Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown

Abstract

This paper proposes two methods for estimating panel data models with group specific parameters when group membership is not known. The first method uses the individual level time series estimates of the parameters to form threshold variables. The problem of parameter heterogeneity is turned into estimation of a panel threshold model with an unknown threshold value. The second method modifies the K-means algorithm to perform conditional clustering. Units are clustered based on the deviations between the individual and the group conditional means. The two approaches are used to analyze growth across countries and housing market dynamics across the states in the U.S.

Sparse estimation of huge networks with a block‐wise structure

“…It is important, however, to remark that our approach requires a priori information on the block structure. If this is not available, then one could exploit methods from the clustering literature that allow us to determine endogenously the optimal grouping of cross-sectional units, such as the k-means algorithm (Forgy, 1965) extended to allow for covariates in the model; see, in particular, Lin and Ng (2012) and Bonhomme and Manresa (2015), and also Ando and Bai (2016). Our approach also has potential application in the area of spatial econometrics.…”

“…When the grouping is not fully known a priori, we could use methods that allow us to determine endogenously the optimal grouping of cross-sectional units, by employing techniques from the clustering literature; see, e.g. Lin and Ng (2012), Bonhomme and Manresa (2015) and Ando and Bai (2016).…”

Classification of Nonparametric Regression Functions in Heterogeneous Panels

“…. , G K 0 in this parametric framework has been considered, for example, in Sarafidis and Weber (2014) and Su et al (2014) who work with penalization techniques, and in Lin and Ng (2012) who employ thresholding and k-means clustering methods.…”

Panel Data Models with Grouped Factor Structure Under Unknown Group Membership

“…There are a small number of papers that study panel data models with unobserved heterogeneity when group membership is unknown. Bonhomme and Manresa (2012), Lin and Ng (2012) and Sun (2005) investigated this challenging problem. In contrast to previous models, there is a factor structure in each group.…”

“…However, there is evidence that homogeneity of the parameters is rejected (see for example Hsiao and Tahmiscioglu (1997), Lin and Ng (2012)). To deal with the presence of unobserved heterogeneity, we therefore extend the proposed model to the flexible yet parsimonious approach.…”