Conceptual Econometrics Using R (Handbook of Statistics)

Front Matter
Copyright
Contributors
Preface
Finite-sample inference and nonstandard asymptotics with Monte Carlo tests and R
	Introduction
	Monte Carlo tests with continuous and discrete test statistics
	Pivotal Monte Carlo tests in R
	Example: Two-sample goodness-of-fit test
	Maximized Monte Carlo tests
	Asymptotic MMC tests
	MMC tests in R
		Global Optimization
			gridSearch
			GenSA
			psoptim
			GA
		Optimal Choice
	MMC tests: Examples
		Behrens-Fisher problem
		Unit root tests in autoregressive models
			Framework
			Code
	Conclusion
	Acknowledgments
	References
New exogeneity tests and causal paths
	Introduction
		Computational agenda and decision rules
	Kernel regression review
		Counterfactuals in kernel regressions
		Kernel regression and consistency
	Cowles commission SEMs
		Need for alternative exogeneity tests
			Weak exogeneity and its limitations
			Hausman-Wu test and its limitations
			Limitations of IV-based tests
			OLS super-consistency implications
			CC-SEM implications for stochastic kernel causality
	Stochastic kernel causality by three criteria
		First criterion Cr1 for XiXj
		Second criterion Cr2 for XiXj
		Third criterion Cr3 for XiXj
	Numerical evaluation of Cr1 and Cr2
	Stochastic dominance of four Orders
		Weighted sum of signs of Cu(sd1) to Cu(sd4)
		Unanimity index summarizing signs
	Review of decision rule computations
	Simulation for checking decision rules
	A bootstrap exogeneity test
		Summarizing sampling distribution of ui
	Application example
		Variables affecting term spread
		Bootstrap inference on Estimated Causality Paths
	Summary and final remarks
	Acknowledgments
	Review of graph theory
	For R code
	References
Adjusting for bias in long horizon regressions using R
	Introduction
	Long horizon regressions
	Bias adjustment for long horizon regressions
		Introduction
		R function longhor1
		R function longhor
		R functions proc_vb_ma0 and proc_vb_maq
	R code for an empirical application
	Acknowledgment
	References
Hypothesis testing, specification testing, and model selection based on the MCMC output using R*
	Introduction
	MCMC and its implementation in R
	Hypothesis testing based on the MCMC output
		Hypothesis testing under decision theory
		The choice of loss function for hypothesis testing
			BFs and 0-1 loss function
			Bernardo and Rueda (2002) and the KL loss function
			Li and Yu (2012) and the Q loss function
			Li et al. (2014) and LR-type loss function
			Li et al. (2015) and LM-type loss function
			Li et al. (2019) and Wald-type loss function
	Specification testing based on the MCMC output
	Model selection based on the MCMC output
		DIC for regular models
		Bayesian predictive distribution as the loss function
		Integrated DIC for latent variable models
		Computing IDIC for latent variable models
	Empirical illustrations
		Statistical inference in asset pricing models
			Hypothesis testing for asset pricing models
			Specification testing for asset pricing models
			Model comparison for asset pricing models
		Statistical inference in stochastic volatility models
			Hypothesis testing for stochastic volatility models
			Specification testing for SV models
			Model comparison of SV models
	Concluding remarks
	References
	Further reading
Dynamic panel GMM using R*
	Introduction
	A dynamic panel model with macro drivers
	R code for dynamic panel estimation
		Data generation
		Within-group estimation
		Difference GMM
		System GMM
		Code verification and comparison
	Simulation results
	Conclusion
	References
	Further reading
Vector autoregressive moving average models
	Introduction
	Vector autoregressive moving average models
	Identifiability of VARMA systems
	State space models
	Identifiability of state space models
	Maximum likelihood estimation
	Initial estimates
		Estimation of VARMA models-The Hannan, Rissanen, Kavalieris procedure
		Estimation of state space models-The CCA subspace method
	Model selection
	Discussion and notes
		Summary
	Acknowledgement
	References
Multivariate GARCH models for large-scale applications: A survey
	Introduction
	Multivariate generalization of GARCH models
	Multivariate distributions
		Multivariate Normal
		Multivariate Student
		Multivariate Laplace
		Multivariate Generalized Hyperbolic distribution
		Copula distributions
	Generalized Orthogonal GARCH models
	Conditional correlation GARCH models
	BIP and GAS MGARCH models
	MGARCH models using high-frequency returns
		Realized BEKK
		HEAVY
		Realized DCC
		Other approaches
	Illustration
	Conclusion
	References
Modeling fractional responses using R
	Introduction
	The base case: Cross-sectional data and no unobserved heterogeneity
		Conditional mean models
		Two-part models
		Partial effects
		Specification tests
	Linearized- and exponential-fractional estimators
		Framework
		Neglected heterogeneity
		Endogenous regressors
		Smearing estimation of partial effects
	Panel data estimators
		Framework
		Pooled random and fixed effects estimators
		Fixed effects estimators based on quasi- and mean-differences
		Correlated random effects estimators
	Future developments
	Acknowledgments
	References
Quantitative game theory applied to economic problems
	Introduction
	Cooperative game theory
		The core
		The Shapley value
		The nucleolus
		Voting power
	Marketing and game theory
		The classic consumer theory
		Attribution models
			Sales game
	Claims problems
		Claims rules
		Obtaining fishing quotas
	Concluding remarks
	Acknowledgments
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W