Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software (Second Edition)

Contents
Foreword
Preface
Preface to the Second Edition
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 β
		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
		1.1.11. Elasticity of substitution
		1.1.12. Typical steps in empirical work
	1.2. Preliminary Regression Theory: Results Using R
		1.2.1. Regression as an object “reg1” in R
		1.2.2. Accessing objects within an R object by using the dollar symbol
	1.3. Deeper Regression Theory: Diagonals of the Hat Matrix
	1.4. Discussion of Four Diagnostic Plots by R
	1.5. Testing Constant Returns and 3D Scatter Plots
	1.6. Homothetic Production and Cost Functions
		1.6.0.1. The constant elasticity of substitution production function
		1.6.0.2. Hidden restrictions of homogeneous/homethetic functional forms
		1.6.0.3. Cost function Lagrangian for minimization of costs
		1.6.0.4. Cost minimizing solution
		1.6.1. Euler theorem and duality theorem
			1.6.1.1. Expansion path
			1.6.1.2. Duality theorem
		1.6.2. Profit maximizing solutions
		1.6.3. Elasticity of total cost wrt output
	1.7. Miscellaneous Microeconomic Topics
		1.7.1. Analytic input demand function for the Cobb–Douglas form
		1.7.2. Separability in the presence of three or more inputs
		1.7.3. Two or more outputs as joint outputs
		1.7.4. Economies of scope
			1.7.4.1. Bell system example on economies of scope
		1.7.5. Productivity and efficiency comparisons of firms or countries
	1.8. Non-homogeneous Production Functions
		1.8.1. Three-input production function for widgets
			1.8.1.1. Creating a text file of data on your C drive for reading in R
			1.8.1.2. Reading data and MNH estimation in R
		1.8.2. Isoquant plotting for a Bell System production function
	1.9. Collinearity Problem, Singular Value Decomposition and Ridge Regression
		1.9.1. What is collinearity?
			1.9.1.1. Rank deficiency m
			1.9.1.2. Near collinearity
		1.9.2. Consequences of near collinearity
			1.9.2.1. Collinearity and ill-conditioned matrices
			1.9.2.2. Rule of thumb for a large condition number
			1.9.2.3. Collinearity as a non-problem
		1.9.3. Regression theory using the singular value decomposition
			1.9.3.1. MSE(b) and extreme unreliability of OLS under near collinearity
			1.9.3.2. REMARK 1 (extreme unreliability of OLS)
			1.9.3.3. Forecasting and collinearity
	1.10. Near Collinearity Solutions by Coefficient Shrinkage
		1.10.1. Ridge regression
			1.10.1.1. Derivation of shrinkage factors Δ
			1.10.1.2. Declining deltas and justification for shrinking to zero prior
			1.10.1.3. Choice of k in ridge regression
		1.10.2. Principal components regression
	1.11. Bell System Production Function in Anti-Trust Trial
		1.11.1. Collinearity diagnostics for Bell data trans-log
		1.11.2. Shrinkage solution and ridge regression for Bell data
		1.11.3. Ridge regression from existing R packages
	1.12. Comments on Wrong Signs, Collinearity, and Ridge Scaling
		1.12.1. Comments on the 1982 Bell System breakup
	1.13. Comparing Productive Efficiency of Firms
	1.14. Regression Model Selection and Inequality Constraints
2. Univariate Time Series Analysis with R
	2.1. Econometric Univariate Time Series are Ubiquitous
		2.1.1. Boosting the Hodrick–Prescott filter
	2.2. Stochastic Difference Equations
	2.3. Second-Order Stochastic Difference Equation and Business Cycles
		2.3.1. Complex number solution of the stochastic AR(2) difference equation
		2.3.2. General solution to ARMA (p, p − 1) stochastic difference equations
	2.4. Properties of ARIMA Models
		2.4.1. Identification of the lag order
		2.4.2. ARIMA estimation
		2.4.3. ARIMA diagnostic checking
	2.5. Stochastic Process and Stationarity
		2.5.1. Stochastic process and underlying probability space
		2.5.2. Autocovariance of a stochastic process and ergodicity
		2.5.3. Stationary process
		2.5.4. Detrending and differencing to achieve stationarity
	2.6. Mean Reversion
	2.7. Autocovariance Generating Functions and the Power Spectrum
		2.7.1. How to get the power spectrum from the AGF?
	2.8. Explicit Modeling of Variance (ARCH, GARCH Models)
		2.8.1. Advanced GARCH-type models in R
	2.9. Tests of Independence, Neglected Nonlinearity, Turning Points
	2.10. Long Memory Models and Fractional Differencing
	2.11. Forecasting
	2.12. Concluding Remarks and Examples
3. Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration
	3.1. Autoregressive Distributed Lag Models
	3.2. Economic Interpretations of ARDL(1, 1) Model
		3.2.1. Description of M1 to M11 model specifications
		3.2.2. ARDL(0,q) as M12 model, impact and long-run multipliers
		3.2.3. Adaptive expectations model to test the rational expectations hypothesis
		3.2.4. Statistical inference and estimation with lagged dependent variables
		3.2.5. Identification problems involving expectational variables (I. Fisher example)
		3.2.6. Impulse response, mean lag, and insights from a polynomials in L
		3.2.7. Choice between M1 to M11 models using R
	3.3. Stochastic Diffusion Models for Asset Prices
	3.4. Cointegration between Non-stationary Series and Spurious Regression (R2 > Durbin Watson)
		3.4.1. Definition: Integrated process of order d, I(d)
		3.4.2. Cointegration definition and discussion
		3.4.3. Error correction models of cointegration
		3.4.4. Economic equilibria and error reductions through learning
		3.4.5. Signs and significance of coefficients on past errors while agents learn
	3.5. Granger Causality Testing
	3.6. Co-movement of Related Series
4. Utility Theory and Empirical Implications
	4.1. Utility Theory
		4.1.1. Expected utility theory
		4.1.2. Arrow–Pratt coefficient of absolute risk aversion
		4.1.3. Risk premium needed to encourage risky investments
		4.1.4. Taylor series links EUT, moments of f(x), and derivatives of U(x)
	4.2. Non-Expected Utility Theory
		4.2.1. Lorenz curve scaling over the unit square
		4.2.2. Mapping from EUT to non-EUT within the unit square to get decision weights
	4.3. Incorporating Utility Theory into Risk Measurement and Stochastic Dominance
		4.3.1. Class D1 of utility functions and investors
		4.3.2. Class D2 of utility functions and investors
		4.3.3. Explicit utility functions and Arrow–Pratt measures of risk aversion
		4.3.4. Class D3 of utility functions and investors
		4.3.5. Class D4 of utility functions and investors
		4.3.6. First-order stochastic dominance
		4.3.7. Second-order stochastic dominance
			4.3.7.1. Orders of stochastic dominance, utility function partials, and return density moments are unrelated
		4.3.8. Third-order stochastic dominance
		4.3.9. Fourth-order stochastic dominance
		4.3.10. Empirical checking of stochastic dominance using matrix multiplications and incorporation of 4DPs of non-EUT
5. Vector Models for Multivariate Problems
	5.1. Introduction and Vector Autoregression Models
		5.1.1. Some R packages for vector modeling
		5.1.2. Vector autoregression or VAR models
		5.1.3. Data collection tips using R
		5.1.4. VAR estimation of Sims’ model
		5.1.5. Granger-causality analysis in VAR models
		5.1.6. Forecasting out-of-sample in VAR models
		5.1.7. Impulse response analysis in VAR models
	5.2. Multivariate Regressions: Canonical Correlations
		5.2.1. Why canonical correlation popularity has lagged?
	5.3. VAR Estimation and Cointegration Testing Using Canonical Correlations
	5.4. Structural VAR or SVAR estimation
	5.5. Final Remarks: Multivariate Statistics Using R
6. Simultaneous Equation Models
	6.1. Introduction
		6.1.1. Simultaneous equation notation system with stars and subscripts
		6.1.2. Simultaneous equations bias and the reduced form
		6.1.3. Successively weaker assumptions regarding the nature of the Zj matrix of regressors
		6.1.4. Reduced form estimation and other alternatives to OLS
		6.1.5. Assumptions of simultaneous equations models
	6.2. Instrumental Variables and Generalized Least Squares
		6.2.1. The instrumental variables and generalized IV estimator
		6.2.2. Choice between OLS and IV by using Wu–Hausman specification test
		6.2.3. Checking nonlinear endogeneity in R
	6.3. Limited Information and Two-Stage Least Squares
		6.3.1. Two-stage least squares
		6.3.2. Thek-class estimator
		6.3.3. Limited information maximum likelihood estimator
	6.4. Identification of Simultaneous Equation Models
		6.4.1. Identification is uniquely going from the reduced form to the structure
	6.5. Full Information and Three-Stage Least Squares
		6.5.1. Full information maximum likelihood
	6.6. Potential of Simultaneous Equations Beyond Econometrics
7. Limited Dependent Variable (GLM) Models
	7.1. Problems with Dummy Dependent Variables
		7.1.1. Proof of the claim that Var(εi) = Pi(1 − Pi)
		7.1.2. The general linear model from biostatistics
		7.1.3. Marginal effects (partial derivatives) in logit-type GLM models
		7.1.4. Further generalizations of logit and probit models
		7.1.5. Ordered response
	7.2. Quasi-likelihood Function for Binary Choice Models
		7.2.1. The ML estimator in binary choice models
		7.2.2. Tobit model for censored dependent variables
	7.3. Heckman Two-Step Estimator for Self-Selection Bias
		7.3.1. Correcting COVID-19 testing bias
	7.4. Time Duration Length (Survival) Models
		7.4.1. Probability distributions and implied hazard functions
		7.4.2. Parametric survival (hazard) models
		7.4.3. Semiparametric including Cox proportional hazard models
8. Consumption and Demand: Kernel Regressions and Machine Learning
	8.1. Reconciling Facts with Theory: Permanent Income Hypothesis
	8.2. Dynamic Optimization
	8.3. Hall’s Random Walk Model
		8.3.1. Data from the Internet and an implementation
		8.3.2. OLS estimation: Random walk in consumption
		8.3.3. Direct estimation of Hall’s NLHS specification
			8.3.3.1. Monthly to quarterly data conversion
		8.3.4. Assumptions of Hall’s random walk
		8.3.5. Testing whether income precedes consumption by Granger-causality VAR
	8.4. Non-parametric Kernel Estimation
		8.4.1. Kernel estimation of amorphous partials
	8.5. Wiener–Hopf–Whittle Model if Consumption Precedes Income
		8.5.1. Determination of target consumption
		8.5.2. Implications for various puzzles of consumer theory
	8.6. Consumption Demand System and Forecasting
		8.6.1. Machine learning tools in R: Policy relevance
		8.6.2. Almost ideal demand system
	8.7. Consumers’ Surplus: Cost/Benefit Analysis of Taxes
	8.8. Final Remarks on Modeling Consumer Behavior
	8.9. Appendix: Additional Macroeconomic VARs
9. Single, Double, and Maximum Entropy Bootstrap and Inference
	9.1. The Motivation and Background Behind Bootstrapping
		9.1.1. Pivotal quantity and p-value
		9.1.2. Uncertainty regarding proper density for regression errors illustrated
		9.1.3. The delta method for standard error of functions
	9.2. Description of Parametric iid Bootstrap
		9.2.1. Simulated sampling distribution for statistical inference using OLS residuals
		9.2.2. Steps in a parametric approximation
		9.2.3. Percentile confidence intervals
		9.2.4. Reflected percentile confidence interval for bias correction
		9.2.5. Significance tests as duals to confidence intervals
	9.3. Description of Non-parametric iid Bootstrap
		9.3.1. Map data from time-domain to (numerical magnitudes) values-domain
		9.3.2. Wild bootstrap for well-behaved bootstrap residuals
	9.4. Double Bootstrap Illustrated with a Nonlinear Model
		9.4.1. A digression on the size of resamples
		9.4.2. Double bootstrap theory involving roots and uniform density
		9.4.3. GNR implementation of nonlinear regression for metals data
	9.5. Maximum Entropy Density Bootstrap for Time Series Data
		9.5.1. Wiener, Kolmogorov, Khintchine ensemble of time series
		9.5.2. Avoiding unrealistic properties of iid bootstrap
		9.5.3. Maximum entropy density is uniform when limits are known
		9.5.4. Quantiles of the patchwork of the ME density
		9.5.5. Numerical illustration of “meboot” package in R
		9.5.6. Simple and size-corrected confidence bounds
		9.5.7. Extensions of meboot algorithm and better Monte Carlo
10. Generalized Least Squares, VARMA, and Estimating Funct
	10.1. Feasible Generalized Least Squares to Adjust for Autocorrelated Errors and/or Heteroscedasticity
		10.1.1. Consequences of ignoring non-spherical errors Ω = IT
		10.1.2. Derivation of the GLS and efficiency comparison
		10.1.3. Computation of the GLS and feasible GLS
		10.1.4. Improved OLS inference for non-spherical errors
		10.1.5. Efficient estimation of β coefficients
		10.1.6. An illustration using Fisher’s model for interest rates
	10.2. Vector ARMA Estimation for Rational Expectations Models
		10.2.1. Greater realism of VARMA(p, q) models
		10.2.2. Expectational variables from conditional forecasts in a general model
		10.2.3. A rational expectation model using VARMA
		10.2.4. Further forecasts, transfer function gains, and response analysis
	10.3. Optimal Estimating Function (OptEF) and Generalized Method of Moments (GMM)
		10.3.1. Derivation of optimal estimating functions for regressions
		10.3.2. Finite sample optimality of OptEF
		10.3.3. Introduction to the GMM
		10.3.4. Cases where OptEF viewpoint dominates GMM
		10.3.5. Advantages and disadvantages of GMM and OptEF
	10.4. Godambe Pivot Functions and Statistical Inference
		10.4.1. Application of the Frisch–Waugh theorem to constructing CI95
		10.4.2. Steps in application of GPF to feasible GLS estimation
	10.5. Consistent Estimation of New Keynesian Phillips Curve using R packages
11. Box–Cox, Loess, Projection Pursuit, Quantile and Threshold Regression
	11.1. Further R Tools for Studying Nonlinear Relations
	11.2. Box–Cox Transformation
		11.2.1. Logarithmic and square root transformations
	11.3. Scatterplot Smoothing and Loess Regressions
		11.3.1. Improved fit (forecasts) by loess smoothing
	11.4. Projection Pursuit Methods
	11.5. Quantile Regression
	11.6. Ridge Regularization in Data Science
	11.7. Threshold Cointegration and Asymmetric Reactions
	11.8. Remarks on Nonlinear Econometrics
12. Miscellany: Dependence, Correlations, Information Entropy, Causality, Panel Data, and Exact Stochastic Dominance
	12.1. Simple Correlation Underestimates Dependence
	12.2. Information Content Equals the Amount of Surprise
		12.2.1. Mutual information in higher dimensions
		12.2.2. Symmetry is neither necessary nor sufficient for dependence
	12.3. Generalized Correlation Coefficients
	12.4. Partial Correlations Generalized
	12.5. Approximate Causality from Observational Data
	12.6. Review of Kernel Causality
		12.6.1. Causality between human activities and global warming
	12.7. Difference in Differences Treatment Effect Estimation
		12.7.1. Matched sampling for causal effects
	12.8. Panel Data Models
		12.8.1. Generalized method of moments estimator for panels
	12.9. Exact Stochastic Dominance
Appendix
	Classification of Vinod’s Papers Into Broad Themes
References
	Part 1: Alphabetic Reference to all Papers Except When Vinod is One of the (Co)Authors
	Part 2: Complete References Involving H. D. Vinod as the Author or One of the Coauthors (Reverse Chronological Order)
		Prefix J Means Journal Articles
		Prefix P Means Published Proceedings of Conferences
		Prefix B Means Books or Chapters in Books
		Prefix U for Widely Circulated Unpublished Documents
Index