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|a Judge, George G.,
|e author.
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|a An information theoretic approach to econometrics /
|c George G. Judge, Ron C. Mittelhammer.
|
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|a Cambridge ;
|a New York :
|b Cambridge University Press,
|c 2012.
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300 |
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|a 1 online resource (xvi, 232 pages) :
|b illustrations
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a Includes bibliographical references and index.
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0 |
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|a Print version record.
|
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0 |
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|6 880-01
|a Econometric Information Recovery -- TRADITIONAL PARAMETRIC AND SEMIPARAMETRIC ECONOMETRIC MODELS: ESTIMATION AND INFERENCE -- Formulation and Analysis of Parametric and Semiparametric Linear Models -- Method of Moments, Generalized Method of Moments, and Estimating Equations -- FORMULATION AND SOLUTION OF STOCHASTIC INVERSE PROBLEMS -- A Stochastic-Empirical Likelihood Inverse Problem: Formulation and Estimation -- A Stochastic Empirical Likelihood Inverse Problem: Estimation and Inference -- Kullback-Leibler Information and the Maximum Empirical Exponential Likelihood -- A FAMILY OF MINIMUM DISCREPANCY ESTIMATORS -- The Cressie-Read Family of Divergence Measures and Empirical Maximum Likelihood Functions -- Cressie-Read-MPD-Type Estimators in Practice: Monte Carlo Evidence of Estimation and Inference Sampling Performance -- BINARY-DISCRETE CHOICE MINIMUM POWER DIVERGENCE (MPD) MEASURES -- Family of MPD Distribution Functions for the Binary Response-Choice Model -- Estimation and Inference for the Binary Response Model Based on the MPD Family of Distributions -- OPTIMAL CONVEX DIVERGENCE -- Choosing the Optimal Divergence under Quadratic Loss -- Epilogue.
|
520 |
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|a "This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic models and methods. Because most data are observational, practitioners work with indirect noisy observation and ill-posed econometric in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of pwer divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-models problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family"--
|c Provided by publisher.
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|a English.
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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|a Econometrics.
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|a Economics.
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|a Economics
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|a Économétrie.
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|a Économie politique.
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|a economics.
|2 aat
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|a BUSINESS & ECONOMICS
|x Econometrics.
|2 bisacsh
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|a BUSINESS & ECONOMICS
|x Statistics.
|2 bisacsh
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|a Econometrics
|2 fast
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|a Economics
|2 fast
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|a Ökonometrie
|2 gnd
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|a Informationstheorie
|2 gnd
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700 |
1 |
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|a Mittelhammer, Ron
|q (Ronald Carl),
|d 1950-
|e author.
|
776 |
0 |
8 |
|i Print version:
|a Judge, George G.
|t Information theoretic approach to econometrics.
|d Cambridge ; New York : Cambridge University Press, 2012
|z 9780521869591
|w (DLC) 2011018358
|w (OCoLC)720261347
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|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=432727
|z Texto completo
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|6 505-00/(S
|g Machine generated contents note:
|g 1.
|t Econometric Information Recovery --
|g 1.1.
|t Book Objectives and Problem Format --
|g 1.2.
|t Organization of the Book --
|g 1.3.
|t Selected References --
|g pt. I
|t TRADITIONAL PARAMETRIC AND SEMIPARAMETRIC ECONOMETRIC MODELS: ESTIMATION AND INFERENCE --
|g 2.
|t Formulation and Analysis of Parametric and Semiparametric Linear Models --
|g 2.1.
|t Data Sampling Processes (DSPs) and Notation --
|g 2.2.
|t Parametric General Linear Model --
|g 2.2.1.
|t Parametric Model and Maximum Likelihood (ML) Estimation of β and σ2 --
|g 2.2.2.
|t Parametric Model and Inference --
|g 2.3.
|t Semiparametric General Linear Model --
|g 2.3.1.
|t Squared Error Metric and the Least Squares (LS) Principle --
|g 2.3.2.
|t LS Estimator --
|g 2.3.3.
|t Finite Sample Statistical Properties of the LS Estimator --
|g 2.3.4.
|t Consistency and Asymptotic Normality of the LS Estimator --
|g 2.3.5.
|t Linear Semiparametric Model Inference --
|g 2.3.6.
|t Inferential Asymptotics --
|g 2.3.7.
|t Hypothesis Testing: Linear Equality Restrictions on β --
|g 2.4.
|t General Linear Model with Stochastic X --
|g 2.4.1.
|t Linear Model Assumptions --
|g 2.4.2.
|t LS Estimator Properties: Finite Samples --
|g 2.4.3.
|t LS Estimator Properties: Asymptotics --
|g 2.4.4.
|t ML Estimation of β and σ2 under Conditional Normality --
|g 2.4.5.
|t Hypothesis Testing and Confidence Region Estimation --
|g 2.4.5a.
|t Semiparametric Case --
|g 2.4.5b.
|t Parametric Case --
|g 2.4.6.
|t Summary: Statistical Implications of Stochastic X --
|g 2.5.
|t Extremum (E) Estimation and Inference --
|g 2.5.1.
|t ML and LS Estimators Expressed in E Estimator Form --
|g 2.5.2.
|t Asymptotic Properties of E Estimators --
|g 2.5.3.
|t Inference Based on E Estimation --
|g 2.5.4.
|t Summary and Forward: E Estimators --
|g 2.6.
|t Selected References --
|g 3.
|t Method of Moments, Generalized Method of Moments, and Estimating Equations --
|g 3.1.
|t Introduction --
|g 3.1.1.
|t Just-Determined Moment System with Random Sampling of Scalars --
|g 3.2.
|t Just-Determined Moment Systems, Random Sampling, and Method of Moments (MOM) --
|g 3.2.1.
|t General Asymptotic Properties --
|g 3.2.2.
|t Linear Model Semiparametric Estimation through Moment Equations --
|g 3.2.3.
|t MOM Conclusions --
|g 3.3.
|t Generalized Method of Moments (GMM) --
|g 3.3.1.
|t GMM Framework --
|g 3.3.2.
|t GMM Linear Model Estimation --
|g 3.3.2a.
|t Optimal GMM Weight Matrix --
|g 3.3.2b.
|t Sampling Properties of Estimated Optimal GMM (EOGMM) Estimator --
|g 3.3.2c.
|t Hypothesis Testing and Confidence Regions --
|g 3.3.2d.
|t Additional Properties of the GMM Approach --
|g 3.3.2e.
|t Summary and Forward: The GMM Approach --
|g 3.4.
|t Estimating Equations --
|g 3.4.1.
|t Duality between Estimating Equations (EEs) and E Estimators --
|g 3.4.2.
|t Linear Estimating Functions (EFs) --
|g 3.4.3.
|t Optimal Unbiased EFs --
|g 3.4.3a.
|t Unbiasedness --
|g 3.4.3b.
|t Optimal Estimating Functions (OptEFs): The Scalar Case --
|g 3.4.3c.
|t OptEFs: The Multivariate Case --
|g 3.4.4.
|t Inference in the Context of EE Estimation --
|g 3.4.4a.
|t Wald (W) and Z Tests and Confidence Regions --
|g 3.4.4b.
|t Generalized Score (Lagrange Multiplier-Type) Tests and Confidence Regions --
|g 3.4.4.c.
|t Pseudo-Likelihood Ratio Tests and Confidence Regions --
|g 3.5.
|t E Estimation with Instrumental Variables --
|g 3.6.
|t Summary and Forward --
|g 3.7.
|t Selected References --
|g pt. II
|t FORMULATION AND SOLUTION OF STOCHASTIC INVERSE PROBLEMS --
|g 4.
|t Stochastic-Empirical Likelihood Inverse Problem: Formulation and Estimation --
|g 4.1.
|t Introduction --
|g 4.2.
|t Stochastic Linear Inverse Problem --
|g 4.2.1.
|t Addressing the Indeterminacy of Unknowns --
|g 4.3.
|t Nonparametric ML Solutions to Inverse Problems --
|g 4.3.1.
|t Nonparametric ML --
|g 4.3.2.
|t Empirical Likelihood (EL) Function for θ --
|g 4.3.3.
|t Comparing the Use of Estimating Functions in EE and EL Contexts --
|g 4.3.4.
|t Functional Form of the EL Function --
|g 4.3.5.
|t Summary of the EL Concept --
|g 4.3.6.
|t Maximum Empirical Likelihood (MEL) Estimation of a Population Mean --
|g 4.3.7.
|t MEL Linear Model Estimation for Stochastic X --
|g 4.4.
|t Epilogue --
|g 4.5.
|t Selected References --
|g Appendix 4
|t Numerical Example: Computing MEL Estimates --
|g 5.
|t Stochastic Empirical Likelihood Inverse Problem: Estimation and Inference --
|g 5.1.
|t Introduction --
|g 5.2.
|t MEL Inference: iid Case --
|g 5.2.1.
|t MEL Efficiency Property --
|g 5.3.
|t Empirical Example of MEL Estimation Based on Two Moments --
|g 5.4.
|t Hypothesis Tests and Confidence Regions: iid Case --
|g 5.4.1.
|t Empirical Likelihood Ratio Tests and Confidence Regions for c(θ) --
|g 5.4.2.
|t Wald Tests and Confidence Regions for c(θ) --
|g 5.4.3.
|t Lagrange Multiplier Tests and Confidence Regions for c(θ) --
|g 5.4.4.
|t Z-Test of Inequality Hypotheses for the Value of c(θ) --
|g 5.4.5.
|t Testing the Validity of Moment Equations --
|g 5.4.6.
|t MEL Testing and Confidence Intervals for Population Mean --
|g 5.4.7.
|t Illustrative MEL Confidence Interval Example --
|g 5.5.
|t Concluding Comments --
|g 5.6.
|t Selected References --
|g 6.
|t Kullback-Leibler Information and the Maximum Empirical Exponential Likelihood --
|g 6.1.
|t Introduction --
|g 6.1.1.
|t Solutions to Systems of Estimating Equations and Kullback-Leibler Information --
|g 6.2.
|t Kullback-Leibler Information Criterion (KLIC) --
|g 6.2.1.
|t Relationship between Maximum Empirical Exponential Likelihood (MEEL) and KL Information --
|g 6.2.1a.
|t Objective of MEEL --
|g 6.3.
|t General MEEL Alternative Empirical Likelihood Formulation --
|g 6.3.1.
|t MEEL Estimator and Alternative Empirical Likelihood --
|g 6.3.2.
|t MEEL Asymptotics --
|g 6.3.3.
|t MEEL Inference --
|g 6.3.3a.
|t Testing H0 : c(θ)=r --
|g 6.3.3b.
|t Testing H0 : c(θ) [≤] r or H0 : c(θ) [≥] r --
|g 6.3.3c.
|t Testing the Validity of Moment Equations --
|g 6.3.3d.
|t Confidence Regions --
|g 6.3.4.
|t Contrasting the Use of Estimating Functions in EE and MEEL Contexts --
|g 6.4.
|t Combining Estimation Equations under Kullback-Leibler Loss --
|g 6.4.1.
|t Combating Model Uncertainty: General Combining Formulations --
|g 6.4.2.
|t Example: A Combined Estimator --
|g 6.4.2a.
|t Finite Sample Performance --
|g 6.4.2b.
|t Implications --
|g 6.5.
|t Informative Reference Distribution --
|g 6.6.
|t Concluding Remarks --
|g 6.7.
|t Reader Idea Checklist --
|g 6.8.
|t Selected References --
|g Appendix 6.A
|t Relationship between the Maximum Empirical Likelihood (MEL) Objective and KL Information --
|g Appendix 6.B
|t Numerical Illustration of MEEL and MEL Estimation of a Probability Distribution --
|g Appendix 6.C
|t Shannon's Entropy -- Some Historical Perspective --
|g pt. III
|t FAMILY OF MINIMUM DISCREPANCY ESTIMATORS --
|g 7.
|t Cressie-Read Family of Divergence Measures and Empirical Maximum Likelihood Functions --
|g 7.1.
|t Introduction --
|g 7.1.1.
|t Family of Likelihood Functions --
|g 7.2.
|t Cressie-Read (CR) Power Divergence Family --
|g 7.3.
|t Three Main Variants of I(p, q, y) --
|g 7.4.
|t Minimum Power Divergence and Empirical Maximum Likelihood (EML) Estimation --
|g 7.5.
|t Inference --
|g 7.5.1.
|t Test Statistics --
|g 7.5.1a.
|t Moment Validity Tests --
|g 7.5.1b.
|t Tests of Parameter Restrictions --
|g 7.6.
|t Concluding Remarks --
|g 7.7.
|t Selected References --
|g Appendix 7.A
|t Propositions, Proofs, and Definitions --
|g Appendix 7.B
|t Entropy Families --
|g 8.
|t Cressie-Read-MPD-Type Estimators in Practice: Monte Carlo Evidence of Estimation and Inference Sampling Performance --
|g 8.1.
|t Introduction --
|g 8.2.
|t Design of Sampling Experiments --
|g 8.3.
|t Sampling Results --
|g 8.3.1.
|t Estimator MSE Performance --
|g 8.3.2.
|t Bias and Variance --
|g 8.3.3.
|t Prediction MSE --
|g 8.3.4.
|t Size of Moment Validity Tests --
|g 8.3.5.
|t Confidence Interval Coverage and Expected Length --
|g 8.3.6.
|t Test Power --
|g 8.4.
|t Summary Comments --
|g 8.5.
|t Selected References --
|g Appendix 8.A
|t Computational Issues and Numerical Approach --
|g pt.
|
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|6 505-00/(S
|t IV
|t BINARY-DISCRETE CHOICE MINIMUM POWER DIVERGENCE (MPD) MEASURES --
|g 9.
|t Family of MPD Distribution Functions for the Binary Response-Choice Model --
|g 9.1.
|t Introduction --
|g 9.2.
|t Statistical Model Base --
|g 9.2.1.
|t Parametric --
|g 9.2.2.
|t Nonparametric Stochastic Inverse Problem Representation --
|g 9.3.
|t Minimum Power Divergence Class of CDFs for the Binary Response Model --
|g 9.3.1.
|t Applying MPD to Conditional Bernoulli Probabilities --
|g 9.3.2.
|t Conditional Reference Probabilities --
|g 9.3.3.
|t Class of CDFs Underlying p --
|g 9.3.4.
|t Properties of the MPD Class of Probability Distribution Functions --
|g 9.3.4a.
|t Moments --
|g 9.3.4b.
|t Concavity of ln(F(w; q, y)) and ln(1-F(w;q, y)) in w --
|g 9.4.
|t Summary and Extensions --
|g 9.5.
|t Selected References --
|g Appendix 9.A
|t Additional Properties of MPD Distributions --
|g 10.
|t Estimation and Inference for the Binary Response Model Based on the MPD Family of Distributions --
|g 10.1.
|t Introduction --
|g 10.2.
|t MPD Solutions for p and λ as Estimators in Binary Response Models --
|g 10.2.1.
|t Interpreting λ as an Estimator of β --
|g 10.2.2.
|t Estimating the Marginal Probability Effects of Changes in Response Variables --
|g 10.3.
|t Asymptotic Estimator Properties --
|g 10.3.1.
|t Asymptotic Inference --
|g 10.4.
|t Estimation Alternatives --
|g 10.4.1.
|t EML Estimation --
|g 10.4.2.
|t NLS-MPD Estimation --
|g 10.5.
|t Sampling Performance --
|g 10.5.1.
|t Sampling Design --
|g 10.5.2.
|t Sampling Results --
|g 10.6.
|t Summary and Extensions --
|g 10.7.
|t Selected References --
|g Appendix 10.A
|t Asymptotic Properties of MPD(q, γ)-Applicability of Assumptions --
|g Appendix 10.B
|t Consistency --
|g Appendix 10.C
|t Asymptotic Normality --
|g pt. V
|t OPTIMAL CONVEX DIVERGENCE --
|g 11.
|t Choosing the Optimal Divergence under Quadratic Loss --
|g 11.1.
|t Introduction --
|g 11.2.
|t Econometric Model and the Cressie-Read (CR) Family --
|g 11.3.
|t Choosing a Minimum Loss Estimation Rule --
|g 11.3.1.
|t Distance-Divergence Measures --
|g 11.3.2.
|t Minimum Quadratic Risk Estimation Rule --
|g 11.3.3.
|t Case of Two CR Alternatives --
|g 11.3.4.
|t Empirical Calculation of α --
|g 11.4.
|t Finite Sample Implication.
|
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|6 505-01/(S
|g Contents note continued:
|g 11.5.
|t Estimator Choice, γ = (1, 0, -1) --
|g 11.6.
|t Sampling Performance --
|g 11.7.
|t Concluding Remarks --
|g 11.8.
|t Selected References --
|g Appendix 11.A
|t γ = (0, -- 1) Special Case Convex Estimation Rule --
|g 12.
|t Epilogue.
|
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