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Financial, Macro and Micro Econometrics Using R, Vol 42. Handbook of Statistics

  • ID: 4829378
  • Book
  • January 2020
  • 349 Pages
  • Elsevier Science and Technology

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 Society
  • Includes descriptions and links to resources and free open source R
  • Gives readers what they need to jumpstart their understanding on the state-of-the-art

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Part I: Finance 1. Financial econometrics and big data: A survey of volatility estimators and tests for the presence of jumps and co-jumps Arpita Mukherjee, Weijia Peng, Norman R. Swanson and Xiye Yang 2. Real time monitoring of asset markets: Bubbles and crises Peter C.B. Phillips and Shuping Shi 3. Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction Jianghao Chu, Tae-Hwy Lee and Aman Ullah

Part II: Macro Econometrics 4. Mixed data sampling (MIDAS) regression models Eric Ghysels, Virmantas Kvedaras and Vaidotas Zemlys-Balevicius 5. Encouraging private corporate investment in India Hrishikesh Vinod, Honey Karun and Lekha S. Chakraborty 6. High-mixed frequency forecasting methods in R-With applications to Philippine GDP and inflation Roberto S. Mariano and Suleyman Ozmucur 7. Nonlinear time series in R: Threshold cointegration with tsDyn Matthieu Stigler

Part III: Micro Econometrics 8. Econometric analysis of productivity: Theory and implementation in R Robin C. Sickles, Wonho Song and Valentin Zelenyuk 9. Stochastic frontier models using R Giancarlo Ferrara
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