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econometrics of panel data and limited dependent variable
Preamble
These lecture notes were written for a 2nd-year Ph.D. course in econometrics of
panel data and limited-dependent-variable-models. The primary goal of the course
is to introduce tools necessary to understand and implement empirical studies in
economics focusing on other than time-series issues. The main emphasis of the
course is twofold: (i) to extend regression models in the context of cross-section
and panel data analysis, (ii) to focus on situations where linear regression models
are not appropriate and to study alternative methods. Examples from applied
work will be used to illustrate the discussed methods. Note that the course covers
much of the work of the Nobel prize laureates for 2000.
以下是这篇文献的目录:
Contents
I Introduction 7
1 Causal Parameters and PolicyAnalysis in Econometrics . . . . . . . . . 7
2 Reminder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1 Note on Properties of Joint Normal pdf . . . . . . . . . . . . . . . 9
2.2 Testing Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Deviations fromthe Basic Linear RegressionModel . . . . . . . . . . . 11
II Panel Data Regression Analysis 13
4 GLS with Panel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1 SURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 RandomCoefficientsModel . . . . . . . . . . . . . . . . . . . . . 14
4.3 RandomEffectsModel . . . . . . . . . . . . . . . . . . . . . . . . 16
5 What to Do When E[ε | x]
= 0 . . . . . . . . . . . . . . . . . . . . . . 17
5.1 The Fixed EffectModel . . . . . . . . . . . . . . . . . . . . . . . 17
5.2 Errors inVariables . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6 Testing in Panel Data Analysis . . . . . . . . . . . . . . . . . . . . . . 21
6.1 Hausman test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.2 UsingMinimumDistanceMethods in Panel Data . . . . . . . . . 22
6.2.1 TheMinimumDistanceMethod . . . . . . . . . . . . . . . 22
6.2.2 Arbitrary Error Structure . . . . . . . . . . . . . . . . . . 24
6.2.3 Testing the Fixed EffectsModel . . . . . . . . . . . . . . . 25
7 Simultaneous Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 26
8 GMMand its Application in Panel Data . . . . . . . . . . . . . . . . . 27
III Qualitative and Limited Dependent Variables 30
9 Qualitative responsemodels . . . . . . . . . . . . . . . . . . . . . . . . 30
9.1 Binary Choice Models . . . . . . . . . . . . . . . . . . . . . . . . 30
9.1.1 Linear Probability Model . . . . . . . . . . . . . . . . . . . 30
9.1.2 Logit and Probit MLE . . . . . . . . . . . . . . . . . . . . 31
9.1.3 The WLS-MD for Multiple Observations . . . . . . . . . . 33
9.1.4 Panel Data Applications of Binary Choice Models . . . . . 33
9.1.5 Choice-based sampling . . . . . . . . . . . . . . . . . . . . 35
9.1.6 Relaxing the distributional assumptions of binary choice
models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
9.2 Multinomial ChoiceModels . . . . . . . . . . . . . . . . . . . . . 38
9.2.1 Unordered Response Models . . . . . . . . . . . . . . . . . 38
9.2.2 Sequential ChoiceModels . . . . . . . . . . . . . . . . . . 42
9.2.3 Ordered ResponseModels . . . . . . . . . . . . . . . . . . 44
9.3 Models for CountData . . . . . . . . . . . . . . . . . . . . . . . . 44
9.4 ThresholdModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
10 LimitedDependent Variables . . . . . . . . . . . . . . . . . . . . . . . . 46
10.1 CensoredModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
10.2 TruncatedModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10.3 Semiparametric Truncated and Censored Estimators . . . . . . . . 50
10.4 Introduction to Sample Selection . . . . . . . . . . . . . . . . . . 52
10.5 Endogenous Stratified Sampling . . . . . . . . . . . . . . . . . . . 52
10.6 Models with Self-selectivity . . . . . . . . . . . . . . . . . . . . . 53
10.6.1 Roy’smodel . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.6.2 Heckman’s λ . . . . . . . . . . . . . . . . . . . . . . . . . . 55
10.6.3 Switching Regression . . . . . . . . . . . . . . . . . . . . . 57
10.6.4 Semiparametric Sample Selection . . . . . . . . . . . . . . 58
11 Program Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
12 Duration Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
12.1 Hazard Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
12.2 Estimation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
12.2.1 Flexible Heterogeneity Approach . . . . . . . . . . . . . . 65
12.2.2 Left Censored Spells . . . . . . . . . . . . . . . . . . . . . 70
12.2.3 ExpectedDuration Simulations . . . . . . . . . . . . . . . 70
12.2.4 Partial Likelihood . . . . . . . . . . . . . . . . . . . . . . . 71
IV Some Recent Topics in Econometrics 72
13 Structural Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
14 Nonparametrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
14.1 Kernel estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
14.2 K-th Nearest Neighbor . . . . . . . . . . . . . . . . . . . . . . . . 73
14.3 Local Linear Regression . . . . . . . . . . . . . . . . . . . . . . . 73
14.4 Multidimensional Extensions . . . . . . . . . . . . . . . . . . . . . 74
14.5 Partial LinearModel . . . . . . . . . . . . . . . . . . . . . . . . . 75
14.6 Quantile Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 75
15 MiscellaneousOther Topics . . . . . . . . . . . . . . . . . . . . . . . . 75