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**Category:**A COMPANION TO Theoretical Econometrics- A COMPANION TO THEORETICAL ECONOMETRICS
- Artificial Regressions
- The Concept of an Artificial Regression
- The Gauss-Newton Regression
- Hypothesis Testing with Artificial Regressions
- The OPG Regression
- An Artificial Regression for GMM Estimation
- Artificial Regressions and HETEROsKEDASTiciTy
- Double-Length Regressions
- An Artificial Regression for Binary Response Models
- General Hypothesis. Testing
- Some Test Principles Suggested in the Statistics Literature
- Neyman-Pearson generalized lemma and its applications
- The Neyman-Pearson lemma and the Durbin-Watson test
- Serial Correlation
- The Box-Jenkins class of models
- Serial correlation in the disturbances of the linear regression model
- Maximum likelihood estimation
- Maximum marginal likelihood estimation
- Hypothesis Testing
- Testing disturbances in the dynamic linear regression model
- Model Selection
- Heteroskedasticity
- Sampling Theory Inference with Known Covariance Matrix
- Sampling Theory Estimation and Inference with Unknown Covariance Matrix
- Testing for heteroskedasticity
- Other extensions
- Concluding Remarks
- Seemingly Unrelated. Regression
- Basic Model
- Stochastic Specification
- Testing linear restrictions
- Diagnostic testing
- Other Developments
- Computational matters
- Bayesian methods
- Improved estimation
- Simultaneous. Equation Model. Estimators: Statistical. Properties and. Practical Implications
- Limited-Information Estimators of Structural Parameters
- Full Information Methods
- Large sample properties of limited information estimators
- Large sample properties of full information estimators
- Structure of Limited Information Estimators as Regression Functions
- Finite Sample Properties of Estimators
- Practical Implications
- Identification in Parametric Models
- Basic Concepts
- The Jacobian Matrix Criterion
- Prior Information
- Handling the Rank in Practice
- The Classical Simultaneous Equations Model
- Measurement Error. and Latent Variables
- The Linear Regression Model with Measurement Error
- Inconsistency and bias of the OLS estimators
- Bounds on the parameters
- Solutions to the Measurement Error Problem
- Instrumental variables
- Panel data
- Latent Variable Models
- Factor analysis
- MIMIC and reduced rank regression
- General linear structural equation models
- Diagnostic Testing in Cross Section Contexts
- Diagnostic testing in nonlinear models of conditional means
- Diagnostic Testing in Time Series Contexts
- Omnibus tests on the errors in time series regression models
- Basic Elements of. Asymptotic Theory
- Modes of Convergence for Sequences of Random Vectors
- Convergence in distribution
- Convergence properties and transformations
- Orders of magnitude
- Independent processes
- Dependent processes
- Uniform laws of large numbers
- Central Limit Theorems
- Further Readings
- Generalized Method. of Moments
- The Population Moment Condition and Identification
- The Estimator and a Fundamental Decomposition
- Asymptotic Properties
- The Optimal Two-step or Iterated GMM Estimator
- The Overidentifying Restrictions Test
- Other Estimators as Special Cases of GMM
- Optimal Moments and Nearly Uninformative Moments
- Nearly uninformative moment conditions
- Finite Sample Behavior
- Collinearity
- The Nature and Statistical Consequences of Collinearity
- Collinearity in the linear regression model
- Diagnosing collinearity using the singular value decomposition
- Collinearity and the least squares predictor
- The Variance Decomposition of Belsley, Kuh, and Welsch (1980)
- Other Diagnostic Issues and Tools
- Other diagnostics
- Collinearity-influential observations
- Detecting harmful collinearity
- What to Do?
- Methods for introducing exact nonsample information
- Methods for introducing inexact nonsample information
- Estimation methods designed specifically for collinear data
- Artificial orthogonalization
- Nonlinear Models
- Collinearity in nonlinear regression models
- Collinearity in maximum likelihood estimation
- Closing Remarks
- Nonnested Hypothesis. Testing: An Overview
- Examples of Nonnested Models
- Model Selection Versus Hypothesis Testing
- Alternative Approaches to Testing Nonnested Hypotheses
- Motivation for nonnested statistics
- The Cox procedure
- The comprehensive approach
- The encompassing approach
- Power and finite sample properties
- Measures of Closeness and Vuong's Approach
- Practical Problems
- Resampling the likelihood ratio statistic: bootstrap methods
- Spatial Econometrics
- Spatial autocorrelation
- Spatial stochastic process models
- Direct representation
- Aymptotics in spatial stochastic processes
- Spatial Regression Models
- Spatial dependence in panel data models
- Spatial dependence in models for qualitative data
- Estimation
- Spatial two-stage least squares
- Method of moments estimators
- Specification Tests
- Implementation Issues
- Essentials of Count. Data Regression
- Poisson Regression
- Interpretation of regression coefficients
- Truncation and censoring
- Overdispersion
- Other Parametric Count Regression Models
- Continuous mixture models
- Finite mixture models
- Modified count models
- Discrete choice models
- Partially Parametric Models
- Least squares estimation
- Semiparametric models
- Time Series, Multivariate and Panel Data
- Multivariate data
- Practical Considerations
- Further Reading
- Panel Data Models
- Linear Models
- Dynamic Models
- Sample Attrition and Sample Selection
- Qualitative Response. Models
- Binary and Multinomial Response Models
- Panel Data with Qualitative Variables
- Semiparametric Estimation
- Simulation Methods
- Self-Selection
- Sample Selection Bias
- Some conventional sample selection models
- Parametric Estimation
- Polychotomous choice sample selection models
- Simulation estimation
- Estimation of simultaneous equation sample selection model
- Misspecification and tests
- Semiparametric and Nonparametric Approaches
- Semiparametric efficiency bound and semiparametric MLE
- Semiparametric IV estimation and conditional moments restrictions
- Estimation of the intercept
- Sample selection models with a tobit selection rule
- Identification and estimation of counterfactual outcomes
- Random Coefficient. Models
- Some First-Generation RCMs
- Second-Generation RCMs
- Criteria for Choosing Concomitants in RCMs
- An Empirical Example
- Monte Carlo tests based on pivotal statistics
- Parametric and. Nonparametric Tests. of Limited Domain and. Ordered Hypotheses. in Economics
- Nonlinear models
- The statistical modeling (proper) period: 1927-present
- Concluding Remarks
- More general processes
- Near Seasonal Integration
- Specifying the cointegrating rank
- Nonparametric. Kernel Methods. of Estimation and. Hypothesis Testing
- Nonparametric Regression
- Goodness of fit measures and choices of kernel and bandwidth
- Combined Regression
- Additive Regressions
- Semiparametric models
- Hypothesis Testing
- Durations
- Duration Variables
- Duration dependence
- Basic duration distributions
- Parametric Models
- Exponential regression model
- The exponential model with heterogeneity
- Heterogeneity and negative duration dependence
- Truncation and censoring
- Proportional hazard model
- Duration Time Series
- The Poisson process
- The ACD model
- The SVD model
- Simulation Based. Inference for. Dynamic Multinomial. Choice Models
- The Dynamic Multinomial Choice Model
- Implementing the Gibbs Sampling Algorithm
- Experimental Design and Results
- Appendix A The Future Component
- Appendix B Existence of Joint Posterior Distribution
- Monte Carlo Test. Methods in. Econometrics
- Statistical Issues: A Practical Approach to Core Questions
- Instrumental regressions
- Normality tests
- Uniform linear hypothesis in multivariate regression models
- Econometric applications: discussion
- The Monte Carlo Test Technique
- Monte Carlo tests in the presence of nuisance parameters
- Monte Carlo Tests: Econometric Applications
- Monte Carlo tests in the presence of nuisance parameters: examples from the multivariate regression model
- Non-identified nuisance parameters
- Bayesian Analysis. of Stochastic. Frontier Models
- The Stochastic Frontier Model with Cross-Sectional Data
- Bayesian inference
- Extensions
- Nonlinear production frontiers
- The Stochastic Frontier Model with Panel Data
- Bayesian random effects model
- Summary