Inference in Approximately Sparse Correlated Random Effects Probit Models
Year of publication: |
2017
|
---|---|
Authors: | Wooldridge, Jeff |
Other Persons: | Zhu, Ying (contributor) |
Publisher: |
[2017]: [S.l.] : SSRN |
Subject: | Probit-Modell | Probit model | Theorie | Theory | Korrelation | Correlation | Induktive Statistik | Statistical inference | Mehrebenenanalyse | Multi-level analysis |
Extent: | 1 Online-Ressource (31 p) |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: Journal of Business and Economic Statistics, Forthcoming Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 30, 2017 erstellt |
Other identifiers: | 10.2139/ssrn.2733187 [DOI] |
Classification: | C14 - Semiparametric and Nonparametric Methods ; C23 - Models with Panel Data ; C25 - Discrete Regression and Qualitative Choice Models ; c55 |
Source: | ECONIS - Online Catalogue of the ZBW |
-
LM Test of Neglected Correlated Random Effects and Its Application
Hahn, Jinyong, (2015)
-
Efficient probit estimation with partially missing covariates
Conniffe, Denis, (2009)
-
What Can We Learn About Correlations from Multinomial Probit Estimates?
Monfardini, Chiara, (2006)
- More ...
-
Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings
Chandar, Bharat, (2019)
-
Improved Estimation of Dynamic Models of Conditional Means and Variances
Wang, Weining, (2020)
-
Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings
Chandar, Bharat, (2020)
- More ...