Group structure detection for a high‐dimensional panel data model

2021 ◽  
Author(s):  
Wu Wang ◽  
Zhongyi Zhu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Group Structure ◽  
Panel Data Model ◽  
High Dimensional ◽  
Structure Detection

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

A panel data model of length of stay in hospitals for hip replacements

2021 ◽  
Vol 40 (7) ◽  
pp. 688-707
Author(s):  
Yan Meng ◽  
Jiti Gao ◽  
Xibin Zhang ◽  
Xueyan Zhao
Keyword(s):  
Panel Data ◽  
Length Of Stay ◽  
Data Model ◽  
Panel Data Model ◽  
Hip Replacements

DETERMINAN PENANAMAN MODAL ASING LANGSUNG DI ASEAN

2012 ◽  
Vol 7 (1) ◽  
pp. 75
Author(s):  
Joko Susanto
Keyword(s):  
Foreign Direct Investment ◽  
Panel Data ◽  
Data Model ◽  
Direct Investment ◽  
Multinational Enterprises ◽  
Statistical Data ◽  
Regression Equation ◽  
Panel Data Model ◽  
Market Size ◽  
Research Analysis

This research analysis the factors’ that determine the foreign directinvestment (FDI) in ASEAN’s countries especially Indonesia, Malaysia, Philippine and Thailand during 1990-2009. Multinational Enterprises’ (MNE) must decideto choose a locationfor relocating its’ factory by market seeking dan resources seeking strategy. Based on this statement, it can be obtained the regression equation with foreign direct investment is a function of market size, worker’s productivity and infrastructure of road. Statistical data of UNESCAP was used in this research. The regression was base on the panel data model, while the estimation was based on common effects model. This results showthat the market size, worker’s productivity and availability of infrastructure road could be an importance consideration for MNE’s in their choice for FDI.Keywords: foreign direct investment, market size, worker’s productivity, infrastructure of road

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