How to deal with unobservable variables in economics
The paper discusses different methods to deal with unobservable variables: Kalman-Filtering, principal components, factor analysis, LISREL, MIMIC, DYMIMIC, PLS with respect to parameter estimation and forecasting. We got very good results by an extension of Kalman-Filtering called AS (general stationary parameter model). LISREL proved to be superior to PLS in parameter estimation. Explicit introduction of the latent variables "mood" of the economic agents, the "political trend" and "social stability" improved the forecasting performance of an econometric model of the FRG.
|Date of creation:||Aug 1997|
|Date of revision:|
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- Engle, Robert F. & Lilien, David M. & Watson, Mark, 1985. "A dymimic model of housing price determination," Journal of Econometrics, Elsevier, vol. 28(3), pages 307-326, June.
- Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
- Geweke, John F. & Singleton, Kenneth J., 1981. "Latent variable models for time series : A frequency domain approach with an application to the permanent income hypothesis," Journal of Econometrics, Elsevier, vol. 17(3), pages 287-304, December.
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