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Social and Behavioral Sciences Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Social and Behavioral Sciences
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as special cases.
Hedonic Housing Prices In Paris: An Unbalanced Spatial Lag Pseudo-Panel Model With Nested Random Effects, Badi H. Baltagi, Georges Bresson, Jean-Michel Etienne
Hedonic Housing Prices In Paris: An Unbalanced Spatial Lag Pseudo-Panel Model With Nested Random Effects, Badi H. Baltagi, Georges Bresson, Jean-Michel Etienne
Center for Policy Research
This paper estimates a hedonic housing model based on flats sold in the city of Paris over the period 1990-2003. This is done using maximum likelihood estimation taking into account the nested structure of the data. Paris is historically divided into 20 arrondissements, each divided into four quartiers (quarters), which in turn contain between 15 and 169 blocks (îlot, in French) per quartier. This is an unbalanced pseudo-panel data containing 156,896 transactions. Despite the richness of the data, many neighborhood characteristics are not observed, and we attempt to capture these neighborhood spill-over effects using a spatial lag model. Using Likelihood …
Estimation And Prediction In The Random Effects Model With Ar(P) Remainder Disturbances, Badi Baltagi, Long Liu
Estimation And Prediction In The Random Effects Model With Ar(P) Remainder Disturbances, Badi Baltagi, Long Liu
Center for Policy Research
This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example.
The Hausman-Taylor Panel Data Model With Serial Correlation, Badi Baltagi, Long Liu
The Hausman-Taylor Panel Data Model With Serial Correlation, Badi Baltagi, Long Liu
Center for Policy Research
This paper modifies the Hausman and Taylor (1981) panel data estimator to allow for serial correlation in the remainder disturbances. It demonstrates the gains in efficiency of this estimator versus the standard panel data estimators that ignore serial correlation using Monte Carlo experiments.