104
Views
1
CrossRef citations to date
0
Altmetric
Article

Signal smoothing for score-driven models: a linear approach

, &
Pages 829-852 | Received 28 Jun 2021, Accepted 12 Jan 2022, Published online: 30 Mar 2022

References

  • Ansley, C. F., and R. Kohn. 1985. Estimation, filtering and smoothing in state space models with incompletely specified initial conditions. Annals of Statistics 13:1286–316.
  • Bell, W. 1984. Signal extraction for nonstationary time series. Annals of Statistics 12:646–64.
  • Bell, W. 2004. On RegCOMPONENT time series models and their applications. In: State space and unobserved component models: Theory and applications, eds., A. C. Harvey, S. J. Koopman and N. Shephard, 248–83. Cambridge: Cambridge University Press.
  • Bell, W. R., and S. C. Hillmer. 1984. Issues involved with the seasonal adjustment of economic time series. Journal of Business & Economic Statistics 2:291–320.
  • Bell, W., and S. Hillmer. A matrix approach to likelihood evaluation and signal extraction for ARIMA component time series models. SRD Research Report No. RR-88/22, Bureau of the Census; 1988.
  • Beveridge, S., and C. R. Nelson. 1981. A new approach to the decomposition of economic time series into permanent and transitory components with particular attention to the measurement of the ‘business cycle’. Journal of Monetary Economics 7 (2):151–74. doi:10.1016/0304-3932(81)90040-4.
  • Blasques, F., A. Lucas, and A. C. van Vlodrop. 2021. Finite sample optimality of score-driven volatility models: Some Monte Carlo evidence. Econometrics and Statistics 19:47–57. doi:10.1016/j.ecosta.2020.03.010.
  • Blasques, F., J. van Brummelen, S. J. Koopman, and A. Lucas. 2021. Maximum likelihood estimation for score-driven models. Journal of Econometrics. doi:10.1016/j.jeconom.2021.06.003.
  • Blasques, F., P. Gorgi, S. J. Koopman, and O. Wintenberger. 2018. Feasible invertibility conditions and maximum likelihood estimation for observation-driven models. Electron J Stat 12:1019–52.
  • Blasques, F., S. J. Koopman, and A. Lucas. 2015. Information-theoretic optimality of observation-driven time series models for continuous responses. Biometrika 102 (2):325–43. doi:10.1093/biomet/asu076.
  • Blazsek, S., A. Escribano, and A. Licht. 2020. Prediction accuracy of bivariate score-driven risk premium and volatility filters: An illustration for the Dow Jones. Working Paper. 2020-10, University Carlos III of Madrid, Department of Economics.
  • Blazsek, S., A. Escribano, and A. Licht. 2021a. Co-integration with score-driven models: An application to US real GDP growth, US inflation rate, and effective federal funds rate. Macroeconomic Dynamics. doi:10.1017/S1365100521000365.
  • Blazsek, S., A. Escribano, and A. Licht. 2021b. Identification of seasonal effects in impulse responses using score-driven multivariate location models. Journal of Econometric Methods 10 (1):53–66. doi:10.1515/jem-2020-0003.
  • Blazsek, S., A. Escribano, and A. Licht. 2021c. Multivariate Markov-switching score-driven models: An application to the global crude oil market. Studies in Nonlinear Dynamics and Econometrics. doi:10.1515/snde-2020-0099.
  • Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 (3):307–27. doi:10.1016/0304-4076(86)90063-1.
  • Box, G. E. P., and G. M. Jenkins. 1970. Time series analysis, forecasting and control. San Francisco: Holden-Day.
  • Bryan, M. F., and C. J. Pike. 1991. Median price changes: An alternative approach to measuring current monetary inflation. Economic Commentary (Federal Reserve Bank of Cleveland). doi:10.26509/frbc-ec-19911201.
  • Buccheri, G., G. Bormetti, F. Corsi, and F. Lillo. 2019. Filtering and smoothing with score-driven models. SSRN Electronic Journal.
  • Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel Inference, a practical information-theoretic approach. New York: Springer-Verlag.
  • Cox, D. R. 1981. Statistical analysis of time series: Some recent developments (with discussion and reply). Scandinavian Journal of Statistics 8:93–115.
  • Creal, D., S. J. Koopman, and A. Lucas. 2008. A general framework for observation driven time-varying parameter models. Tinbergen Institute Discussion Paper 08-108/4.
  • Creal, D., S. J. Koopman, and A. Lucas. 2011. A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations. Journal of Business & Economic Statistics 29 (4):552–63. doi:10.1198/jbes.2011.10070.
  • Creal, D., S. J. Koopman, and A. Lucas. 2013. Generalized autoregressive score models with applications. Journal of Applied Econometrics 28 (5):777–95. doi:10.1002/jae.1279.
  • Cristadoro, R., M. Forni, L. Reichlin, and G. Veronese. 2005. A core inflation indicator for the euro area. Journal of Money, Credit, and Banking 37 (3):539–60. doi:10.1353/mcb.2005.0028.
  • Dickey, D. A., and W. A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74:427–31.
  • Durbin, J., and S. J. Koopman. 2012. Time series analysis by state space methods. Oxford: Oxford University Press.
  • Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (4):987–1007. doi:10.2307/1912773.
  • Erceg, C. J., and A. T. Levin. 2003. Imperfect credibility and inflation persistence. Journal of Monetary Economics 50 (4):915–44. doi:10.1016/S0304-3932(03)00036-9.
  • Ghysels, E. 1987. Seasonal extraction in the presence of feedback. Journal of Business & Economic Statistics 5:191–4.
  • Harvey, A. C. 1989. Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.
  • Harvey, A. C. 2013. Dynamic models for volatility and heavy tails: With applications to financial and economic time series. Econometric Society Monographs. Cambridge: Cambridge University Press.
  • Harvey, A. C., and T. Chakravarty. 2008. Beta-t-(E)GARCH. Cambridge Working Papers in Economics 0840, Faculty of Economics, University of Cambridge.
  • Harvey, A., and A. Luati. 2014. Filtering with heavy tails. Journal of the American Statistical Association.109 (507):1112–22. doi:10.1080/01621459.2014.887011.
  • Hyndman, R. J., A. B. Koehler, R. D. Snyder, and S. Grose. 2002. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 18 (3):439–54. doi:10.1016/S0169-2070(01)00110-8.
  • Koopman, S. J., and A. C. Harvey. 2003. Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics and Control 27 (7):1317–33. doi:10.1016/S0165-1889(02)00061-1.
  • McElroy, T. 2008. Matrix formulas for nonstationary ARIMA signal extraction. Econometric Theory 24:1–22.
  • McElroy, T. S., and A. Maravall. 2014. Optimal signal extraction with correlated components. Journal of Time Series Econometrics 6:237–73.
  • Millar, R. B. 2011. Maximum likelihood estimation and inference: With examples in R, SAS and ADMB. Chichester: John Wiley & Sons.
  • Nelson, D. B. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 (2):347–70. doi:10.2307/2938260.
  • Ord, K., A. B. Koehler, and R. D. Snyder. 1997. Estimation and prediction for a class of dynamic nonlinear statistical models. Journal of the American Statistical Association 92 (440):1621–9. doi:10.1080/01621459.1997.10473684.
  • Proietti, T. 2006. Trend-cycle decompositions with correlated components. Econometric Reviews 25 (1):61–84. doi:10.1080/07474930500545496.
  • Rich, R. W., and C. Steindler. 2005. A review of core inflation and an evaluation of its measures. FRB of New York Staff Report No. 236.
  • Shapiro, S. S., and M. B. Wilk. 1965. An analysis of variance test for normality (complete samples). Biometrika 52 (3-4):591–611. doi:10.1093/biomet/52.3-4.591.
  • Snyder, R. D. 1985. Recursive estimation of dynamic linear models. Journal of the Royal Statistical Society 47 (2):272–6. doi:10.1111/j.2517-6161.1985.tb01355.x.
  • White, H. 2001. Asymptotic theory for econometricians. San Diego: Academic Press.
  • Wooldridge, J. M. 1994. Estimation and inference for dependent processes. In: Handbook of econometrics, eds. R.F. Engle and D. L. McFadden, vol. 4, 2639–738. Amsterdam: North-Holland.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.