Hands-on intermediate econometrics using R: templates for learning quantitative methods and R software
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| Language: | English |
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Singapore
World Scientific
[2022]
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| Edition: | Second edition |
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| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxxvi, 608 Seiten Diagramme |
| ISBN: | 9789811256172 9789811256738 |
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| 245 | 1 | 0 | |a Hands-on intermediate econometrics using R |b templates for learning quantitative methods and R software |c Hrishikesh D. Vinod |
| 250 | |a Second edition | ||
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| 300 | |a xxxvi, 608 Seiten |b Diagramme | ||
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| adam_text | Contents Foreword vii Preface xi xix Preface to the Second Edition xxiii About the Author 1. Production Function and Regression Methods Using R 1.1 R and Microeconometric Preliminaries..................... 1.1.1 Data on metals production available in R................................................. 1.1.2 Descriptive statistics using R....................... 1.1.3 Writing skewness and kurtosis functions in R................................................. 1.1.4 Measurement units and numerically reliable ß ....................................................... 1.1.5 Basic graphics in R......................................... 1.1.6 The isoquant .................................................. 1.1.7 Total productivity of an input....................... 1.1.8 The marginal productivity (MP) of an input.......................................................... 1.1.9 Slope of the isoquant and MRTS................. 1.1.10 Scale elasticity as the returns to scale parameter......................................... 12 1.1.11 Elasticity of substitution................................ 1.1.12 Typical steps in empirical work.................... XXV 1 2 3 4 5 7 7 9 10 10 10 13 14
Hands-on Intermediate Econometrics Using R xxvi Preliminary Regression Theory: Results Using R......................................................... . . 15 1.2.1 Regression as an object “regl” in R............ 18 1.2.2 Accessing objects within an R object by using the dollar symbol.................... 18 1.3 Deeper Regression Theory: Diagonals of the Hat Matrix......................................................... 20 1.4 Discussion of Four Diagnostic Plots by R............... 22 1.5 Testing Constant Returns and 3D Scatter Plots ... 25 1.6 Homothetic Production and Cost Functions............ 29 1.6.1 Euler theorem and duality theorem ..... 32 1.6.2 Profit maximizing solutions.............. 33 1.6.3 Elasticity of total cost wrt output............... 35 1.7 Miscellaneous Microeconomic Topics.......................... 35 1.7.1 Analytic input demand function for the Cobb-Douglas form.......................... 35 1.7.2 Separability in the presence of three or more inputs...................................... 36 1.7.3 Two or more outputs as joint outputs ... 36 1.7.4 Economies of scope........................................ 36 1.7.5 Productivity and efficiency comparisons of firms or countries ....................... 37 1.8 Non-homogeneous Production Functions........................ 37 1.8.1 Three-input production function for widgets.......... 38 1.8.2 Isoquant plotting for a Bell System production function.......................... 43 1.9 Collinearity Problem, Singular Value Decomposition and Ridge Regression........................ 47 1.9.1 What is
collinearity?..................................... 48 1.9.2 Consequences of near collinearity............... 51 1.9.3 Regression theory using the singular value decomposition . ............................................... 53 1.10 Near Collinearity Solutions by Coefficient Shrinkage...................................................................... 58 1.10.1 Ridge regression.............................................. 60 1.10.2 Principal components regression.......................63 1.2
Contents xxvii 1.11 Bell System Production Function in Anti-Trust Trial............................................... 64 1.11.1 Collinearity diagnostics for Bell data trans-log.......................................................... 67 1.11.2 Shrinkage solution and ridge regression for Bell data...................................... 67 1.11.3 Ridge regression from existing R packages......................................... 68 1.12 Comments on Wrong Signs, Collinearity, and Ridge Scaling..................................................... 71 1.12.1 Comments on the 1982 Bell System breakup.......................................................... 77 1.13 Comparing Productive Efficiency of Firms...............78 1.14 Regression Model Selection and Inequality Constraints................................................................... 79 2. Univariate Time Series Analysis with R, 2.1 2.2 2.3 2.4 2.5 81 Econometric Univariate Time Series are Ubiquitous......................................................... 81 2.1.1 Boosting the Hodrick-Prescottfilter .... 86 Stochastic Difference Equations................................. 87 Second-Order Stochastic Difference Equation and Business Cycles............................................................. 92 2.3.1 Complex number solution of the stochastic AR(2) difference equation........................... 94 2.3.2 General solution to ARMA (p,p — 1) stochastic difference equations........ 97 Properties of ARIMA Models..................................... 98 2.4.1 Identification of the lag order......................... 100
2.4.2 ARIMA estimation........................................... 108 2.4.3 ARIMA diagnostic checking............................ 109 Stochastic Process and Stationarity........................... 116 2.5.1 Stochastic process and underlying probability space............................................. 116 2.5.2 Autocovariance of a stochasticprocess and ergodicity................................................... 119 2.5.3 Stationary process........................................... 121 2.5.4 Detrending and differencing toachieve stationarity...................................................... 126
Hands-on Intermediate Econometrics Using R xxviii Mean Reversion ............................................................ 138 Autocovariance Generating Functions and the Power Spectrum.................................................. 142 2.7.1 How to get the power spectrum from the AGF?........................................... 143 2.8 Explicit Modeling of Variance (ARCH, GARCH Models)........................................................................... 147 2.8.1 Advanced GARCH-type models in R ... . 151 2.9 Tests of Independence, Neglected Nonlinearity, Turning Points..................................................... 151 2.10 Long Memory Models and Fractional Differencing........................................................... 154 2.11 Forecasting..................................................................... 158 2.12 Concluding Remarks and Examples.......................... 162 2.6 2.7 3. Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration 3.1 3.2 3.3 3.4 165 Autoregressive Distributed Lag Models .................... 165 Economic Interpretations of ARDL(1,1) Model ... 173 3.2.1 Description of Ml to Mil model specifications..................................... 173 3.2.2 ARDL(0,q) as M12 model, impact and long-run multipliers ......................... 178 3.2.3 Adaptive expectations model to test the rational expectations hypothesis....... 179 3.2.4 Statistical inference and estimation with lagged dependent variables................ 180 3.2.5 Identification problems involving expectational variables (I. Fisher
example).............................................. 181 3.2.6 Impulse response, mean lag, and insights from a polynomials in L................ ; 182 3.2.7 Choice between Ml to Mil models using R 184 Stochastic Diffusion Models for Asset Prices............ 189 Cointegration between Non-stationary Series and Spurious Regression (R2 Durbin Watson).. 194 3.4.1 Definition: Integrated process of order d, 1(d)...................................................... 195 3.4.2 Cointegration definition and discussion . . . 196
Contents xxix Error correction models of cointegration . . 197 Economic equilibria and error reductions through learning ............................... 198 3.4.5 Signs and significance of coefficients on past errors while agents learn................... 199 Granger Causality Testing.............................................. 202 Co-movement of Related Series..................................... 204 3.4.3 3.4.4 3.5 3.6 4. Utility Theory and Empirical Implications 4.1 4.2 4.3 207 Utility Theory................................................................ 207 4.1.1 Expected utility theory.................................... 209 4.1.2 Arrow-Pratt coefficient of absolute risk aversion.............................................. 215 4.1.3 Risk premium needed to encourage risky investments........................................ 216 4.1.4 Taylor series links EUT, moments of /(x), and derivatives of U(x)..................... 218 Non-Expected Utility Theory......................... 219 4.2.1 Lorenz curve scaling over the unit square................................................. 220 4.2.2 Mapping from EUT to non-EUT within the unit square to get decision weights . . . 224 Incorporating Utility Theory into Risk Measurement and Stochastic Dominance.......... 228 4.3.1 Class DI of utility functions and investors............................................................ 229 4.3.2 Class D2 of utility functions and investors................................................ 229 4.3.3 Explicit utility functions and Arrow-Pratt measures of risk
aversion................................ 229 4.3.4 Class D3 of utility functions and investors.......................................... 230 4.3.5 Class D4 of utility functions and investors............................................................ 231 4.3.6 First-order stochastic dominance................. 233 4.3.7 Second-order stochastic dominance.............. 235 4.3.8 Third-order stochastic dominance.................237 4.3.9 Fourth-order stochastic dominance..............238
Hands-on Intermediate Econometrics Using R xxx 4.3.10 5. Vector Models for Multivariate Problems 5.1 5.2 5.3 5.4 5.5 6. Empirical checking of stochastic dominance using matrix multiplications and incorporation of 4DPs of non-EUT . . . 238 Introduction and Vector Autoregression Models . . . 247 5.1.1 Some R packages for vector modeling .... 248 5.1.2 Vector autoregression or VAR models .... 249 5.1.3 Data collection tips using R........................... 250 5.Ł4 VAR estimation of Sims’ model..................... 253 5.1.5 Granger-causality analysis in VAR models........................................ 256 5.1.6 Forecasting out-of-sample in VAR models........................................ 259 5.1.7 Impulse response analysis in VAR models........................................ 261 Multivariate Regressions: Canonical Correlations......................................... 266 5.2.1 Why canonical correlation popularity has lagged?........................................ 268 VAR Estimation and Cointegration Testing Using Canonical Correlations ........................................ 275 Structural VAR or SVAR estimation........................... 277 Final Remarks: MultivariateStatistics Using R . . . 279 * Simultaneous Equation Models 6.1 247 281 Introduction.................................................................. 281 6.1.1 Simultaneous equation notation system with stars and subscripts................... 283 6.1.2 Simultaneous equations bias and the reduced form................................... 288 6.1.3 Successively weaker assumptions regarding the nature of
the Zj matrix of regressors...................................... . 290 6.1.4 Reduced form estimation and other alternatives to OLS............................ 291 6.1.5 Assumptions of simultaneous equations models................................................. 293
Contents 6.2 6.3 6.4 6.5 6.6 7. Instrumental Variables and Generalized Least Squares....................................................... 293 6.2.1 The instrumental variables and generalized IV estimator......................................................295 6.2.2 Choice between OLS and IV by using Wu-Hausman specification test...... 297 6.2.3 Checking nonlinear endogeneity in R ... . 299 Limited Information and Two-Stage Least Squares....................................................... 300 6.3.1 Two-stage least squares ................................. 301 6.3.2 The fc-class estimator....................................... 302 6.3.3 Limited information maximum likelihood estimator ......................................................... 304 Identification of Simultaneous Equation Models . . . 306 6.4.1 Identification is uniquely going from the reduced form to the structure........................309 Full Information and Three-Stage Least Squares....................................................... 312 6.5.1 Full information maximum likelihood .... 318 Potential of Simultaneous Equations Beyond Econometrics............. 319 Limited Dependent Variable (GLM) Models 7.1 7.2 xxxi 321 Problems with Dummy Dependent Variables .... 322 7.1.1 Proof of the claim that Var(ej) =Pi(l-Pi) ....................................... 326 7.1.2 The general linear model from biostatistics........................................ 331 7.1.3 Marginal effects (partial derivatives) in logit-type GLM models................ 334 7.1.4 Further generalizations of logit and probit
models..................................... 336 7.1.5 Ordered response........................... 338 Quasi-likelihood Function for Binary Choice Models.................................................... 341 7.2.1 The ML estimator in binary choice models..................................... 342 7.2.2 Tobit model for censored dependent variables.............................................. 345
Hands-on Intermediate Econometrics Using R xxxii 7.3 7.4 8. Heckman Two-Step Estimator for Self-Selection Bias...................................................................... ... 348 7.3.1 Correcting COVID-19 testing bias .................353 Time Duration Length (Survival) Models.............. 354 7.4.1 Probability distributions and implied hazard functions ............................... 357 7.4.2 Parametric survival (hazard) models.... 358 7.4.3 Semiparametric including Cox proportional hazard models..................................... 359 Consumption and Demand: Kernel Regressions and Machine Learning 8.1 8.2 8.3 8.4 8.5 8.6 371 Reconciling Facts with Theory: Permanent Income Hypothesis......................................................... 372 Dynamic Optimization.....................................................373 Hall’s Random Walk Model........................................... 375 8.3.1 Data from the Internet and an implementation.......................................... 378 8.3.2 OLS estimation: Random walk in consumption...................................................... 380 8.3.3 Direct estimation of Hall’s NLHS specification............ .................... 382 8.3.4 Assumptions of Hall’s random walk............... 385 8.3.5 Testing whether income precedes consumption by Granger-causality VAR.................................................................. 386 Non-parametric Kernel Estimation.............................. 388 8.4.1 Kernel estimation of amorphous partials................................................. 390 Wiener-Hopf-Whittle
Model if Consumption Precedes Income..................................................394 8.5.1 Determination of target consumption .... 396 8.5.2 Implications for various puzzles of consumer theory ......................... . 399 Consumption Demand System and Forecasting . . . 400 8.6.1 Machine learning tools in R: Policy relevance.............................................. 400 8.6.2 Almost ideal demand system........................... 406
Contents 8.7 8.8 8.9 9. Consumers’ Surplus: Cost/Benefit Analysis of Taxes................................................................ 407 Final Remarks on Modeling Consumer Behavior ............................................................. 409 Appendix: Additional Macroeconomic VARs . . . .411 Single, Double, and Maximum Entropy Bootstrap and Inference 9.1 9.2 9.3 9.4 xxxiii 417 The Motivation and Background Behind Bootstrapping.................................................... 417 9.1.1 Pivotal quantity and p-value......................... 418 9.1.2 Uncertainty regarding proper density for regression errors illustrated ............ 420 9.1.3 The delta method for standard error of functions.............................................. 423 Description of Parametric iid Bootstrap..................... 424 9.2.1 Simulated sampling distribution for statistical inference using OLS residuals.......... 424 9.2.2 Steps in a parametric approximation .... 426 9.2.3 Percentile confidence intervals........................ 428 9.2.4 Reflected percentile confidence interval for bias correction............... ............................. 429 9.2.5 Significance tests as duals to confidence intervals............................................................ 430 Description of Non-parametric iid Bootstrap .... 432 9.3.1 Map data from time-domain to (numerical magnitudes) values-domain ............... 433 9.3.2 Wild bootstrap for well-behaved bootstrap residuals............ ............................................... 440 Double Bootstrap Illustrated with a Nonlinear
Model.................................................... 441 9.4.1 A digression on the size of resamples .... 442 9.4.2 Double bootstrap theory involving roots and uniform density ......................... 442 9.4.3 GNR implementation of nonlinear regression for metals data................445
Hands-on Intermediate Econometrics Using R xxxiv 9.5 Maximum Entropy Density Bootstrap for Time Series Data........................................... 450 9.5.1 Wiener, Kolmogorov, Khintchine ensemble of time series................................................... 451 9.5.2 Avoiding unrealistic properties of iid bootstrap ..................................... 453 9.5.3 Maximum entropy density is uniform when limits are known...................... 454 9.5.4 Quantiles of the patchwork of the ME density........................................ 456 9.5.5 Numerical illustration of “meboot” package in R........................................ 457 9.5.6 Simple and size-corrected confidence bounds ................................................. 462 9.5.7 Extensions of meboot algorithm and better Monte Carlo............................ 463 10. Generalized Least Squares, VARMA, and Estimating Functions 465 10.1 Feasible Generalized Least Squares to Adjust for Autocorrelated Errors and/or Heteroscedasticity......................................................... 465 10.1.1 Consequences of ignoring non-spherical errors Ω Jr................................................... 466 10.1.2 Derivation of the GLS and efficiency comparison ...................................................... 466 10.1.3 Computation of the GLS and feasible GLS...................................................... 468 10.1.4 Improved OLS inference for non-spherical errors................................................ 470 10.1.5 Efficient estimation of /3 coefficients............ 471 10.1.6 An illustration
using Fisher’s model for interest rates ................................................... 473 10.2 Vector ARMA Estimation for Rational Expectations Models................................... 476 10.2.1 Greater realism of VARMA(p, q) models............................................................... 478 10.2.2 Expectational variables from conditional forecasts in a general model............. 479
Contents xxxv 10.2.3 A rational expectation model using VARMA............................................ 480 10.2.4 Further forecasts, transfer function gains, and response analysis......................... 484 10.3 Optimal Estimating Function (OptEF) and Generalized Method of Moments (GMM)............... 490 10.3.1 Derivation of optimal estimating functions for regressions................................................... 490 10.3.2 Finite sample optimality of OptEF........... 492 10.3.3 Introduction to the GMM............................. 493 10.3.4 Cases where OptEF viewpoint dominates GMM............................................. 494 10.3.5 Advantages and disadvantages of GMM and OptEF...................................................... 497 10.4 Godambe Pivot Functions and Statistical Inference............................................................. 498 10.4.1 Application of the Frisch-Waugh theorem to constructing CI95 ....................... 500 10.4.2 Steps in application of GPF to feasible GLS estimation.................................. 502 10.5 Consistent Estimation of New Keynesian Phillips Curve using R packages................................... 505 11. Box-Cox, Loess, Projection Pursuit, Quantile and Threshold Regression 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 513 Further R Tools for Studying Nonlinear Relations........................... 513 Box-Cox Transformation............................................ 513 11.2.1 Logarithmic and square root transformations.................................. 513 Scatterplot Smoothing and Loess Regressions . . .
.517 11.3.1 Improved fit (forecasts) by loess smoothing............................ 519 Projection Pursuit Methods......................................... 521 Quantile Regression..................................................... 531 Ridge Regularization in Data Science....................... 535 Threshold Cointegration and Asymmetric Reactions........................................................................ 536 Remarks on Nonlinear Econometrics.......................... 539
Hands-on Intermediate Econometrics Using R xxxvi 12. Miscellany: Dependence, Correlations, Information Entropy, Causality, Panel Data, and Exact Stochastic Dominance 541 12.1 Simple Correlation Underestimates Dependence . . . 542 12.2 Information Content Equals the Amount of Surprise................................................................... 543 12.2.1 Mutual information in higher dimensions......................................................... 544 12.2.2 Symmetry is neither necessary nor sufficient for dependence................................. 545 12.3 Generalized Correlation Coefficients.......................... 545 12.4 Partial Correlations Generalized................................ 547 12.5 Approximate Causality from Observational Data..................... 548 12.6 Review of Kernel Causality......................................... 549 12.6.1 Causality between human activities and global warming................................................ 551 12.7 Difference in Differences Treatment Effect Estimation..................... 554 12.7.1 Matched sampling for causal effects............ 557 12.8 Panel Data Models........................................................ 557 12.8.1 Generalized method of moments estimator for panels............... ..........................................563 12.9 Exact Stochastic Dominance........... .......................... 564 Appendix 567 References 575 Index 603
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Contents Foreword vii Preface xi xix Preface to the Second Edition xxiii About the Author 1. Production Function and Regression Methods Using R 1.1 R and Microeconometric Preliminaries. 1.1.1 Data on metals production available in R. 1.1.2 Descriptive statistics using R. 1.1.3 Writing skewness and kurtosis functions in R. 1.1.4 Measurement units and numerically reliable ß . 1.1.5 Basic graphics in R. 1.1.6 The isoquant . 1.1.7 Total productivity of an input. 1.1.8 The marginal productivity (MP) of an input. 1.1.9 Slope of the isoquant and MRTS. 1.1.10 Scale elasticity as the returns to scale parameter. 12 1.1.11 Elasticity of substitution. 1.1.12 Typical steps in empirical work. XXV 1 2 3 4 5 7 7 9 10 10 10 13 14
Hands-on Intermediate Econometrics Using R xxvi Preliminary Regression Theory: Results Using R. . . 15 1.2.1 Regression as an object “regl” in R. 18 1.2.2 Accessing objects within an R object by using the dollar symbol. 18 1.3 Deeper Regression Theory: Diagonals of the Hat Matrix. 20 1.4 Discussion of Four Diagnostic Plots by R. 22 1.5 Testing Constant Returns and 3D Scatter Plots . 25 1.6 Homothetic Production and Cost Functions. 29 1.6.1 Euler theorem and duality theorem . 32 1.6.2 Profit maximizing solutions. 33 1.6.3 Elasticity of total cost wrt output. 35 1.7 Miscellaneous Microeconomic Topics. 35 1.7.1 Analytic input demand function for the Cobb-Douglas form. 35 1.7.2 Separability in the presence of three or more inputs. 36 1.7.3 Two or more outputs as joint outputs . 36 1.7.4 Economies of scope. 36 1.7.5 Productivity and efficiency comparisons of firms or countries . 37 1.8 Non-homogeneous Production Functions. 37 1.8.1 Three-input production function for widgets. 38 1.8.2 Isoquant plotting for a Bell System production function. 43 1.9 Collinearity Problem, Singular Value Decomposition and Ridge Regression. 47 1.9.1 What is
collinearity?. 48 1.9.2 Consequences of near collinearity. 51 1.9.3 Regression theory using the singular value decomposition . . 53 1.10 Near Collinearity Solutions by Coefficient Shrinkage. 58 1.10.1 Ridge regression. 60 1.10.2 Principal components regression.63 1.2
Contents xxvii 1.11 Bell System Production Function in Anti-Trust Trial. 64 1.11.1 Collinearity diagnostics for Bell data trans-log. 67 1.11.2 Shrinkage solution and ridge regression for Bell data. 67 1.11.3 Ridge regression from existing R packages. 68 1.12 Comments on Wrong Signs, Collinearity, and Ridge Scaling. 71 1.12.1 Comments on the 1982 Bell System breakup. 77 1.13 Comparing Productive Efficiency of Firms.78 1.14 Regression Model Selection and Inequality Constraints. 79 2. Univariate Time Series Analysis with R, 2.1 2.2 2.3 2.4 2.5 81 Econometric Univariate Time Series are Ubiquitous. 81 2.1.1 Boosting the Hodrick-Prescottfilter . 86 Stochastic Difference Equations. 87 Second-Order Stochastic Difference Equation and Business Cycles. 92 2.3.1 Complex number solution of the stochastic AR(2) difference equation. 94 2.3.2 General solution to ARMA (p,p — 1) stochastic difference equations. 97 Properties of ARIMA Models. 98 2.4.1 Identification of the lag order. 100
2.4.2 ARIMA estimation. 108 2.4.3 ARIMA diagnostic checking. 109 Stochastic Process and Stationarity. 116 2.5.1 Stochastic process and underlying probability space. 116 2.5.2 Autocovariance of a stochasticprocess and ergodicity. 119 2.5.3 Stationary process. 121 2.5.4 Detrending and differencing toachieve stationarity. 126
Hands-on Intermediate Econometrics Using R xxviii Mean Reversion . 138 Autocovariance Generating Functions and the Power Spectrum. 142 2.7.1 How to get the power spectrum from the AGF?. 143 2.8 Explicit Modeling of Variance (ARCH, GARCH Models). 147 2.8.1 Advanced GARCH-type models in R . . 151 2.9 Tests of Independence, Neglected Nonlinearity, Turning Points. 151 2.10 Long Memory Models and Fractional Differencing. 154 2.11 Forecasting. 158 2.12 Concluding Remarks and Examples. 162 2.6 2.7 3. Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration 3.1 3.2 3.3 3.4 165 Autoregressive Distributed Lag Models . 165 Economic Interpretations of ARDL(1,1) Model . 173 3.2.1 Description of Ml to Mil model specifications. 173 3.2.2 ARDL(0,q) as M12 model, impact and long-run multipliers . 178 3.2.3 Adaptive expectations model to test the rational expectations hypothesis. 179 3.2.4 Statistical inference and estimation with lagged dependent variables. 180 3.2.5 Identification problems involving expectational variables (I. Fisher
example). 181 3.2.6 Impulse response, mean lag, and insights from a polynomials in L. ; 182 3.2.7 Choice between Ml to Mil models using R 184 Stochastic Diffusion Models for Asset Prices. 189 Cointegration between Non-stationary Series and Spurious Regression (R2 Durbin Watson). 194 3.4.1 Definition: Integrated process of order d, 1(d). 195 3.4.2 Cointegration definition and discussion . . . 196
Contents xxix Error correction models of cointegration . . 197 Economic equilibria and error reductions through learning . 198 3.4.5 Signs and significance of coefficients on past errors while agents learn. 199 Granger Causality Testing. 202 Co-movement of Related Series. 204 3.4.3 3.4.4 3.5 3.6 4. Utility Theory and Empirical Implications 4.1 4.2 4.3 207 Utility Theory. 207 4.1.1 Expected utility theory. 209 4.1.2 Arrow-Pratt coefficient of absolute risk aversion. 215 4.1.3 Risk premium needed to encourage risky investments. 216 4.1.4 Taylor series links EUT, moments of /(x), and derivatives of U(x). 218 Non-Expected Utility Theory. 219 4.2.1 Lorenz curve scaling over the unit square. 220 4.2.2 Mapping from EUT to non-EUT within the unit square to get decision weights . . . 224 Incorporating Utility Theory into Risk Measurement and Stochastic Dominance. 228 4.3.1 Class DI of utility functions and investors. 229 4.3.2 Class D2 of utility functions and investors. 229 4.3.3 Explicit utility functions and Arrow-Pratt measures of risk
aversion. 229 4.3.4 Class D3 of utility functions and investors. 230 4.3.5 Class D4 of utility functions and investors. 231 4.3.6 First-order stochastic dominance. 233 4.3.7 Second-order stochastic dominance. 235 4.3.8 Third-order stochastic dominance.237 4.3.9 Fourth-order stochastic dominance.238
Hands-on Intermediate Econometrics Using R xxx 4.3.10 5. Vector Models for Multivariate Problems 5.1 5.2 5.3 5.4 5.5 6. Empirical checking of stochastic dominance using matrix multiplications and incorporation of 4DPs of non-EUT . . . 238 Introduction and Vector Autoregression Models . . . 247 5.1.1 Some R packages for vector modeling . 248 5.1.2 Vector autoregression or VAR models . 249 5.1.3 Data collection tips using R. 250 5.Ł4 VAR estimation of Sims’ model. 253 5.1.5 Granger-causality analysis in VAR models. 256 5.1.6 Forecasting out-of-sample in VAR models. 259 5.1.7 Impulse response analysis in VAR models. 261 Multivariate Regressions: Canonical Correlations. 266 5.2.1 Why canonical correlation popularity has lagged?. 268 VAR Estimation and Cointegration Testing Using Canonical Correlations . 275 Structural VAR or SVAR estimation. 277 Final Remarks: MultivariateStatistics Using R . . . 279 * Simultaneous Equation Models 6.1 247 281 Introduction. 281 6.1.1 Simultaneous equation notation system with stars and subscripts. 283 6.1.2 Simultaneous equations bias and the reduced form. 288 6.1.3 Successively weaker assumptions regarding the nature of
the Zj matrix of regressors. . 290 6.1.4 Reduced form estimation and other alternatives to OLS. 291 6.1.5 Assumptions of simultaneous equations models. 293
Contents 6.2 6.3 6.4 6.5 6.6 7. Instrumental Variables and Generalized Least Squares. 293 6.2.1 The instrumental variables and generalized IV estimator.295 6.2.2 Choice between OLS and IV by using Wu-Hausman specification test. 297 6.2.3 Checking nonlinear endogeneity in R . . 299 Limited Information and Two-Stage Least Squares. 300 6.3.1 Two-stage least squares . 301 6.3.2 The fc-class estimator. 302 6.3.3 Limited information maximum likelihood estimator . 304 Identification of Simultaneous Equation Models . . . 306 6.4.1 Identification is uniquely going from the reduced form to the structure.309 Full Information and Three-Stage Least Squares. 312 6.5.1 Full information maximum likelihood . 318 Potential of Simultaneous Equations Beyond Econometrics. 319 Limited Dependent Variable (GLM) Models 7.1 7.2 xxxi 321 Problems with Dummy Dependent Variables . 322 7.1.1 Proof of the claim that Var(ej) =Pi(l-Pi) . 326 7.1.2 The general linear model from biostatistics. 331 7.1.3 Marginal effects (partial derivatives) in logit-type GLM models. 334 7.1.4 Further generalizations of logit and probit
models. 336 7.1.5 Ordered response. 338 Quasi-likelihood Function for Binary Choice Models. 341 7.2.1 The ML estimator in binary choice models. 342 7.2.2 Tobit model for censored dependent variables. 345
Hands-on Intermediate Econometrics Using R xxxii 7.3 7.4 8. Heckman Two-Step Estimator for Self-Selection Bias. . 348 7.3.1 Correcting COVID-19 testing bias .353 Time Duration Length (Survival) Models. 354 7.4.1 Probability distributions and implied hazard functions . 357 7.4.2 Parametric survival (hazard) models. 358 7.4.3 Semiparametric including Cox proportional hazard models. 359 Consumption and Demand: Kernel Regressions and Machine Learning 8.1 8.2 8.3 8.4 8.5 8.6 371 Reconciling Facts with Theory: Permanent Income Hypothesis. 372 Dynamic Optimization.373 Hall’s Random Walk Model. 375 8.3.1 Data from the Internet and an implementation. 378 8.3.2 OLS estimation: Random walk in consumption. 380 8.3.3 Direct estimation of Hall’s NLHS specification. . 382 8.3.4 Assumptions of Hall’s random walk. 385 8.3.5 Testing whether income precedes consumption by Granger-causality VAR. 386 Non-parametric Kernel Estimation. 388 8.4.1 Kernel estimation of amorphous partials. 390 Wiener-Hopf-Whittle
Model if Consumption Precedes Income.394 8.5.1 Determination of target consumption . 396 8.5.2 Implications for various puzzles of consumer theory . . 399 Consumption Demand System and Forecasting . . . 400 8.6.1 Machine learning tools in R: Policy relevance. 400 8.6.2 Almost ideal demand system. 406
Contents 8.7 8.8 8.9 9. Consumers’ Surplus: Cost/Benefit Analysis of Taxes. 407 Final Remarks on Modeling Consumer Behavior . 409 Appendix: Additional Macroeconomic VARs . . . .411 Single, Double, and Maximum Entropy Bootstrap and Inference 9.1 9.2 9.3 9.4 xxxiii 417 The Motivation and Background Behind Bootstrapping. 417 9.1.1 Pivotal quantity and p-value. 418 9.1.2 Uncertainty regarding proper density for regression errors illustrated . 420 9.1.3 The delta method for standard error of functions. 423 Description of Parametric iid Bootstrap. 424 9.2.1 Simulated sampling distribution for statistical inference using OLS residuals. 424 9.2.2 Steps in a parametric approximation . 426 9.2.3 Percentile confidence intervals. 428 9.2.4 Reflected percentile confidence interval for bias correction. . 429 9.2.5 Significance tests as duals to confidence intervals. 430 Description of Non-parametric iid Bootstrap . 432 9.3.1 Map data from time-domain to (numerical magnitudes) values-domain . 433 9.3.2 Wild bootstrap for well-behaved bootstrap residuals. . 440 Double Bootstrap Illustrated with a Nonlinear
Model. 441 9.4.1 A digression on the size of resamples . 442 9.4.2 Double bootstrap theory involving roots and uniform density . 442 9.4.3 GNR implementation of nonlinear regression for metals data.445
Hands-on Intermediate Econometrics Using R xxxiv 9.5 Maximum Entropy Density Bootstrap for Time Series Data. 450 9.5.1 Wiener, Kolmogorov, Khintchine ensemble of time series. 451 9.5.2 Avoiding unrealistic properties of iid bootstrap . 453 9.5.3 Maximum entropy density is uniform when limits are known. 454 9.5.4 Quantiles of the patchwork of the ME density. 456 9.5.5 Numerical illustration of “meboot” package in R. 457 9.5.6 Simple and size-corrected confidence bounds . 462 9.5.7 Extensions of meboot algorithm and better Monte Carlo. 463 10. Generalized Least Squares, VARMA, and Estimating Functions 465 10.1 Feasible Generalized Least Squares to Adjust for Autocorrelated Errors and/or Heteroscedasticity. 465 10.1.1 Consequences of ignoring non-spherical errors Ω Jr. 466 10.1.2 Derivation of the GLS and efficiency comparison . 466 10.1.3 Computation of the GLS and feasible GLS. 468 10.1.4 Improved OLS inference for non-spherical errors. 470 10.1.5 Efficient estimation of /3 coefficients. 471 10.1.6 An illustration
using Fisher’s model for interest rates . 473 10.2 Vector ARMA Estimation for Rational Expectations Models. 476 10.2.1 Greater realism of VARMA(p, q) models. 478 10.2.2 Expectational variables from conditional forecasts in a general model. 479
Contents xxxv 10.2.3 A rational expectation model using VARMA. 480 10.2.4 Further forecasts, transfer function gains, and response analysis. 484 10.3 Optimal Estimating Function (OptEF) and Generalized Method of Moments (GMM). 490 10.3.1 Derivation of optimal estimating functions for regressions. 490 10.3.2 Finite sample optimality of OptEF. 492 10.3.3 Introduction to the GMM. 493 10.3.4 Cases where OptEF viewpoint dominates GMM. 494 10.3.5 Advantages and disadvantages of GMM and OptEF. 497 10.4 Godambe Pivot Functions and Statistical Inference. 498 10.4.1 Application of the Frisch-Waugh theorem to constructing CI95 . 500 10.4.2 Steps in application of GPF to feasible GLS estimation. 502 10.5 Consistent Estimation of New Keynesian Phillips Curve using R packages. 505 11. Box-Cox, Loess, Projection Pursuit, Quantile and Threshold Regression 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 513 Further R Tools for Studying Nonlinear Relations. 513 Box-Cox Transformation. 513 11.2.1 Logarithmic and square root transformations. 513 Scatterplot Smoothing and Loess Regressions . . .
.517 11.3.1 Improved fit (forecasts) by loess smoothing. 519 Projection Pursuit Methods. 521 Quantile Regression. 531 Ridge Regularization in Data Science. 535 Threshold Cointegration and Asymmetric Reactions. 536 Remarks on Nonlinear Econometrics. 539
Hands-on Intermediate Econometrics Using R xxxvi 12. Miscellany: Dependence, Correlations, Information Entropy, Causality, Panel Data, and Exact Stochastic Dominance 541 12.1 Simple Correlation Underestimates Dependence . . . 542 12.2 Information Content Equals the Amount of Surprise. 543 12.2.1 Mutual information in higher dimensions. 544 12.2.2 Symmetry is neither necessary nor sufficient for dependence. 545 12.3 Generalized Correlation Coefficients. 545 12.4 Partial Correlations Generalized. 547 12.5 Approximate Causality from Observational Data. 548 12.6 Review of Kernel Causality. 549 12.6.1 Causality between human activities and global warming. 551 12.7 Difference in Differences Treatment Effect Estimation. 554 12.7.1 Matched sampling for causal effects. 557 12.8 Panel Data Models. 557 12.8.1 Generalized method of moments estimator for panels. .563 12.9 Exact Stochastic Dominance. . 564 Appendix 567 References 575 Index 603 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:57:55Z |
| indexdate | 2024-07-10T09:33:14Z |
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| spelling | Vinod, Hrishikesh D. 1939- Verfasser (DE-588)135564700 aut Hands-on intermediate econometrics using R templates for learning quantitative methods and R software Hrishikesh D. Vinod Second edition Singapore World Scientific [2022] © 2022 xxxvi, 608 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Ökonometrie (DE-588)4132280-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf R Programm (DE-588)4705956-4 s Ökonometrie (DE-588)4132280-0 s b DE-604 Erscheint auch als Online-Ausgabe 978-981-125-618-9 Erscheint auch als Online-Ausgabe 978-981-125-619-6 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033634743&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vinod, Hrishikesh D. 1939- Hands-on intermediate econometrics using R templates for learning quantitative methods and R software Ökonometrie (DE-588)4132280-0 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4705956-4 |
| title | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software |
| title_auth | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software |
| title_exact_search | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software |
| title_exact_search_txtP | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software |
| title_full | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software Hrishikesh D. Vinod |
| title_fullStr | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software Hrishikesh D. Vinod |
| title_full_unstemmed | Hands-on intermediate econometrics using R templates for learning quantitative methods and R software Hrishikesh D. Vinod |
| title_short | Hands-on intermediate econometrics using R |
| title_sort | hands on intermediate econometrics using r templates for learning quantitative methods and r software |
| title_sub | templates for learning quantitative methods and R software |
| topic | Ökonometrie (DE-588)4132280-0 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Ökonometrie R Programm |
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