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- W42516076 abstract "Unbalanced panels are often found in empirical settings. In this paper we concentrate on the case where a particular variable of interest is only observed sporadically for the members of the panel, but a set of explanatory variables exists for the complete panel. We propose a methodology that optimally predicts the missing observations and insures that when an observation exists, the prediction equals the actual observation. We start by defining an econometric model for the variable of interest, which is then formulated as a state space model in terms of the observed data at each time period. A contemporaneously correlated error covariance matrix for the predictions is postulated, in order to gain information from other cross-sectional units in the panel. The method produces a prediction (and prediction error) for each missing observation, estimates of the parameters of the original econometric model and one-step-ahead forecasts for each cross-section in the panel. The estimation and prediction are carried out using Kalman filtering-smoothing algorithms, and both stationary and integrated cases can be handled. An empirical application is presented for the prediction of (log) sale prices for houses based on a hedonic price function." @default.
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- W42516076 date "2002-01-01" @default.
- W42516076 modified "2023-09-26" @default.
- W42516076 title "Predicting missing observations in unbalanced panels: A Kalman filtering-smoothing approach" @default.
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