| Preface | p. vii |
| Foreword | p. xv |
| Production Function and Regression Methods Using R | p. 1 |
| R and Microeconometric Preliminaries | p. 2 |
| Data on Metals Production Available in R | p. 3 |
| Descriptive Statistics Using R | p. 4 |
| Writing Skewness and Kurtosis Functions in R | p. 5 |
| Units of Measurement and Numerical Reliability of Regressions | p. 6 |
| Basic Graphics in R | p. 7 |
| The Isoquant | p. 8 |
| Total Productivity of an Input | p. 9 |
| The Marginal Productivity (MP) of an Input | p. 9 |
| Slope of the Isoquant and MRTS | p. 9 |
| Scale Elasticity as the Returns to Scale Parameter | p. 11 |
| Elasticity of Substitution | p. 12 |
| Typical Steps in Empirical Work | p. 13 |
| Preliminary Regression Theory: Results Using R | p. 13 |
| Regression as an Object `reg1' in R | p. 16 |
| Accessing Objects Within an R Object by Using the Dollar Symbol | p. 17 |
| Deeper Regression Theory: Diagonals of the Hat Matrix | p. 18 |
| Discussion of Four Diagnostic Plots by R | p. 20 |
| Testing Constant Returns and 3D Scatter Plots | p. 23 |
| Homothetic Production and Cost Functions | p. 26 |
| Euler Theorem and Duality Theorem | p. 29 |
| Profit Maximizing Solutions | p. 30 |
| Elasticity of Total Cost w.r.t. Output | p. 31 |
| Miscellaneous Microeconomic Topics | p. 32 |
| Analytic Input Demand Function for the Cobb-Douglas Form | p. 32 |
| Separability in the Presence of Three or More Inputs | p. 32 |
| Two or More Outputs as Joint Outputs | p. 33 |
| Economies of Scope | p. 33 |
| Nonhomogeneous Production Functions | p. 34 |
| Three-Input Production Function for Widgets | p. 34 |
| Isoquant Plotting for a Bell System Production Function | p. 42 |
| Collinearity Problem, Singular Value Decomposition (SVD), and Ridge Regression | p. 45 |
| What is Collinearity? | p. 45 |
| Consequences of Near Collinearity | p. 48 |
| Regression Theory Using the Singular Value Decomposition | p. 51 |
| Near Collinearity Solutions by Coefficient Shrinkage | p. 55 |
| Ridge Regression | p. 57 |
| Principal Components Regression | p. 61 |
| Bell System Production Function in Anti-Trust Trial | p. 62 |
| Collinearity Diagnostics for Bell Data Trans-Log | p. 65 |
| Shrinkage Solution and Ridge Regression for Bell Data | p. 65 |
| Ridge Regression from Existing R Packages | p. 66 |
| Comments on Wrong Signs, Collinearity, and Ridge Scaling | p. 69 |
| Concluding Comments on the 1982 Bell System Breakup | p. 75 |
| Data Appendix | p. 75 |
| Univariate Time Series Analysis with R | p. 77 |
| Econometric Univariate Time Series are Ubiquitous | p. 77 |
| Stochastic Difference Equations | p. 81 |
| Second-Order Stochastic Difference Equation and Business Cycles | p. 85 |
| Complex Number Solution of the Stochastic AR(2) Difference Equation | p. 87 |
| General Solution to ARMA (p,p - 1) Stochastic Difference Equations | p. 89 |
| Properties of ARIMA Models | p. 91 |
| Identification of the Lag Order | p. 93 |
| ARIMA Estimation | p. 100 |
| ARIMA Diagnostic Checking | p. 101 |
| Stochastic Process and Stationarity | p. 108 |
| Stochastic Process and Underlying Probability Space | p. 108 |
| Autocovariance of a Stochastic Process and Ergodicity | p. 110 |
| Stationary Process | p. 112 |
| Detrending and Differencing to Achieve Stationarity | p. 117 |
| Mean Reversion | p. 129 |
| Autocovariance Generating Functions (AGF) and the Power Spectrum | p. 132 |
| How to Get the Power Spectrum from the AGF? | p. 133 |
| Explicit Modeling of Variance (ARCH, GARCH Models.) | p. 136 |
| Tests of Independence, Neglected Nonlinearity, Turning Points | p. 139 |
| Long Memory Models and Fractional Differencing | p. 143 |
| Forecasting | p. 147 |
| Concluding Remarks and Examples | p. 150 |
| Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration | p. 153 |
| Autoregressive Distributed Lag (ARDL) Models | p. 153 |
| Economic Interpretations of ARDL(1,1) Model | p. 161 |
| Description of M1 to M11 Model Specifications | p. 162 |
| ARDL(0,q) as M12 Model, Impact and Long-Run Multipliers | p. 166 |
| Adaptive Expectations Model to Test Rational Expectations Hypothesis | p. 167 |
| Statistical Inference and Estimation with Lagged-Dependent Variables | p. 168 |
| Identification Problems Involving Expectational Variables (I. Fisher Example) | p. 168 |
| Impulse Response, Mean Lag and Insights from a Polynomials in L | p. 169 |
| Choice Between M1 to M11 Models Using R | p. 170 |
| Stochastic Diffusion Models for Asset Prices | p. 176 |
| Spurious Regression (R2 > Durbin Watson) and Cointegration | p. 183 |
| Definition of a Process Integrated of Order d, I(d) | p. 183 |
| Cointegration Definition and Discussion | p. 184 |
| Error Correction Models of Cointegration | p. 185 |
| Economic Equilibria and Error Reductions through Learning | p. 186 |
| Signs and Significance of Coefficients on Past Errors while Agents Learn | p. 187 |
| Granger Causality Testing | p. 189 |
| Utility Theory and Empirical Implications | p. 191 |
| Utility Theory | p. 191 |
| Expected Utility Theory (EUT) | p. 192 |
| Arrow-Pratt Coefficient of Absolute Risk Aversion (CARA) | p. 197 |
| Risk Premium Needed to Encourage Risky Investments | p. 199 |
| Taylor Series Links EUT, Moments of f(x) and Derivatives of U(x) | p. 200 |
| Non-Expected Utility Theory | p. 202 |
| Lorenz Curve Scaling over the Unit Square | p. 203 |
| Mapping From EUT to Non-EUT within the Unit Square to Get Decision Weights | p. 206 |
| Incorporating Utility Theory into Risk Measurement and Stochastic Dominance | p. 210 |
| Class D1 of Utility Functions and Investors | p. 210 |
| Class D2 of Utility Functions and Investors | p. 210 |
| Explicit Utility Functions and Arrow-Pratt Measures of Risk Aversion | p. 211 |
| Class D3 of Utility Functions and Investors | p. 212 |
| Class D4 of Utility Functions and Investors | p. 212 |
| First-Order Stochastic Dominance (1SD) | p. 214 |
| Second-Order Stochastic Dominance (2SD) | p. 216 |
| Third-Order Stochastic Dominance (3SD) | p. 217 |
| Fourth-Order Stochastic Dominance (4SD) | p. 218 |
| Empirical Checking of Stochastic Dominance Using Matrix Multiplications and Incorporation of 4DPs of Non-EUT | p. 218 |
| Vector Models for Multivariate Problems | p. 227 |
| Introduction and VAR Models | p. 227 |
| Some R Packages for Vector Modeling | p. 228 |
| Vector Autoregression or VAR Models | p. 228 |
| Data Collection Tips Using R | p. 229 |
| VAR Estimation of Sims' Model | p. 237 |
| Granger-Causality Analysis in VAR Models | p. 240 |
| Forecasting Out-of-Sample in VAR Models | p. 242 |
| Impulse Response Analysis in VAR Models | p. 243 |
| Multivariate Regressions: Canonical Correlations | p. 248 |
| Why Canonical Correlation is Not Popular So Far | p. 251 |
| VAR Estimation and Cointegration Testing Using Canonical Correlations | p. 257 |
| Final Remarks: Multivariate Statisics Using R | p. 259 |
| Simultaneous Equation Models | p. 261 |
| Introduction | p. 261 |
| Simultaneous Equation Notation System with Stars and Subscripts | p. 263 |
| Simultaneous Equations Bias and the Reduced Form | p. 266 |
| Successively Weaker Assumptions Regarding the Nature of the Zj Matrix of Regressors | p. 269 |
| Reduced Form Estimation and Other Alternatives to OLS | p. 269 |
| Assumptions of Simultaneous Equations Models | p. 271 |
| Instrumental Variables and Generalized Least Squares | p. 272 |
| The Instrumental Variables (IV) and Generalized IV (GIV) Estimator | p. 273 |
| Choice Between OLS and IV by Using Wu-Hausman Specification Test | p. 275 |
| Limited Information and Two-Stage Least Squares | p. 277 |
| Two-Stage Least Squares | p. 277 |
| The k-class Estimator | p. 278 |
| Limited Information Maximum Likelihood (LIML) Estimator | p. 280 |
| Identification of Simultaneous Equation Models | p. 282 |
| Identification is Uniquely Going from the Reduced Form to the Structure | p. 285 |
| Full Information and Three-Stage Least Squares (3SLS) | p. 288 |
| Full Information Maximum Likelihood | p. 293 |
| Potential of Simultaneous Equations Beyond Econometrics | p. 294 |
| Limited Dependent Variable (GLM) Models | p. 295 |
| Problems with Dummy Dependent Variables | p. 295 |
| Proof of the Claim that Var(e<$$$>[Page No. xxiv]i) = Pi(1 - Pi) | p. 300 |
| The General Linear Model from Biostatistics | p. 304 |
| Marginal Effects (Partial Derivatives) in Logit-Type GLM Models | p. 308 |
| Further Generalizations of Logit and Probit Models | p. 309 |
| Ordered Response | p. 312 |
| Quasi-Likelihood Function for Binary Choice Models | p. 314 |
| The ML Estimator in Binary Choice Models | p. 315 |
| Tobit Model for Censored Dependent Variables | p. 317 |
| Heckman Two-Step Estimator for Self-Selection Bias | p. 322 |
| Time Duration Length (Survival) Models | p. 326 |
| Probability Distributions and Implied Hazard Functions | p. 330 |
| Parametric Survival (Hazard) Models | p. 331 |
| Semiparametric Including Cox Proportional Hazard Models | p. 333 |
| Dynamic Optimization and Empirical Analysis of Consumer Behavior | p. 343 |
| Introduction | p. 343 |
| Dynamic Optimization | p. 344 |
| Hall's Random Walk Model | p. 346 |
| Data from the Internet and an Implementation | p. 349 |
| OLS Estimation of the Random Walk Model | p. 350 |
| Direct Estimation of Hall's NLHS Specification | p. 352 |
| Strong Assumptions and Granger-Causality Tests | p. 356 |
| Nonparametric Kernel Estimation | p. 358 |
| Kernel Estimation of Amorphous Partials | p. 360 |
| Wiener-Hopf-Whittle Model if Consumption Precedes Income | p. 364 |
| Determination of Target Consumption | p. 365 |
| Implications for Various Puzzles of Consumer Theory | p. 368 |
| Final Remarks on Consumer Theory | p. 369 |
| Appendix: Additional R Code | p. 370 |
| Single, Double and Maximum Entropy Bootstrap and Inference | p. 377 |
| The Motivation and Background Behind Bootstrapping | p. 377 |
| Pivotal Quantity and p-Value | p. 378 |
| Uncertainty Regarding Proper Density for Regression Errors Illustrated | p. 380 |
| The Delta Method for Standard Error of Functions | p. 382 |
| Description of Parametric iid Bootstrap | p. 383 |
| Simulated Sampling Distribution for Statistical Inference Using OLS Residuals | p. 383 |
| Steps in a Parametric Approximation | p. 386 |
| Percentile Confidence Intervals | p. 387 |
| Reflected Percentile Confidence Interval for Bias Correction | p. 388 |
| Significance Tests as Duals to Confidence Intervals | p. 388 |
| Description of Nonparametric iid Bootstrap | p. 391 |
| Map Data from Time-Domain to (Numerical Magnitudes) Values-Domain | p. 391 |
| Double Bootstrap Illustrated with a Nonlinear Model | p. 398 |
| A Digression on the Size of Resamples | p. 399 |
| Double Bootstrap Theory Involving Roots and Uniform Density | p. 399 |
| GNR Implementation of Nonlinear Regression for Metals Data | p. 401 |
| Maximum Entropy Density Bootstrap for Time-Series Data | p. 407 |
| Wiener, Kolmogorov, Khintchine (WKK) Ensemble of Time Series | p. 408 |
| Avoiding Unrealistic Properties of iid Bootstrap | p. 409 |
| Maximum Entropy Density is Uniform When Limits are Known | p. 410 |
| Quantiles of the Patchwork of the ME Density | p. 412 |
| Numerical Illustration of "Meboot" Package in R | p. 413 |
| Simple and Size-Corrected Confidence Bounds | p. 418 |
| Generalized Least Squares, VARMA, and Estimating Functions | p. 419 |
| Feasible Generalized Least Squares (GLS) to Adjust for Autocorrelated Errors and/or Heteroscedasticity | p. 419 |
| Consequences of Ignoring Nonspherical Errors O ≠<$$$>[Page No. xxvi] IT | p. 419 |
| Derivation of the GLS and Efficiency Comparison | p. 420 |
| Computation of the GLS and Feasible GLS | p. 422 |
| Improved OLS Inference for Nonspherical Errors | p. 424 |
| Efficient Estimation of b<$$$>[Page No. xxvi] Coefficients | p. 425 |
| An Illustration Using Fisher's Model for Interest Rates | p. 426 |
| Vector ARMA Estimation for Rational Expectations Models | p. 429 |
| Greater Realism of VARMA(p,q) Models | p. 431 |
| Expectational Variables from Conditional Forecasts in a General Model | p. 432 |
| A Rational Expectation Model Using VARMA | p. 433 |
| Further Forecasts, Transfer Function Gains, and Response Analysis | p. 438 |
| Optimal Estimating Function (OptEF) and Generalized Method of Moments (GMM) | p. 443 |
| Derivation of Optimal Estimating Functions for Regressions | p. 443 |
| Finite Sample Optimality of OptEF | p. 445 |
| Introduction to the GMM | p. 445 |
| Cases Where OptEF Viewpoint Dominates GMM | p. 447 |
| Advantages and Disadvantages of GMM and OptEF | p. 449 |
| Godambe Pivot Functions (GPFs) and Statistical Inference | p. 450 |
| Application of the Frisch-Waugh Theorem to Constructing CI95 | p. 452 |
| Steps in Application of GPF to Feasible GLS Estimation | p. 453 |
| Box-Cox, Loess and Projection Pursuit Regression | p. 459 |
| Further R Tools for Studying Nonlinear Relations | p. 459 |
| Box-Cox Transformation | p. 459 |
| Logarithmic and Square Root Transformations | p. 459 |
| Scatterplot Smoothing and Loess Regressions | p. 463 |
| Improved Fit (Forecasts) by Loess Smoothing | p. 465 |
| Projection Pursuit Methods | p. 466 |
| Remarks on Nonlinear Econometrics | p. 477 |
| Appendix | p. 479 |
| References | p. 485 |
| Index | p. 505 |
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