Panel Data Econometrics with R
  • Release Date : 22 October 2018
  • Publisher : John Wiley & Sons
  • Genre : Business & Economics
  • Pages : 328 pages
  • ISBN 13 : 9781118949160
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Download or read book entitled Panel Data Econometrics with R by author: Yves Croissant which was release on 22 October 2018 and published by John Wiley & Sons with total page 328 pages . This book available in PDF, EPUB and Kindle Format. 1 Introduction 5 1.1 Panel data econometrics: a gentle introduction 5 1.1.1 Eliminating unobserved components 6 1.2 R for econometric computing 11 1.2.1 The modus operandi of R 12 1.2.2 Data management 13 1.3 plm for the casual R user 14 1.3.1 R for the matrix language user 14 1.3.2 R for the user of econometric packages 16 1.4 plm for the procient R user 18 1.4.1 Reproducibile econometric work 18 1.4.2 Object-orientation for the user 19 1.5 plm for the R developer 20 1.5.1 Object orientation for development 21 1.6 Notations 24 2 The error component model 31 2.1 Notations and hypotheses 31 2.1.1 Notations 31 2.1.2 Some useful transformations 32 2.1.3 Hypotheses concerning the errors 34 2.2 Ordinary least squares estimators 36 2.2.1 Ordinary least squares on the raw data: the pooling model 36 2.2.2 The between estimator 38 2.2.3 The within estimator 39 2.3 The generalized least squares estimator 44 2.3.1 Presentation of the gls estimator 44 2.3.2 Estimation of the variances of the components of the error 46 2.4 Comparison of the estimators 51 2.4.1 Relations between the estimators 51 2.4.2 Comparison of the variances 52 2.4.3 Fixed vs random eects 53 2.4.4 Some simple linear model examples 55 2.5 The two-ways error components model 60 2.5.1 Error components in the two-ways model 60 2.5.2 Fixed and random eects models 61 2.6 Estimation of a wage equation 62 3 Advanced error components models 67 3.1 Unbalanced panels 67 3.1.1 Individual eects model 67 3.1.2 Two-ways error component model 69 3.1.3 Estimation of the components of the error variance 73 3.2 Seemingly unrelated regression equations 80 3.2.1 Introduction 80 3.2.2 Constrained least squares 81 3.2.3 Inter-equations correlation 82 3.2.4 SUR with panel data 83 3.3 The maximum likelihood estimator 88 3.3.1 Derivation of the likelihood function 89 3.3.2 Computation of the estimator 90 3.4 The nested error components model 92 3.4.1 Presentation of the model 92 3.4.2 Estimation of the variance of the error components 93 4 Tests on error component models 101 4.1 Tests on individual and/of time eects 102 4.1.1 F tests 102 4.1.2 Breusch-Pagan tests 102 4.2 Tests for correlated eects 107 4.2.1 The Mundlak approach 108 4.2.2 Hausman's test 109 4.2.3 Chamberlain's approach 110 4.3 Tests for serial correlation 115 4.3.1 Unobserved eects test 116 4.3.2 Score test of serial correlation and/or individual eects 117 4.3.3 Likelihood Ratio tests for ar(1) and individual eects 120 4.3.4 Applying traditional serial correlation tests to panel data 122 4.3.5 Wald tests for serial correlation 124 4.4 Tests for cross-sectional dependence 126 4.4.1 Pairwise correlation coe-cients 126 4.4.2 cd -type tests for cross-sectional dependence 127 4.4.3 Testing cross-sectional dependence in a pseries 129 5 Robust inference and estimation 133 5.1 Robust inference 133 5.1.1 Robust covariance estimators 134 5.1.2 plm and generic sandwich estimators 145 5.1.3 Robust testing of linear hypotheses 150 5.2 Unrestricted generalized least squares 154 5.2.1 General feasible generalized least squares 155 5.2.2 Applied examples 160 6 Endogeneity 167 6.1 Introduction 167 6.2 The instrumental variables estimator 168 6.2.1 Generalities about the instrumental variables estimator 168 6.2.2 The within instrumental variables estimator 170 6.3 Error components instrumental variables estimator 173 6.3.1 The general model 173 6.3.2 Special cases of the general model 176 6.4 Estimation of a system of equations 186 6.4.1 The three stage least squares estimator 186 6.4.2 The error components three stage least squares estimator 188 6.5 More empirical examples 191 7 Estimation of a dynamic model 193 7.1 Dynamic model and endogeneity 195 7.1.1 The bias of the ols estimator 195 7.1.2 The within estimator 197 7.1.3 Consistent estimation methods for dynamic models 198 7.2 gmm estimation of the dierenced model 201 7.2.1 Instrumental variables