Карта сайта Мастерской Своего Дела
- 07-2015 The Concept of an Artificial Regression
- 07-2015 The Gauss-Newton Regression
- 07-2015 Hypothesis Testing with Artificial Regressions
- 07-2015 The OPG Regression
- 07-2015 An Artificial Regression for GMM Estimation
- 07-2015 Artificial Regressions and HETEROsKEDASTiciTy
- 07-2015 Double-Length Regressions
- 07-2015 An Artificial Regression for Binary Response Models
- 07-2015 General Hypothesis. Testing
- 07-2015 Some Test Principles Suggested in the Statistics Literature
- 07-2015 Neyman-Pearson generalized lemma and its applications
- 07-2015 The Neyman-Pearson lemma and the Durbin-Watson test
- 07-2015 Detecting harmful collinearity
- 07-2015 What to Do?
- 07-2015 Methods for introducing exact nonsample information
- 07-2015 Methods for introducing inexact nonsample information
- 07-2015 Estimation methods designed specifically for collinear data
- 07-2015 Artificial orthogonalization
- 07-2015 Nonlinear Models
- 07-2015 Collinearity in nonlinear regression models
- 07-2015 Collinearity in maximum likelihood estimation
- 07-2015 Closing Remarks
- 07-2015 Nonnested Hypothesis. Testing: An Overview
- 07-2015 Examples of Nonnested Models
- 07-2015 Model Selection Versus Hypothesis Testing
- 07-2015 Alternative Approaches to Testing Nonnested Hypotheses
- 07-2015 Motivation for nonnested statistics
- 07-2015 The Cox procedure
- 07-2015 The comprehensive approach
- 07-2015 The encompassing approach
- 07-2015 Power and finite sample properties
- 07-2015 Measures of Closeness and Vuong's Approach
- 07-2015 Practical Problems
- 07-2015 Resampling the likelihood ratio statistic: bootstrap methods
- 07-2015 Spatial Econometrics
- 07-2015 Spatial autocorrelation
- 07-2015 Spatial stochastic process models
- 07-2015 Direct representation
- 07-2015 Aymptotics in spatial stochastic processes
- 07-2015 Spatial Regression Models
- 07-2015 Spatial dependence in panel data models
- 07-2015 Spatial dependence in models for qualitative data
- 07-2015 Estimation
- 07-2015 Spatial two-stage least squares
- 07-2015 Method of moments estimators
- 07-2015 Specification Tests
- 07-2015 Implementation Issues
- 07-2015 Essentials of Count. Data Regression
- 07-2015 Poisson Regression
- 07-2015 Interpretation of regression coefficients
- 07-2015 Truncation and censoring
- 07-2015 Overdispersion
- 07-2015 Other Parametric Count Regression Models
- 07-2015 Continuous mixture models
- 07-2015 Finite mixture models
- 07-2015 Modified count models
- 07-2015 Discrete choice models
- 07-2015 Partially Parametric Models
- 07-2015 Least squares estimation
- 07-2015 Semiparametric models
- 07-2015 Time Series, Multivariate and Panel Data
- 07-2015 Multivariate data
- 07-2015 Practical Considerations
- 07-2015 Further Reading
- 07-2015 Panel Data Models
- 07-2015 Linear Models
- 07-2015 Dynamic Models
- 07-2015 Sample Attrition and Sample Selection
- 07-2015 Qualitative Response. Models
- 07-2015 Binary and Multinomial Response Models
- 07-2015 Panel Data with Qualitative Variables
- 07-2015 Semiparametric Estimation
- 07-2015 Simulation Methods
- 07-2015 Self-Selection
- 07-2015 Sample Selection Bias
- 07-2015 Some conventional sample selection models
- 07-2015 Parametric Estimation
- 07-2015 Polychotomous choice sample selection models
- 07-2015 Simulation estimation
- 07-2015 Estimation of simultaneous equation sample selection model
- 07-2015 Misspecification and tests
- 07-2015 Semiparametric and Nonparametric Approaches
- 07-2015 Semiparametric efficiency bound and semiparametric MLE
- 07-2015 Semiparametric IV estimation and conditional moments restrictions
- 07-2015 Estimation of the intercept
- 07-2015 Sample selection models with a tobit selection rule
- 07-2015 Identification and estimation of counterfactual outcomes
- 07-2015 Random Coefficient. Models
- 07-2015 Some First-Generation RCMs
- 07-2015 Second-Generation RCMs
- 07-2015 Criteria for Choosing Concomitants in RCMs
- 07-2015 An Empirical Example
- 07-2015 Serial Correlation
- 07-2015 The Box-Jenkins class of models
- 07-2015 Serial correlation in the disturbances of the linear regression model
- 07-2015 Maximum likelihood estimation
- 07-2015 Maximum marginal likelihood estimation
- 07-2015 Hypothesis Testing
- 07-2015 Testing disturbances in the dynamic linear regression model
- 07-2015 Model Selection
- 07-2015 Heteroskedasticity
- 07-2015 Sampling Theory Inference with Known Covariance Matrix
- 07-2015 Sampling Theory Estimation and Inference with Unknown Covariance Matrix
- 07-2015 Testing for heteroskedasticity
- 07-2015 Other extensions
- 07-2015 Concluding Remarks
- 07-2015 Seemingly Unrelated. Regression
- 07-2015 Basic Model
- 07-2015 Stochastic Specification
- 07-2015 Testing linear restrictions
- 07-2015 Diagnostic testing
- 07-2015 Other Developments
- 07-2015 Computational matters
- 07-2015 Bayesian methods
- 07-2015 Improved estimation
- 07-2015 Simultaneous. Equation Model. Estimators: Statistical. Properties and. Practical Implications
- 07-2015 Limited-Information Estimators of Structural Parameters
- 07-2015 Full Information Methods
- 07-2015 Large sample properties of limited information estimators
- 07-2015 Large sample properties of full information estimators
- 07-2015 Structure of Limited Information Estimators as Regression Functions
- 07-2015 Finite Sample Properties of Estimators
- 07-2015 Practical Implications
- 07-2015 Identification in Parametric Models
- 07-2015 Basic Concepts
- 07-2015 The Jacobian Matrix Criterion
- 07-2015 Prior Information
- 07-2015 Handling the Rank in Practice
- 07-2015 The Classical Simultaneous Equations Model
- 07-2015 Measurement Error. and Latent Variables
- 07-2015 The Linear Regression Model with Measurement Error
- 07-2015 Inconsistency and bias of the OLS estimators
- 07-2015 Bounds on the parameters
- 07-2015 Solutions to the Measurement Error Problem
- 07-2015 Instrumental variables
- 07-2015 Panel data
- 07-2015 Latent Variable Models
- 07-2015 Factor analysis
- 07-2015 MIMIC and reduced rank regression
- 07-2015 General linear structural equation models
- 07-2015 Diagnostic Testing in Cross Section Contexts
- 07-2015 Diagnostic testing in nonlinear models of conditional means
- 07-2015 Diagnostic Testing in Time Series Contexts
- 07-2015 Omnibus tests on the errors in time series regression models
- 07-2015 Basic Elements of. Asymptotic Theory
- 07-2015 Modes of Convergence for Sequences of Random Vectors
- 07-2015 Convergence in distribution
- 07-2015 Convergence properties and transformations
- 07-2015 Orders of magnitude
- 07-2015 Independent processes
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