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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 | |