- 利用分位数回归的统计与经济分析(英文版)
- - 作者：霍丽娟|责编:梁铜华
  - 出版社：北京理工大学
  - ISBN：9787568269308
  - 出版日期：2019/04/01
  - 页数：232
- 售价：32

内容大纲
异常值可以对传统经典的统计量产生相当大的影响，并导致对变量及变量之间关系的分析发生偏差，从而得出错误的结论。不仅传统的统计量，基于均值的最小二乘估计也会受到异常值的影响。如何在异常值存在与否的情况下都能获得更加稳健的结果，已经吸引了大量研究人员的兴趣，并且已经发表了大量有影响力的文献。多种稳健回归方法被学者们提出来。分位数回归，作为LAD从中位数向不同分位数的扩展，首先由Koenker和Bassett（1978）提出。由于它的稳健和有效性，同时允许研究人员不仅在中心而且在因变量的整个条件分布上研究经济变量之间的关系等优点，分位数回归被应用于经济和金融等许多学术领域。
本书对于在金融风险存在时稳健统计量计算的投资组合的表现进行了研究，并对分位数回归的理论和应用进行了研究，基于分位数回归进一步分析我国省际数据下以及86个非石油国家的经济增长趋同性，外国直接投资对增长的影响以及金融风险测量，以及风险度量等。本书读者适合为经济学专业高年级本科生及研究生。
作者介绍
目录
Chapter 1  Introduction
  1.1  Overview
  1.2  Quantile Regression and Its Applications
  References
Chapter 2  Robust Statistics and Robust Regressions
  2.1  Introduction to Classical and Robust Approaches to Statistics
  2.2  Least Squares Linear Regression
  2.3  Robust Regression
    2.3.1  Least Absolute Values Regression
    2.3.2  M-estimator
  2.4  Quantile Regression
    2.4.1  Quantile Regression Model
    2.4.2  The Finite-sample Distribution of Regression Quantiles
    2.4.3  Quantile Regression Asymptotics
    2.4.4  Wald Tests
    2.4.5  Estimation of Asymptotic Covariance Matrix
    2.4.6  Quantile Likelihood Ratio Tests
  References
Chapter  3 Robust Estimates of Covariance
  3.1  Conventional Measure of Covariance
  3.2  Robust Measures of Covariance
    3.2.1  Median Absolute Deviation About the Median (MAD)
    3.2.2  Gnanadesikan and Ketenring Robust Measures of Covariance
    3.2.3  M-estimates
    3.2.4  Minimum Volume Ellipsoid Estimate (MVE)
    3.2.5  S-estimates
    3.2.6  Minimum Covariance Determinant Estimate (MCD)
  3.3  An Alternative Robust Measure of Covariance
  3.4  Monte Carlo Simulations
  3.5  Empirical Application
    3.5.1  Empirical Comparison of Robust Estimates
    3.5.2  Portfolio Performances of Robust Covariances
  3.6  Conclusion
  3.7  Appendix: Derivation of Conventional Covariance with Outlier(s)
  References
Chapter 4  Quantile Regression Serial Correlation Tests
  4.1  Spurious Autocorrelation in Quantile Models
    4.1.1  Standard LM Test for Linear Model with AR(p) Errors
    4.1.2  Theoretical Explanation to the Occurance of Spurious Autocorrelation
  4.2  Correctly-sized Tests
    4.2.1  QF test
    4.2.2  The QR-LM Test
  4.3  Monte-Carlo Simulations
  4.4  An Empirical Example
  4.5  Conclusion
  4.6  Appendix
  References
Chapter 5  Growth Empirics Based on IV Panel Quantile Regression
  5.1  Economic Growth Convergence
  5.2  Quantile Regression for Panel Data Model with Fixed Effects

  5.3  Growth Convergence at the Conditional Mean
  5.4  Growth Convergence at Different Conditional Quantiles
  5.5  Empirical Results from 86 Non-oil Countries
    5.5.1  Data and Samples
    5.5.2  Empirical Results
    5.5.3  Conclusion
  5.6  Evidence from China Provincial Panel Data
    5.6.1  Literature on China's Regional Economic Development
    5.6.2  Model and Data
    5.6.3  Empirical Results
    5.6.4  Conclusion from China's Empirical Results
  References
Chapter 6  The Impact of FDI on Economic Growth: an Empirical Evidence from IV Panel Quantile Regression
  6.1  FDI and Economic Growth
  6.2  IV Quantile Regression Model for Panel Data with Fixed Effects
  6.3  Data and Empirical Results
  6.4  Conclusion
  6.5  Appendix
  References
Chapter 7  Financial Risk Measurement: CoVaR
  7.1  Financial Risk Transition Mechanism and Source of Risk in China
    7.1.1  The Transmission Mechanism of Financial Risk in China
    7.1.2  Sources of Financial Risk in China
  7.2  Risk Measurements: VaR, CoVaR, and △CoVaR
    7.2.1  Definition of VaR
    7.2.2  Calculation of VaR
    7.2.3  Definition of CoVaR and △CoVaR
    7.2.4  Calculation of CoVaR
    7.2.5  CoVaR Model Based on Quantile Regression
  7.3  Empirical Study on Systemic Financial Risks in China
    7.3.1  Data Selection
    7.3.2  Data Processing and Descriptive Statistics
    7.3.3  Identification of Systemically Important Financial Institutions
  7.4  Static Risk Contribution of Financial Sub-industries on Financial System
    7.4.1  Data Selection
    7.4.2  Data Processing and Descriptive Statistics
  7.5  Risk Spillover Effects Between Financial Sub-sectors
    7.5.1  Static Risk Spillover Effects Between Financial Sub-sectors
    7.5.2  Dynamic Risk Spillover Effects Between Financial Sub-industries
  7.6  Conclusion
  References
Chapter 8  Markov Regime Switching in Quantile Autoregression Stock Market Return Model
  8.1  Introduction to Markov-switching model
  8.2  Markov-switching Quantile Autoregressive Model for Stock Market Returns
  8.3  Data Description and Empirical Results
  8.4  Conclusion
  References

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