2006), financial risk-return analysis (Ludvigson and Ng 2007), monetary policy analysis (e.g., Bernanke et al. Areas of economic analysis using dynamic factor models include, for example, yield curve modeling (e.g., Diebold and Li 2006 Diebold et al. They have therefore become popular among macroeconometricians see, e.g., Breitung and Eickmeier ( 2006), for an overview. In Economics, dynamic factor models are motivated by theory, which predicts that macroeconomic shocks should be pervasive and affect most variables within an economic system. The common component is assumed to be driven by a few common factors, thereby reducing the dimension of the system.
#Kalman filter eviews series
The basic idea is to separate a possibly large number of observable time series into two independent and unobservable, yet estimable, components: a ‘common component’ that captures the main bulk of co-movement between the observable series, and an ‘idiosyncratic component’ that captures any remaining individual movement. ISBN 1-4039-0209-7.Dynamic factor models are used in data-rich environments. Modelling Trends and Cycles in Economic Time Series. Applied Econometric Time Series (Third ed.). "An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data" (PDF). "Why You Should Never Use the Hodrick-Prescott Filter" (PDF). "Exact Formulas for the Hodrick-Prescott Filter". "Estimating Changes in Trend Growth of Total Factor Productivity: Kalman and H-P Filters versus a Markov-Switching Framework".
"On adjusting the Hodrick–Prescott filter for the frequency of observations" (PDF). " Hodrick–Prescott Filter" March 12, 2004
Proceedings of the Edinburgh Mathematical Association. Business Cycles: An Empirical Investigation". Hodrick finds that for time series in which there are distinct growth and cyclical components, the HP filter comes closer to isolating the cyclical component than the Hamilton alternative. Hamilton is actually better than the HP filter at extracting the cyclical component of several simulated time series calibrated to approximate U.S. Hodrick titled "An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data" examines whether the proposed alternative approach of James D. A regression of the variable at date t+h on the four most recent values as of date t offers a robust approach to detrending that achieves all the objectives sought by users of the HP filter with none of its drawbacks."Ī working paper by Robert J. (3) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice, e.g., a value for λ far below 1600 for quarterly data. (2) A one-sided version of the filter reduces but does not eliminate spurious predictability and moreover produces series that do not have the properties sought by most potential users of the HP filter. "(1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process.
Hamilton at UC San Diego titled "Why You Should Never Use the Hodrick-Prescott Filter" presents evidence against using the HP filter. The reasoning for the methodology uses ideas related to the decomposition of time series. 2 Drawbacks to the Hodrick–Prescott filter.