Forecasting with the Standardized Self-Perturbed Kalman Filter
Stefano Grassi (),
Nima Nonejad () and
Paolo Santucci de Magistris ()
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Nima Nonejad: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
We propose and study the finite-sample properties of a modified version of the self-perturbed Kalman filter of Park and Jun (1992) for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is now weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the level of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other on-line, classical and Bayesian methods. The standardized self-perturbed Kalman filter is adopted to forecast the equity premium on the S&P500 index under several model specifications, and to investigate to what extent and how realized variance can be exploited to predict excess returns.
Keywords: TVP models; Self-Perturbed Kalman Filter; Forecasting; Equity Premium; Realized Variance (search for similar items in EconPapers)
JEL-codes: C10 C11 C22 C80 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Working Paper: Forecasting with the Standardized Self-Perturbed Kalman Filter (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2014-12
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