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Preface | |

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Foreword | |

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Production Function and Regression Methods Using R | |

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R and Microeconometric Preliminaries | |

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Data on Metals Production Available in R | |

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Descriptive Statistics Using R | |

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Writing Skewness and Kurtosis Functions in R | |

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Units of Measurement and Numerical Reliability of Regressions | |

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Basic Graphics in R | |

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The Isoquant | |

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Total Productivity of an Input | |

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The Marginal Productivity (MP) of an Input | |

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Slope of the Isoquant and MRTS | |

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Scale Elasticity as the Returns to Scale Parameter | |

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Elasticity of Substitution | |

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Typical Steps in Empirical Work | |

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Preliminary Regression Theory: Results Using R | |

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Regression as an Object `reg1' in R | |

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Accessing Objects Within an R Object by Using the Dollar Symbol | |

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Deeper Regression Theory: Diagonals of the Hat Matrix | |

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Discussion of Four Diagnostic Plots by R | |

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Testing Constant Returns and 3D Scatter Plots | |

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Homothetic Production and Cost Functions | |

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Euler Theorem and Duality Theorem | |

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Profit Maximizing Solutions | |

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Elasticity of Total Cost w.r.t. Output | |

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Miscellaneous Microeconomic Topics | |

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Analytic Input Demand Function for the Cobb-Douglas Form | |

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Separability in the Presence of Three or More Inputs | |

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Two or More Outputs as Joint Outputs | |

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Economies of Scope | |

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Nonhomogeneous Production Functions | |

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Three-Input Production Function for Widgets | |

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Isoquant Plotting for a Bell System Production Function | |

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Collinearity Problem, Singular Value Decomposition (SVD), and Ridge Regression | |

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What is Collinearity? | |

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Consequences of Near Collinearity | |

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Regression Theory Using the Singular Value Decomposition | |

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Near Collinearity Solutions by Coefficient Shrinkage | |

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Ridge Regression | |

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Principal Components Regression | |

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Bell System Production Function in Anti-Trust Trial | |

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Collinearity Diagnostics for Bell Data Trans-Log | |

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Shrinkage Solution and Ridge Regression for Bell Data | |

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Ridge Regression from Existing R Packages | |

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Comments on Wrong Signs, Collinearity, and Ridge Scaling | |

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Concluding Comments on the 1982 Bell System Breakup | |

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Data Appendix | |

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Univariate Time Series Analysis with R | |

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Econometric Univariate Time Series are Ubiquitous | |

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Stochastic Difference Equations | |

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Second-Order Stochastic Difference Equation and Business Cycles | |

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Complex Number Solution of the Stochastic AR(2) Difference Equation | |

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General Solution to ARMA (p,p - 1) Stochastic Difference Equations | |

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Properties of ARIMA Models | |

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Identification of the Lag Order | |

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ARIMA Estimation | |

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ARIMA Diagnostic Checking | |

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Stochastic Process and Stationarity | |

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Stochastic Process and Underlying Probability Space | |

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Autocovariance of a Stochastic Process and Ergodicity | |

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Stationary Process | |

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Detrending and Differencing to Achieve Stationarity | |

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Mean Reversion | |

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Autocovariance Generating Functions (AGF) and the Power Spectrum | |

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How to Get the Power Spectrum from the AGF? | |

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Explicit Modeling of Variance (ARCH, GARCH Models.) | |

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Tests of Independence, Neglected Nonlinearity, Turning Points | |

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Long Memory Models and Fractional Differencing | |

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Forecasting | |

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Concluding Remarks and Examples | |

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Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration | |

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Autoregressive Distributed Lag (ARDL) Models | |

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Economic Interpretations of ARDL(1,1) Model | |

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Description of M1 to M11 Model Specifications | |

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ARDL(0,q) as M12 Model, Impact and Long-Run Multipliers | |

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Adaptive Expectations Model to Test Rational Expectations Hypothesis | |

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Statistical Inference and Estimation with Lagged-Dependent Variables | |

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Identification Problems Involving Expectational Variables (I. Fisher Example) | |

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Impulse Response, Mean Lag and Insights from a Polynomials in L | |

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Choice Between M1 to M11 Models Using R | |

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Stochastic Diffusion Models for Asset Prices | |

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Spurious Regression (R2 > Durbin Watson) and Cointegration | |

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Definition of a Process Integrated of Order d, I(d) | |

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Cointegration Definition and Discussion | |

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Error Correction Models of Cointegration | |

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Economic Equilibria and Error Reductions through Learning | |

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Signs and Significance of Coefficients on Past Errors while Agents Learn | |

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Granger Causality Testing | |

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Utility Theory and Empirical Implications | |

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Utility Theory | |

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Expected Utility Theory (EUT) | |

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Arrow-Pratt Coefficient of Absolute Risk Aversion (CARA) | |

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Risk Premium Needed to Encourage Risky Investments | |

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Taylor Series Links EUT, Moments of f(x) and Derivatives of U(x) | |

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Non-Expected Utility Theory | |

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Lorenz Curve Scaling over the Unit Square | |

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Mapping From EUT to Non-EUT within the Unit Square to Get Decision Weights | |

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Incorporating Utility Theory into Risk Measurement and Stochastic Dominance | |

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Class D1 of Utility Functions and Investors | |

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Class D2 of Utility Functions and Investors | |

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Explicit Utility Functions and Arrow-Pratt Measures of Risk Aversion | |

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Class D3 of Utility Functions and Investors | |

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Class D4 of Utility Functions and Investors | |

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First-Order Stochastic Dominance (1SD) | |

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Second-Order Stochastic Dominance (2SD) | |

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Third-Order Stochastic Dominance (3SD) | |

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Fourth-Order Stochastic Dominance (4SD) | |

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Empirical Checking of Stochastic Dominance Using Matrix Multiplications and Incorporation of 4DPs of Non-EUT | |

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Vector Models for Multivariate Problems | |

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Introduction and VAR Models | |

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Some R Packages for Vector Modeling | |

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Vector Autoregression or VAR Models | |

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Data Collection Tips Using R | |

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VAR Estimation of Sims' Model | |

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Granger-Causality Analysis in VAR Models | |

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Forecasting Out-of-Sample in VAR Models | |

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Impulse Response Analysis in VAR Models | |

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Multivariate Regressions: Canonical Correlations | |

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Why Canonical Correlation is Not Popular So Far | |

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VAR Estimation and Cointegration Testing Using Canonical Correlations | |

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Final Remarks: Multivariate Statisics Using R | |

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Simultaneous Equation Models | |

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Introduction | |

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Simultaneous Equation Notation System with Stars and Subscripts | |

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Simultaneous Equations Bias and the Reduced Form | |

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Successively Weaker Assumptions Regarding the Nature of the Zj Matrix of Regressors | |

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Reduced Form Estimation and Other Alternatives to OLS | |

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Assumptions of Simultaneous Equations Models | |

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Instrumental Variables and Generalized Least Squares | |

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The Instrumental Variables (IV) and Generalized IV (GIV) Estimator | |

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Choice Between OLS and IV by Using Wu-Hausman Specification Test | |

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Limited Information and Two-Stage Least Squares | |

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Two-Stage Least Squares | |

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The k-class Estimator | |

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Limited Information Maximum Likelihood (LIML) Estimator | |

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Identification of Simultaneous Equation Models | |

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Identification is Uniquely Going from the Reduced Form to the Structure | |

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Full Information and Three-Stage Least Squares (3SLS) | |

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Full Information Maximum Likelihood | |

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Potential of Simultaneous Equations Beyond Econometrics | |

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Limited Dependent Variable (GLM) Models | |

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Problems with Dummy Dependent Variables | |

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Proof of the Claim that Var(e<$$$>[Page No. xxiv]i) = Pi(1 - Pi) | |

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The General Linear Model from Biostatistics | |

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Marginal Effects (Partial Derivatives) in Logit-Type GLM Models | |

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Further Generalizations of Logit and Probit Models | |

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Ordered Response | |

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Quasi-Likelihood Function for Binary Choice Models | |

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The ML Estimator in Binary Choice Models | |

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Tobit Model for Censored Dependent Variables | |

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Heckman Two-Step Estimator for Self-Selection Bias | |

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Time Duration Length (Survival) Models | |

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Probability Distributions and Implied Hazard Functions | |

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Parametric Survival (Hazard) Models | |

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Semiparametric Including Cox Proportional Hazard Models | |

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Dynamic Optimization and Empirical Analysis of Consumer Behavior | |

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Introduction | |

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Dynamic Optimization | |

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Hall's Random Walk Model | |

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Data from the Internet and an Implementation | |

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OLS Estimation of the Random Walk Model | |

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Direct Estimation of Hall's NLHS Specification | |

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Strong Assumptions and Granger-Causality Tests | |

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Nonparametric Kernel Estimation | |

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Kernel Estimation of Amorphous Partials | |

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Wiener-Hopf-Whittle Model if Consumption Precedes Income | |

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Determination of Target Consumption | |

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Implications for Various Puzzles of Consumer Theory | |

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Final Remarks on Consumer Theory | |

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Appendix: Additional R Code | |

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Single, Double and Maximum Entropy Bootstrap and Inference | |

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The Motivation and Background Behind Bootstrapping | |

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Pivotal Quantity and p-Value | |

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Uncertainty Regarding Proper Density for Regression Errors Illustrated | |

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The Delta Method for Standard Error of Functions | |

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Description of Parametric iid Bootstrap | |

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Simulated Sampling Distribution for Statistical Inference Using OLS Residuals | |

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Steps in a Parametric Approximation | |

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Percentile Confidence Intervals | |

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Reflected Percentile Confidence Interval for Bias Correction | |

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Significance Tests as Duals to Confidence Intervals | |

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Description of Nonparametric iid Bootstrap | |

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Map Data from Time-Domain to (Numerical Magnitudes) Values-Domain | |

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Double Bootstrap Illustrated with a Nonlinear Model | |

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A Digression on the Size of Resamples | |

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Double Bootstrap Theory Involving Roots and Uniform Density | |

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GNR Implementation of Nonlinear Regression for Metals Data | |

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Maximum Entropy Density Bootstrap for Time-Series Data | |

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Wiener, Kolmogorov, Khintchine (WKK) Ensemble of Time Series | |

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Avoiding Unrealistic Properties of iid Bootstrap | |

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Maximum Entropy Density is Uniform When Limits are Known | |

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Quantiles of the Patchwork of the ME Density | |

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Numerical Illustration of "Meboot" Package in R | |

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Simple and Size-Corrected Confidence Bounds | |

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Generalized Least Squares, VARMA, and Estimating Functions | |

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Feasible Generalized Least Squares (GLS) to Adjust for Autocorrelated Errors and/or Heteroscedasticity | |

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Consequences of Ignoring Nonspherical Errors O ≠<$$$>[Page No. xxvi] IT | |

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Derivation of the GLS and Efficiency Comparison | |

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Computation of the GLS and Feasible GLS | |

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Improved OLS Inference for Nonspherical Errors | |

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Efficient Estimation of b<$$$>[Page No. xxvi] Coefficients | |

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An Illustration Using Fisher's Model for Interest Rates | |

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Vector ARMA Estimation for Rational Expectations Models | |

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Greater Realism of VARMA(p,q) Models | |

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Expectational Variables from Conditional Forecasts in a General Model | |

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A Rational Expectation Model Using VARMA | |

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Further Forecasts, Transfer Function Gains, and Response Analysis | |

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Optimal Estimating Function (OptEF) and Generalized Method of Moments (GMM) | |

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Derivation of Optimal Estimating Functions for Regressions | |

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Finite Sample Optimality of OptEF | |

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Introduction to the GMM | |

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Cases Where OptEF Viewpoint Dominates GMM | |

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Advantages and Disadvantages of GMM and OptEF | |

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Godambe Pivot Functions (GPFs) and Statistical Inference | |

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Application of the Frisch-Waugh Theorem to Constructing CI95 | |

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Steps in Application of GPF to Feasible GLS Estimation | |

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Box-Cox, Loess and Projection Pursuit Regression | |

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Further R Tools for Studying Nonlinear Relations | |

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Box-Cox Transformation | |

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Logarithmic and Square Root Transformations | |

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Scatterplot Smoothing and Loess Regressions | |

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Improved Fit (Forecasts) by Loess Smoothing | |

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Projection Pursuit Methods | |

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Remarks on Nonlinear Econometrics | |

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Appendix | |

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References | |

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Index | |