Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown
نویسندگان
چکیده
This paper proposes a new approach to estimate panel data models with group specific parameters when group membership is not known. We first create a set of “pseudo” threshold variables based on time series estimation of the individual specific parameters. We then use these variables to stratify individuals. The problem of parameter heterogeneity is turned into estimation of a panel threshold model in which the threshold variables are themselves being estimated. We show that individuals can be consistently sorted into groups distinguished by parameter heterogeneity when N and T are large. Results are compared to the K-means algorithm adapted to panel data regressions with fixed effects.
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