wiley, New York. Balaban, B., Bayar, A. and Kan, "O. (2001). Stock returns, seasonaly and asymmetric condional volatily in world equy markets. Applied Economic Letters 8, 263-268. Berlinet, A. and Francq, C. (1997). On Bartlett's formula for nonlinear processes. Journal of Time Series Analysis 18, 535-552. Basawa, I. V. and Lund, R. (2001). Large sample properties of parameter estimates for periodic ARMA models. Journal of Time Series Analysis 22, 651-663. Bibi, A. and Gautier, A. (2006) Propri'et'es dans $L^2$ et estimation des processus purement bilin'eaires et strictement superdiagonaux `a coefficients p'eriodiques. Revue Canadienne de Statistique / Canadian Journal of Statistics 34, 131-148. Bloomfield, P., Hurd, H. L., and Lund, R. (1994). Periodic correlation in stratospheric ozone data. Journal of Time Series Analysis 15, 127-150. Bollerslev, T., and Ghysels, E. (1996). Periodic autoregressive condional heteroscedasticy. Journal of Business & Economic Statistics 14, 139-51. Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. 2nd ed., Springer, New York. Cheng, Q. (1999). On time-reversibily of linear processes. Biometrika 86, 483-486. Davydov, Y. A. (1968). On convergence of distributions generated by stationary processes. Theory of Probabily and s Applications 13, 691-696. den Haan, W., and Levin, A. (1997). A practioner’s guide to robust covariance matrix estimation. em in Handbook of Statistics 15, G. Maddala and C. Rao, Eds, 309-327. Elsevier, Amsterdam. Dunsmuir, W. (1979) A central lim theorem for parameters in stationary vector time series and s application to model for a signal observed wh noise. Annals of Statistics 7, 490-506. Dunsmuir, W. and Hannan, E. J. (1976). Vector linear time series models. Advances in Applied Probabily 8, 339-364. Francq, C., and Zako"ian, J. M. (1998a). Estimating linear representations of nonlinear processes. Journal of Statistical Planning and Inference 68, 145-165. Francq, C., and Zako"ian, J. M. (1998b). Estimating the order of weak ARMA models. Prague Stochastic'98 Proceedings, 1, 165-168. Francq, C., and Zako"ian, J. M. (2004). Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes. Bernouilli 10, 605-637. Francq, C., and Zako"ian, J. M. (2009). Bartlett's formula for a general class of nonlinear processes. Journal of Time Series Analysis 30, 449-465. Francq, C., Roy, R. and Zako"ian, J. M. (2005). Diagnostic checking in ARMA models wh uncorrelated errors. Journal of the American Statistical Association 100, 532--544. Franses, P. H. and Paap, R. (2000). Modelling day-of-the-week seasonaly in the S&P 500 index. Applied Financial Economics 10, 483-488. Franses, P. H. and Paap, R. (2004). Periodic Time Series Models. Oxford Universy Press, Oxford. Gardner, W., and C. Spooner (1994). The cumulant theory of cyclostationary time-series, Part I: Foundation. IEEE Transactions on Signal Processing 42, 3387-3408. Gladyshev, E. G. (1961). Periodically correlated random sequences. Soviet Mathematics 2, 385-388. Hannan, E. J. and Deisltler, M. (1988). The Statistical Theory of Linear Systems. Wiley, New York. Hipel, K. W. and McLeod, A. I. (1994) Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam. Hosoya, Y. and Taniguchi, M. (1982). A central lim theorem for stationary processes and the parameter estimation of linear processes. Annals of Statistics 10, 132-153. Correction (1993), 21, 1115-1117. Ibragimov, I. A. (1962). Some lim theorems for stationary processes. Theory of Probabily and s Applications 7, 349-382. Jimenez, C., McLeod, A. I., and Hipel, K. W. (1989). Kalman filter estimation for periodic autoregressive-moving average models. Stochastic Hydrology and Hydraulics 3, 227-240. Jones, R. and Brelsford, W. (1967). Time series wh periodic structure. Biometrika 54, 403-408. Ling, S. and McAleer M. (2002). Necessary and sufficient moment condions for the GARCH(r,s) and asymmetric power GARCH(r,s) models. Econometric Theory 18, 722-729. Lund, R. (2006). A seasonal analysis of riverflow trends. Journal of Statistical Computation and Simulation 76, 397-405. Lund, R. and Basawa, I. V. (2000). Recursive prediction and likelihood evaluation for periodic ARMA models. Journal of Time Series Analysis 21, 75-93. Lund, R., Hurd, H., Bloomfield, P., and Smh, R. (1995). Climatological time series wh periodic correlation. Journal of Climate 8, 2787-2809. Lund, R., Shao, Q. and Basawa, I. (2006). Parsimonious periodic time series modeling. Australian and New Zealand Jourmal of Statistics 48, 33-47. McLeod, A. I. (1994). Diagnostic checking periodic autoregression models wh application. Journal of Time Series Analysis 15, 221-233. Addendum, 16, 647-648. Osborn, D., and Smh, J. (1989). The performance of periodic autoregressive models in forecasting seasonal U.K. consumption. Journal of Business and Economic Statistics 7, 117-127. Pagano, M. (1978). On periodic and multiple autoregressions. Annals of Statistics 6, 1310-1317. Peir'o, A. (1994). Daily seasonaly in stock returns: Further international evidence. Economics Letters 45, 227-232. Reinsel, G. C. (1997). Elements of Multivariate Time Series Analysis. 2nd ed., Springer, New York. Romano, J. P., and Thombs, L. A. (1996). Inference for autocorrelations under weak assumptions. Journal of the American Statistical Association 91, 590-600. Roy, R. and Saidi A. (2008). Temporal aggregation and systematic sampling in PARMA processes. Computational Statistics and Data analysis 52, 4287-4304. Salas, J. D., and Obeysekera, J. T. B. (1992). Conceptual basis of seasonal streamflow time series models. Journal of Hydraulic Engineering 118, 1186-1194. Smadi, A. A. (2005). LS estimation of periodic autoregressive models wh non-Gaussian errors: A simulation study. Journal of Statistical Computation and Simulation 75, 207-216. Shao, Q., and Lund, R. (2004). Computation and characterization of autocorrelations and partial autocorrelations in periodic ARMA models. Journal of Time Series Analysis 25, 359-372. Taniguchi, M. and Kakizawa, Y. (2000). Asymptotic Theory of Statistical Inference for Time Series. Springer, New York. Tesfaye, Y. G., Meerschaert, M. M., and Anderson, P. L. (2006). Identification of PARMA models and their application to the modeling of river flows. Water Resources Research 42, W01419, doi:10.1029/2004WR003772 Tiao, G. C., and Grupe, R. M. (1980). Hidden periodic autoregressive moving average models in time series data. Biometrika 67, 365-373. Troutman, B. (1979). Some results in periodic autoregression. Biometrika 66, 219-228. Vecchia, A. V. (1985a). Maximum likelihood estimation for periodic autoregressive-moving average models. Technometrics 27, 375-384. Vecchia, A. V. (1985b). Periodic autoregressive-moving average modeling wh applications to water resources. Water Resources Bulletin 21, 721-730. Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K. and Ma, J. (2005). Testing and modelling autoregressive condional heteroskedasticy of streamflow processes. Nonlinear Processes in Geophysics 12, 55-66. Wang, W., Vrijling, J. K., Van Gelder, P. H. A. J. M. and Ma, J. (2006). Testing for nonlineary of streamflow at different timescales processes. Journal of Hydrology 322, 247-268. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/28721 |