Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

/pdf/econometric-analysis-of-panel-data-46ld72a5p1.pdf

Econometric Analysis of Panel Data

计量经济分析 = Econometric analysis

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly. The properties of the QMLE and the information matrix are exploited to yield several useful tests for model misspecification.

https://ecsocman.hse.ru/data/774/042/1218/white_maximum_likekihood_estimation.pdf

Maximum likelihood estimation of misspecified models

Preface MATRICES: Basic Properties of Vectors and Matrices Kronecker Products, the Vec-Operator and the Moore- Penrose Inverse Miscellaneous Matrix Results DIFFERENTIALS: THE THEORY: Mathematical Preliminaries Differentials and Differentiability The Second Differential Static Optimization DIFFERENTIALS: THE PRACTICE: Some Important Differentials First- Order Differentials and Jacobian Matrices Second-Order Differentials and Hessian Matrices INEQUALITIES: Inequalities THE LINEAR MODEL: Statistical Preliminaries The Linear Regression Model Further Topics in the Linear Model APPLICATIONS TO MAXIMUM LIKELIHOOD ESTIMATION: Maximum Likelihood Estimation Simultaneous Equations Topics in Psychometrics Subject Index Bibliography.

Matrix Differential Calculus with Applications in Statistics and Econometrics

This paper considers the problem of testing statistical hypotheses in linear regression models with inequality constraints on the regression coefficients. The Kuhn-Tucker multiplier test statistic is defined and its relationships with the likelihood ratio test and the Wald test are examined. It is shown, in particular, that these relationships are the same as in the equality constrained case. It is emphasized, however, that their common asymptotic distribution is a mixture of chi-square distributions under the null hypothesis.

Likelihood ration test, Wald test and Kuhn-Tucker test in linear-models with inequality constraints on the regression parameters Likelihood Ratio Test, Wald Test and Kuhn-Tucker Test in Linear Models with Inequality Constraints on the Regression Parameters

A Remark on Hausman's Specification Test

https://www.zora.uzh.ch/id/eprint/1143/1/van_de_Ven_2003.pdf

Risk adjustment and risk selection on the sickness fund insurance market in five European countries.

Statistical inference for a system of simultaneous, nonlinear, implicit equations is discussed. The discussion considers inference as an adjunct to maximum likelihood estimation rather than in a general setting. The null and non-null asymptotic distributions of the Wald test, the Lagrange multiplier test (Rao's efficient score test), and the likelihood ratio test are obtained. Several refinements in the existing theory of maximum likelihood estimation are accomplished as intermediate steps. IT IS NECESSARY to compute the power of a statistical test in two instances. The first is in the design of an experiment. In design, one is obliged to verify, prior to the expenditure of resources, that an experimental effect would be detected with a reasonably high probability. Peak load electricity pricing experiments come immediately to mind as examples. The second is when failure to reject a null hypothesis is used to claim that the data support the null hypothesis. To validate this claim, it must be shown that candidate alternatives would have been detected with a reasonably high probability. This article sets forth formulas for asymptotic approximations of power for tests commonly used in connection with maximum likelihood estimation for a system of simultaneous, nonlinear, implicit equations. The reader who is interested only in this result should skim Sections 2 and 5 to become familiar with the notation and then read Section 6. See Gallant and Jorgenson [7] for similar formulas if two- and three-stage estimation methods are employed instead. Several refinements in the existing theory of maximum likelihood estimation (Amemiya [2]) are accomplished as intermediate steps in the derivation of the asymptotic approximations. They are as follows. In any theory of nonlinear statistical analysis, various sequences of random functions must converge uniformly in their argument. However, merely listing these sequences and assuming uniform convergence is not very helpful to the practitioner. Conditions which are easily recognized as obtaining or not obtaining in an application are preferabie. Here, the notion of Cesaro summable sequences is used to show that uniform convergence obtains if the log likelihood and its derivatives are dominated by integrable functions. If normal errors are imposed, then it is shown that the requisite domination may be stated in terms of the structural model itself. The critical assumption is that the limit of the log likelihood must have a unique maximum. This implies strong consistency of the maximum likelihood estimator itself, not merely that there exists a solution of the first order conditions which is

https://www.jstor.org/stable/pdfplus/1913131.pdf

Alberto Holly

Papers

Likelihood ration test, Wald test and Kuhn-Tucker test in linear-models with inequality constraints on the regression parameters Likelihood Ratio Test, Wald Test and Kuhn-Tucker Test in Linear Models with Inequality Constraints on the Regression Parameters

A Remark on Hausman's Specification Test

Risk adjustment and risk selection on the sickness fund insurance market in five European countries.

Statistical inference in an implicit, nonlinear, simultaneous equation model in the context of maximum likelihood estimation'

Risk adjustment in Switzerland.