Signal extraction and filtering by linear semiparametric methods

Computational Statistics and Data Analysis - Tập 52 - Trang 935-958 - 2007
Tommaso Proietti1
1SEFeMEQ, University of Rome ‘Tor Vergata’, Via Columbia 2, 00133 Rome, Italy

Tài liệu tham khảo

Anderson, 1979 Ansley, 1985, Estimation, filtering and smoothing in state space models with incompletely specified initial conditions, Ann. Statist., 13, 1286, 10.1214/aos/1176349739 Ansley, 1990, Filtering and smoothing in state space models with partially diffuse initial conditions, J. Time Ser. Anal., 11, 275, 10.1111/j.1467-9892.1990.tb00058.x Baxter, 1999, Measuring business cycles: approximate band-pass filters for economic time series, Rev. Econom. Statist., 81, 575, 10.1162/003465399558454 Bell, 2004, Computation of asymmetric signal Extraction filters and mean squared error for ARIMA component models, J. Time. Ser. Anal., 25, 603, 10.1111/j.1467-9892.2004.01920.x Beveridge, 1981, A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘Business cycle’, J. Monetary Econom., 7, 151, 10.1016/0304-3932(81)90040-4 Brown, 2001, Nonparametric smoothing using state space techniques, Canad. J. Statist., 29, 37, 10.2307/3316049 Bryson, 1969 Bujosa, M., García-Ferrer, A., Young, P.C., 2007. Linear dynamic harmonic regression. Comput. Statist. Data Anal., this issue, doi:10.1016/j.csda.2007.07.008. Christiano, 2003, The band pass filter, Internat. Economic Rev., 44, 435, 10.1111/1468-2354.t01-1-00076 Clark, 1987, The cyclical component of U.S. economic activity, Quarterly J. Economics, 102, 797, 10.2307/1884282 Cox, 1961, Prediction by exponentially weighted moving averages and related methods, J. Roy. Statist. Soc. Ser. B, 23, 414 de Boor, 2001 de Jong, 1988, The likelihood for a state space model, Biometrika, 75, 165, 10.1093/biomet/75.1.165 de Jong, 1988, A cross-validation filter for time series models, Biometrika, 75, 594, 10.1093/biomet/75.3.594 de Jong, 1989, Smoothing and interpolation with the state space model, J. Amer. Statist. Assoc., 84, 1085, 10.2307/2290087 de Jong, 1991, The diffuse Kalman filter, Ann. Statist., 19, 1073, 10.1214/aos/1176348139 de Jong, 1994, Fast likelihood evaluation and prediction for nonstationary state space models, Biometrika, 81, 133, 10.1093/biomet/81.1.133 de Jong, 2003, Smoothing with an unknown initial condition, J. Time Ser. Anal., 81, 133 de Jong, 2001, Modeling and smoothing unequally spaced sequence data, Statist. Inference Stochastic Process., 4, 53, 10.1023/A:1017510420686 de Jong, 1998, Diagonosing shocks in time series, J. Amer. Statist. Assoc., 93, 796, 10.2307/2670129 Duncan, 1972, Linear dynamic recursive estimation from the viewpoint of regression analysis, J. Amer. Statist. Assoc., 67, 815, 10.2307/2284643 Durbin, 2001 Godolphin, 2006, Decomposition of time series models in state-space form, Comput. Statist. Data Anal., 50, 2232, 10.1016/j.csda.2004.12.012 Golub, 1996 Gómez, 1999, Three equivalent methods for filtering finite non-stationary time series, J. Business Econom. Statist., 17, 109, 10.2307/1392242 Gómez, 2001, The use of Butterworth filters for trend and cycle estimation in economic time series, J. Business Econom. Statist., 19, 365, 10.1198/073500101681019909 Gómez, 2006, Wiener–Kolmogorov filtering and smoothing for multivariate series with state-space structure, J. Time Ser. Anal., 28, 361, 10.1111/j.1467-9892.2006.00514.x Green, 1994 Harvey, 1989 Harvey, 1993, Detrending, stylized facts and the business cycle, J. Appl. Econometrics, 8, 231, 10.1002/jae.3950080302 Harvey, 2000, Signal extraction and the formulation of unobserved component models, Econometrics J., 3, 84, 10.1111/1368-423X.00040 Harvey, 2003, General model-based filters for extracting trends and cycles in economic time series, Rev. Econom. Statist., 85, 244, 10.1162/003465303765299774 Harville, 1977, Maximum likelihood approaches to variance component estimation and to related problems, J. Amer. Statist. Assoc., 72, 320, 10.2307/2286796 Hastie, 1990 Henderson, 1981, On deriving the inverse of a sum of matrices, SIAM Rev., 23, 53, 10.1137/1023004 Henderson, 1916, Note on graduation by adjusted average, Trans. Actuarial Soc. Amer., 17, 43 Hillmer, 1982, An ARIMA-model-based approach to seasonal adjustment, J. Amer. Statist. Assoc., 77, 63, 10.2307/2287770 Hodrick, 1997, Postwar U.S. business cycles: an empirical investigation, J. Money Credit Banking, 29, 1, 10.2307/2953682 Kaiser, R., Maravall, A., 2001. Measuring Business Cycles in Economic Time Series. Lecture Notes in Statistics, vol. 154. Springer, New York. Kaiser, 2005, Combining filter design with model-based filtering: an application to business-cycle estimation, Internat. J. Forecasting, 21, 691, 10.1016/j.ijforecast.2005.04.016 Kalbfleisch, 1970, Application of likelihood methods to models involving large numbers of parameters (with discussion), J. Roy. Statist. Soc. Ser. B, 32, 175 Kalman, 1960, A new approach to linear filtering and prediction problems, J. Basic Eng. Trans. ASME, Ser. D, 82, 35, 10.1115/1.3662552 Kohn, 1989, A fast algorithm for signal extraction, influence and cross-validation in state space models, Biometrika, 76, 65, 10.1093/biomet/76.1.65 Kohn, 1986, Estimation, prediction, and interpolation for ARIMA modes with missing data, J. Amer. Statist. Assoc., 81, 751, 10.2307/2289007 Koopman, 1993, Disturbance smoother for state space models, Biometrika, 80, 117, 10.1093/biomet/80.1.117 Koopman, 1997, Exact initial Kalman filter and smoother for non-stationary time series models, J. Amer. Statist. Assoc., 92, 1630, 10.2307/2965434 Koopman, 2003, Computing observation weights for signal extraction and filtering, J. Econom. Dynamics Control, 27, 1317, 10.1016/S0165-1889(02)00061-1 Koopman, 2006, Forecasting daily time series using periodic unobserved components time series models, Comput. Statist. Data Anal., 51, 885, 10.1016/j.csda.2005.09.009 Leser, 1961, A simple method of trend construction, J. Roy. Statist. Soc. Ser. B, 23, 91 Lütkepohl, 2005 Maravall, 1993, Stochastic linear trends: modes and estimators, J. Econometrics, 56, 5, 10.1016/0304-4076(93)90099-Q McElroy, 2006, An iterated parametric approach to nonstationary signal extraction, Comput. Statist. Data Anal., 50, 2206, 10.1016/j.csda.2005.07.008 Meinhold, 1983, Understanding the Kalman Filter, Amer. Statist., 37, 123, 10.2307/2685871 Muth, 1960, Optimal properties of exponentially weighted forecasts, J. Amer. Statist. Assoc., 55, 299, 10.2307/2281742 Patterson, 1971, Recovery of inter-block information when blocks sizes are unequal, Biometrika, 58, 545, 10.1093/biomet/58.3.545 Pierce, 1978, Signal extraction error in nonstationary time series, Ann. Stat., 7, 1303, 10.1214/aos/1176344848 Poirier, 1973, Piecewise regression using cubic splines, J. Am. Stat. Assoc., 68, 515, 10.2307/2284770 Pollock, 1999 Pollock, 2000, Trend estimation and de-trending via rational square-wave filters, J. Econometrics, 99, 317, 10.1016/S0304-4076(00)00028-2 Pollock, 2003, Improved frequency selective filters, Comput. Statist. Data Anal., 42, 279, 10.1016/S0167-9473(02)00228-1 Pollock, 2006, Introduction to the special issue on statistical signal extraction and filtering, Comput. Statist. Data Anal., 50, 2137, 10.1016/j.csda.2005.07.009 Pollock, 2006, Econometric methods of signal extraction, Comput. Statist. Data Anal., 50, 2268, 10.1016/j.csda.2005.07.010 Proietti, 2003, Leave-k-out diagnostics in state-space models, J. Time Ser. Anal., 24, 221, 10.1111/1467-9892.00304 Proietti, 2005, New methods for dating the business cycle, Comput. Statist. Data Anal., 49, 477, 10.1016/j.csda.2004.05.024 Proietti, 2007, On the model based interpretation of filters and the reliability of trend-cycle estimates, Econometric Rev Proietti, T., Luati, A., 2007. Least Squares Regression: Graduation and Filters. In: Boumans, M. (Ed.), Measurement in Economics: A Handbook. ch. 16, Academic Press. Rao, 2002 Robinson, 1991, That BLUP is a good thing: the estimation of random effects, Statist. Sci., 6, 15, 10.1214/ss/1177011926 Rosenberg, 1973, Random coefficient models: the analysis of a cross-section of time series by stochastically convergent parameter regression, Ann. Econom. Social Measurement, 2, 399 Ruppert, 2003 Sayed, 2001, A survey of spectral factorization methods, Numer. Linear Algebra Appl., 8, 467, 10.1002/nla.250 Schweppe, 1965, Evaluation of likelihood functions for Gaussian signals, IEEE Trans. Inform. Theory, 11, 61, 10.1109/TIT.1965.1053737 Severini, T.A., 2000. Likelihood Methods in Statistics. Oxford Statistical Science Series, vol. 22. Oxford University Press, Oxford. Shephard, 1990, On the probability of estimating a deterministic component in the local level model, J. Time Ser. Anal., 11, 339, 10.1111/j.1467-9892.1990.tb00062.x Tiao, 1993, Robustness of maximum likelihood estimates for multi-step predictions: the exponential smoothing case, Biometrika, 80, 623, 10.1093/biomet/80.3.623 Tunnicliffe-Wilson, 1989, On the use of marginal likelihood in time series model estimation, J. Roy. Statist. Soc. Ser. B, 51, 15 Wahba, 1978, Improper priors, spline smoothing and the problem of guarding against errors in regression, J. Roy. Statist. Soc. Ser. B, 3, 364 Wecker, 1983, The signal extraction approach to nonlinear regression and spline smoothing, J. Amer. Statist. Assoc., 78, 81, 10.2307/2287113 West, 1997 Whittaker, 1923, On new method of graduation, Proc. Edinburgh Math. Soc., 41, 63, 10.1017/S0013091500077853 Whittle, 1983