Sieve Estimation of Time-Varying Panel Data Models With Latent Structures
By Liangjun Su, Xia Wang, Sainan Jin. 2019
78 citations recorded by the source corpus.
We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960–2012 and find four latent groups.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f3da0a5bafddfda8b8c7619dc38d1e63f21b40c4
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Citations in the corpus
- On Time-Varying Factor Models: Estimation and Testing ∗ (2017, methodology): “1 To allow the model to capture various kinds of time-varying factor loadings, we follow the nonparametric literature on time-varying models (e.g., Cai, 2007; Robinson, 2012; Chen and Hong, 2012; Chen et al., 2012; Su et al., 2016) and model λit as a nonrandom function of t/T :”