Financial, Macro and Micro Econometrics Using R: Volume 42

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Contributors
Preface
Financial econometrics and big data: A survey of volatility estimators and tests for the presence of jumps and
co-jumps
	Introduction
	Setup
	Realized measures of integrated volatility
		Realized volatility
		Realized bipower variation
		Tripower variation
		Two-scale realized volatility
		Multiscale realized volatility
		Realized kernel
		Truncated realized volatility
		Modulated bipower variation
		Threshold bipower variation
		Subsampled realized kernel
		MedRV and MinRV
	Jump testing
		Barndorff-Nielsen and Shephard test
		Lee and Mykland test
		Jiang and Oomen test
		Aït-Sahalia and Jacod test
		Podolskij and Ziggel (PZ) test
		Corradi, Silvapulle, and Swanson test
	Co-jump testing
		BLT co-jump testing
		JT co-jump testing
		MG threshold co-jump test
		GST co-exceedance rule
		CKR co-jump testing
	Empirical experiments
		Data description
		Methodology
		Findings
	Conclusion
	R code
	References
Real time monitoring of asset markets: Bubbles and crises
	Introduction
	The PSY Procedure
		The Augmented Dickey-Fuller test
		The Recursive Evolving Algorithm
	The PSY Test for Bubble Identification
		The Rationale
		Consistency
	The PSY Test for Crisis Identification
		The Rationale
		Consistency
	A New Composite Bootstrap
	Empirical Applications with R
		Example 1: The S&P 500 Market
		Example 2: Credit Risk in the European Sovereign Sector
	Conclusion
	References
	Further reading
Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability predicti
	Introduction
	AdaBoost
	Extensions to AdaBoost algorithms
		Real AdaBoost
		LogitBoost
		Gentle AdaBoost
	Alternative classification methods
		Deep Neural Network
		Logistic regression with LASSO
		Semiparametric single-index model
	Monte Carlo
	Applications
	Conclusions
	Acknowledgments
	References
Mixed data sampling (MIDAS) regression models
	Introduction
	A stylized MIDAS regression model
		A few examples of the constraint function h
		Selection of h, d, and k
		Statistical inference
	Linear and quasi-linear MIDAS models (affine g)
		Unconstrained MIDAS
		MIDAS
		MIDAS with nonparametric smoothing of weights
	Nonlinear parametric MIDAS models
		General considerations
		Logistic smooth transition MIDAS (LSTR-MIDAS)
		MIDAS with min-mean-max effects (MMM-MIDAS)
	Semiparametric MIDAS models
		MIDAS with partially (quasi)linear effects (PL-MIDAS)
		The single index MIDAS model (SI-MIDAS)
	Illustration with simulated data
		Data generation
		Estimation
	Empirical examples
		Okun's law
		Inflation and the effective federal funds rate
	References
Encouraging private corporate investment in India
	Introduction
	Literature review
	Interpreting data and model implications
	Estimation and results
	Data abbreviations and sources
	Causality results
	Conclusion
	Appendix
	References
High-mixed frequency forecasting methods in R-With applications to Philippine GDP and inflation
	Introduction
	Alternative forecasting models in this study
		Quarterly models
		Monthly models
		MIDAS regressions
		Mixed-frequency dynamic latent factor models (MF-DLFM)
	Application to forecasting Philippine GDP and inflation and computer implementation in R
		Getting data with R
		Statistical properties of quarterly real GDP and GDP deflator
			Descriptive statistics for GDP and GDP deflator
			Unit root tests for GDP and GDP deflator
			Autocorrelation and partial autocorrelation functions GDP and GDP deflator
			Correlations and cross-correlations: GDP and GDP deflator
	Estimated models in R
		Box-Jenkins (ARIMA(p,d,q)) univariate time-series models
		Vector autoregressive models
		Bridge equations
		Principal components using monthly indicators and bridge equations using principal components
		MIDAS models
		ADL-MIDAS
		MIDAS-VAR
		VARX and VARXM
		ARIMA and MIDAS
		Factor MIDAS
		Dynamic factor models-Small number of indicators
		Dynamic factor models-Large number of indicators
	Comparison of forecasts and concluding remarks
	Acknowledgments
	References
	Further reading
Nonlinear time series in R: Threshold cointegration with tsDyn
	Introduction: Linear and threshold cointegration
	Estimation, testing, and interpretation
		Estimation and testing for cointegration
		Estimation and testing for threshold models
		Estimation and testing for threshold cointegration models
			Two-step approach
			Direct test: No cointegration vs threshold cointegration
			A small remark on testing for threshold cointegration
		Estimation of a threshold estimated model
		(Generalized) impulse response functions
	The tsDyn package
		Linear models: AR, VAR, and VECM in tsDyn
		Univariate models: SETAR
		Multivariate models: TVAR and TVECM
	Empirical application
		Unit roots and linear cointegration
		Threshold cointegration in the univariate residual-based approach
		Threshold cointegration in the multivariate system-based approach
	Conclusion
	Acknowledgment
	References
	Further reading
Econometric analysis of productivity: Theory and implementation in R
	Introduction
	Why estimate production (technical) efficiency?
	Regression-based methods to estimate production efficiency
		The stochastic frontier paradigm
			Corrected OLS
			Stochastic frontier model
				Estimation of individual inefficiencies
			Panel stochastic frontiers
			Second- and third-generation stochastic frontier models
				Factor models and SFA
				True fixed effects and SFA
	Envelopment estimators
		The origins of DEA
		The basic DEA model
		The myriad of DEA models
			Relaxing constant returns to scale and convexity
			Modeling with undesirable outputs or with congesting inputs
			Other streams of DEA
		Statistical analysis of DEA and FDH
	SFA efficiency software in R
		Basic model setup
		Figures and tables
		Different estimators
	DEA efficiency software in R
	Summary and final remarks
	Acknowledgments
	References
Stochastic frontier models using R
	Introduction
	Methods
		Contextual variables
		Spatial external factors
		P-splines: Computational aspects
	Numerical illustrations
		Example 1: Linear homoscedastic model
		Example 2: Nonlinear exponential homoscedastic model
		Example 3: Nonmonotone model
		Example 4: Quadratic polynomial model with heteroscedasticity
	Empirical application to crops data
	Conclusions
	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
	X