Financial, macro and micro econometrics using R /

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 forecast...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Vinod, Hrishikesh D., 1939- (Editor ), Rao, C. Radhakrishna (Calyampudi Radhakrishna), 1920-2023 (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : North-Holland is an imprint of Elsevier, [2020]
Colección:Handbook of statistics (Amsterdam, Netherlands) ; v. 42.
Temas:
Econometrics.
R (Computer program language)
�Econom�etrie.
R (Langage de programmation)
Econometrics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Financial, Macro and Micro Econometrics Using R
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Part I: Finance
  • Chapter 1: Financial econometrics and big data: A survey of volatility estimators and tests for the presence of jumps and ...
  • 1. Introduction
  • 2. Setup
  • 3. Realized measures of integrated volatility
  • 3.1. Realized volatility
  • 3.2. Realized bipower variation
  • 3.3. Tripower variation
  • 3.4. Two-scale realized volatility
  • 3.5. Multiscale realized volatility
  • 3.6. Realized kernel
  • 3.7. Truncated realized volatility
  • 3.8. Modulated bipower variation
  • 3.9. Threshold bipower variation
  • 3.10. Subsampled realized kernel
  • 3.11. MedRV and MinRV
  • 4. Jump testing
  • 4.1. Barndorff-Nielsen and Shephard test
  • 4.2. Lee and Mykland test
  • 4.3. Jiang and Oomen test
  • 4.4. A�it-Sahalia and Jacod test
  • 4.5. Podolskij and Ziggel (PZ) test
  • 4.6. Corradi, Silvapulle, and Swanson test
  • 5. Co-jump testing
  • 5.1. BLT co-jump testing
  • 5.2. JT co-jump testing
  • 5.3. MG threshold co-jump test
  • 5.4. GST co-exceedance rule
  • 5.5. CKR co-jump testing
  • 6. Empirical experiments
  • 6.1. Data description
  • 6.2. Methodology
  • 6.3. Findings
  • 7. Conclusion
  • Appendix. R code
  • References
  • Chapter 2: Real time monitoring of asset markets: Bubbles and crises**This chapter draws on several of our earlier works ...
  • 1. Introduction
  • 2. The PSY Procedure
  • 2.1. The Augmented Dickey-Fuller test
  • 2.2. The Recursive Evolving Algorithm
  • 3. The PSY Test for Bubble Identification
  • 3.1. The Rationale
  • 3.2. Consistency
  • 4. The PSY Test for Crisis Identification
  • 4.1. The Rationale
  • 4.2. Consistency
  • 5. A New Composite Bootstrap
  • 6. Empirical Applications with R
  • 6.1. Example 1: The S & P 500 Market
  • 6.2. Example 2: Credit Risk in the European Sovereign Sector
  • 7. Conclusion
  • References
  • Further reading
  • Chapter 3: Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability predicti
  • 1. Introduction
  • 2. AdaBoost
  • 3. Extensions to AdaBoost algorithms
  • 3.1. Real AdaBoost
  • 3.2. LogitBoost
  • 3.3. Gentle AdaBoost
  • 4. Alternative classification methods
  • 4.1. Deep Neural Network
  • 4.2. Logistic regression with LASSO
  • 4.3. Semiparametric single-index model
  • 5. Monte Carlo
  • 6. Applications
  • 7. Conclusions
  • Acknowledgments
  • References
  • Part II: Macro Econometrics
  • Chapter 4: Mixed data sampling (MIDAS) regression models
  • 1. Introduction
  • 2. A stylized MIDAS regression model
  • 2.1. A few examples of the constraint function h
  • 2.2. Selection of h, d, and k
  • 2.3. Statistical inference
  • 3. Linear and quasi-linear MIDAS models (affine g)
  • 3.1. Unconstrained MIDAS
  • 3.2. MIDAS
  • 3.3. MIDAS with nonparametric smoothing of weights
  • 4. Nonlinear parametric MIDAS models
  • 4.1. General considerations
  • 4.2. Logistic smooth transition MIDAS (LSTR-MIDAS)

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