Financial, Macro and Micro Econometrics Using R: Volume 42

Description

Financial, Macro and Micro Econometrics Using R, Volume 42, provides state-of-the-art information on important topics in econometrics, including multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, financial market jumps and co-jumps, among other topics. Presents chapters authored by distinguished, honored researchers who have received awards from the Journal of Econometrics or the Econometric SocietyIncludes descriptions and links to resources and free open source RGives readers what they need to jumpstart their understanding on the state-of-the-art Series Page Copyright 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