Name： HONG Yongmiao           Title： Professor              E-mail：ymhong@amss.ac.cn            Personal homepage：             http://people.ucas.ac.cn/~ymhong?language=en             Research Interests:             Model specification testing             Nonlinear time series analysis             Financial econometrics             Empirical studies on Chinese economy and financial markets

 

Biography

Professor Yongmiao Hong is currently a distinguished research fellow at Academy of Mathematics and Systems Science and Center for Forecasting Science, Chinese Academy of Sciences (CAS), and a special-term professor at School of Economics and Management, University of Chinese Academy of Sciences. He is a Fellow of The World Academy of Sciences (TWAS) for the advancement of science in developing countries, a Fellow of The Econometric Society, a Fellow of International Association of Applied Econometrics (IAAE), and a Senior Fellow of Rimini Center for Economic Analysis (RCEA).

Before he joined CAS and UCAS, Professor Hong was the Ernest S. Liu Professor of Economics and International Studies, a Professor of Statistics, and a field member in Center of Applied Mathematics at Cornell University in the United States.

Professor Hong received his BS in Physics in 1985 and MA in Economics in 1988 from Xiamen University, and his PhD in Economics from University of California at San Diego in 1993. Upon graduation, he joined as a faculty member in the Department of Economics and Department of Statistics and Data Science at Cornell University and later became the Ernest S. Liu Professor of Economics and International Studies from 2010 to 2020. He moved from Cornell University to University of Chinese Academy of Sciences in December, 2020. He was President of Chinese Economists Society in North America from 2009 to 2010. 

Professor Hong's research interests include model specification testing, nonlinear time series analysis, financial econometrics, and empirical studies on Chinese economy and financial markets. He publishes refereed articles in mainstream economic, financial and statistical journals such as Annals of Statistics, Biometrika, Econometric Theory, Econometrica, International Economic Review, Journal of American Statistical Association, Journal of Applied Econometrics, Journal of Business and Economic Statistics, Journal of Econometrics, Journal of Political Economy, Journal of Royal Statistical Society (Series B), Quarterly Journal of Economics, Review of Economic Studies, Review of Economics and Statistics, and Review of Financial Studies.

 

Awards and Honors

1. September 2020    Senior Fellow, Rimini Centre for Economic Analysis (RCEA)
2. November 2019    Fellow, International Association for Applied Econometrics (IAAE)
3. November 2018    Fellow, The Econometric Society
4. August 2018     40 Most Influential Chinese Overseas Students of the Past Four Decades, by The China Press and Eastday.com
5. June 2018    Elsevier Awards for the Best Papers for the paper entitled as “Do China’s High-speed-rail Projects Promote Local Economy? New Evidence from a Panel Data Approach,” coauthored with X. Ke, H. Chen and C. Hsiao, China Economic Review (2017).
6. 2017    Charter Fellow, Institute for Nonlinear Dynamical Inference (INDI), Moscow, Russia
7. November 2015    Fellow, The World Academy of Science (TWAS) for Advance of Sciences in Developing Countries
8. 2014-2019   Annual Most Cited Chinese Scholars in Economics, Econometrics and Finance, Published by Elsevier
9. March 2006       Tjalling C. Koopmans Econometric Theory Prize2006 for the paper entitled as “Diagnostic Checking for the Adequacy of Nonlinear Time Series Models,” coauthored with T.H. Lee, Econometric Theory (2003), Volume 19, 1065-1121.
10. May 2003       Cornell Hatfield Fund for Innovating Undergraduate Teaching
11. 1989-1993     Academic Excellence Awards, Department of Economics, University of California, San Diego 

 

Research Summary

Professor Hong's research interests include model specification testing and evaluation, nonlinear time series analysis, locally stationary time series analysis, generalized spectral analysis, financial econometrics, interval-valued time series data analysis, and machine learning-based time series analysis. 

On specification testing, and evalution Professor Hong develops a class of sophisticated semiparametric tests for econometric models of cross-sectional, time series, and panel data respectively. The basic idea is to compare a null econometric model with a flexible nonparametric alternative, developing tests that have power against various model misspecifications. Nonparametric tools used include orthogonal series, kernel, and wavelet methods. In Hong and White (1995, Econometrica), a generalized F test is developed by comparing the sums of squared residuals of a parametric regression model and a nonparametric series regression model where the order of the series expansion grows with the sample size, ensuring the test able to detect various functional form misspecifications. This methodology is extended in Hong (1996, Econometrica) to test a dynamic regression model. This is achieved by comparing a nonparametric kernel estimator for the spectral density of the estimated model residual with the flat spectrum of a serially uncorrelated white noise. An optimal kernel function or weighting function for lags is derived to ensure that the proposed test has optimal power. This test is generalized in Hong and Kao (2004, Econometrica) to a panel data regression model where wavelets are used in nonparametric spectral density estimation. Hong and Lee (2013, Annals of Statistics) show that a loss function-based specification testing approach is asymptotically more efficient than a generalized likelihood ratio test approach which includes the tests based on comparing sums of squared residuals. 

Another main area of Professor Hong's research interests is nonlinear time series analysis, locally stationary time series analysis, and generalized spectral analysis. Observing that many economic and financial time series are serially uncorrelated but not serially independent, Hong (1998, Journal of Royal Statistical Society Series B; 2000, Journal of Royal Statistical Society Series B) develops nonparametric Hoeffding-type measures of and tests for serial dependence in a time series which can detect subtle dependence structure. In particular, Hong and White (2005, Econometrica) develop a challenging asymptotic distribution theory for smoothed nonparametric entropy measures of serial dependence which was not previously available in the literature. Chen and Hong (2012, Econometrica) propose a new approach to testing parameter constancy of a time series regression model against smooth structural changes as well as abrupt structural breaks. 

On another important development, Hong (1999, Journal of American Statistical Association) proposes a new analytic tool for nonlinear economic time series -- the generalized spectrum. The basic idea is to transform original time series data via a complex-valued exponential function and then consider the spectrum of the transformed series. This can capture both linear and nonlinear serial dependence while avoiding the drawbacks of the conventional spectrum, which cannot capture nonlinear serial dependence, and higher order spectra (e.g., bispectrum), which requires the existence of restrictive moment conditions. Real data applications (e.g., Hong and Lee (2003a, Review of Economics and Statistics)) show that the generalized spectral tool can detect dynamic structures which would otherwise be neglected by conventional tools, thus offering new insights into economic and financial time series data. The generalized spectrum is also used to develop powerful procedures for nonlinear time series analysis. For example, Hong and Lee (2003b, Econometric Theory) use it to check any neglected dependence structure in the estimated standardized residuals of a nonlinear time series model, and Hong and Lee (2005, Review of Economic Studies) use the first order partial derivative of the generalized spectrum to focus on neglected nonlinearity in the conditional mean dynamics of a time series model. Hong and Lee (2003b, Econometric Theory) win the Koopman Econometrics Prize 2006. 

Hong also works on financial econometrics. Hong and Li (2005, Review of Financial Studies) develop a nonparametric specification test for continuous-time models using discretely sampled data. The basic idea is to consider transformed data via the model-implied dynamic transition density, which should be independent and uniformly distributed if the continuous-time model is correctly specified. The proposed test is generally applicable and robust to persistent dependence in data because the i.i.d. property holds even if the original data display highly persistent dependence. Hong, Tu and Zhou (2007, Review of Financial Studies) develop copula-based tests for asymmetric dependence in asset returns and assess their economic implications. 

Professor Hong has recently started a research on modeling interval-valued time series data. An interval-valued observation in a time period contains more information than a point-valued observation in the same time period. Examples of interval data include the maximum and minimum temperatures in a day, the maximum and minimum GDP growth rates in a year, the maximum and minimum stock prices in a trading day, the ask and bid prices in a trading period, the long term and short term interest, and the 90%-tile and 10%-tile incomes of a cohort in a year, etc. Interval forecasts may be of direct interest in practice, as it contains information on the range of variation and the level of economic variables. Moreover, the informational advantage of interval data can be exploited for more efficient econometric estimation and inference. Hong and his coauthors (e.g) Sun, Han, Hong and Wang (2018, Journal of Econometrics) propose a new class of autoregressive conditional interval (ACI) models for interval-valued time series data. A minimum distance estimation method is developed to estimate the parameters of an ACI model. Both simulation and empirical studies show that the use of interval time series data can provide more accurate estimation for model parameters in terms of mean squared error criterion.

 

Publications

1. “Solving Euler equations via two-stage nonparametric penalized splines,” with L. Cui and Y. Li, Journal of Econometrics (2020), https://doi.org/10.1016/j.jeconom.2020.04.042.
2. “Time-varying model averaging,” with T. Lee, Y. Sun, S. Wang and X. Zhang, Journal of Econometrics (2020), https://doi.org/10.1016/j.jeconom.2020.02.006.
3. “A model-free consistent test for structural change in regression possibly with endogeneity,” with Z. Fu, Journal of Econometrics 211 (2019), 206-242.
4. “Asymmetric pass-through of oil prices to gasoline prices with interval time series modelling,” with Y. Sun, X. Zhang and S. Wang, Energy Economics 78 (2019), 165-173.
5. “Nowcasting China’s GDP using a Bayesian approach,” with Y. Zhang, C. Yu and H. Li, Journal of Management Science and Engineering 3 (2018), 232-258.
6. “Advance in theoretical econometrics—Essays in honor of Takeshi Amemiya,” with Z. Cai and C. Hsiao, Journal of Econometrics 206 (2018), 279-281.
7. “Econometric modeling and economic forecasting,” with Z. Cai and S. Wang, Journal of Management Science and Engineering 3 (2018), 179-182.
8. “Threshold autoregressive models for interval-valued time series data,” with Y. Sun, A. Han, and S. Wang, Journal of Econometrics 206 (2018), 414-446.
9. “Characteristic function-based testing for conditional independence via a nonparametric regression approach,” with X. Wang, Econometric Theory 34 (2018), 815-849.
10. “Testing strict stationarity with applications to macroeconomic time series,” with X. Wang and S. Wang, International Economic Review 58 (2017), 1227-1277.
11. “A general approach to testing volatility models in time series,’’ with Y.J. Lee, Journal of Management Science and Engineering 2 (2017), 1-33.
12. “An efficient integrated nonparametric entropy estimator of serial dependence,” with X. Wang, W. Zhang and S. Wang, Econometric Reviews 36 (2017), 728–780.
13. “Do China’s high-speed-rail projects promote local economy? New evidence from a panel data approach,” with X. Ke, H. Chen and C. Hsiao, China Economic Review 44 (2017), 203-226.
14. “A vector autoregressive moving average model for interval-valued time series data,” with A. Han, S. Wang and X. Yun, Advances in Econometrics 36 (2016), edited by R. Hill, G. Gonzalez-Rivera and T. Lee, pp.417-460.
15. “Analysis of crisis impact on crude oil prices: A new approach with interval time series modeling,” with W. Yang, A. Han and S. Wang, Quantitative Finance 16 (2016), 1917-1928.
16. “Detecting for smooth structural changes in GARCH models,” with B. Chen, Econometric Theory 32 (2016), 740-791.
17. “Impact of the new health care reform on hospital expenditures in China: A case study from a pilot city,” with J. Yang and S. Ma, China Economic Review 39 (2016), 1-14.
18. “Time-varying Granger causality tests for applications in global crude oil markets,” with F. Lu, S. Wang, K. Lai and J. Liu, Energy Economics. 42 (2014), 289-298.
19. “A unified approach to validating univariate and multivariate conditional distribution models in time series,” with B. Chen, Journal of Econometrics 178 (2014), 22-44.
20. “A loss function approach to model specification testing and its relative efficiency,” with Y. Lee, Annals of Statistics 41 (2013), 1166-1203.
21. “How smooth is price discovery? Evidence from cross-listed stock trading,” with H. Chen and P.M. Choi, Journal of International Money and Finance 32 (2013), 668-699.
22. “Productivity spillovers among linked sectors,” with L. Peng, China Economic Review 25 (2013), 44-61.
23. “Testing for smooth structural changes in time series models via nonparametric regression,” with B. Chen, Econometrica 80 (2012), 1157-1183.
24. “Testing for the Markov property in time series,” with B. Chen, Econometric Theory 28 (2012), 130-178.
25. “Are corporate bond market returns predictable?” with H. Lin and C. Wu, Journal of Banking and Finance 36 (2012), 2216-2232.
26. “Testing the structure of conditional correlations in multivariate GARCH models: A generalized cross-spectrum approach,” with N. McCloud, International Economic Review 52 (2011), 991-1037.
27. “Generalized spectral testing for multivariate continuous-time models,” with B. Chen, Journal of Econometrics 164 (2011), 268-293.
28. “Detecting misspecifications in autoregressive conditional duration models and non-negative time-series processes,” with Y.-J. Lee, Journal of Time Series Analysis 32 (2011), 1-32.
29. “Characteristic function-based testing for multifactor continuous-time Markov models via nonparametric regression,” with B. Chen, Econometric Theory 26 (2010), 1115-1179.
30. “Modeling the dynamics of Chinese spot interest rates,” with H. Lin and S. Wang, Journal of Banking and Finance 34 (2010), 1047-1061.
31. “Granger causality in risk and detection of extreme risk spillover between financial markets,” with Y. Liu and S. Wang, Journal of Econometrics 150 (2009), 271–287.
32. “Central limit theorems for generalized U-statistics with applications in nonparametric specification,” with J. Gao, Journal of Nonparametric Statistics 20 (2008), 61-76.
33. “Interval time series analysis with an application to the Sterling-Dollar exchange rate,” with A. Han, K. K. Lai and S. Wang, Journal of Systems Science and Complexity 21 (2008), 558-573.
34. “An empirical study on information spillover effects between the Chinese copper futures market and spot market,” with X. Liu, S. Cheng, S. Wang and Y. Li, Physica A 387 (2008), 899-914.
35. “Serial correlation and serial dependence,” The New Palgrave Dictionary in Economics, 2008, 2nd Edition, ed. Steven Durlauf.
36. “Model-free evaluation of directional predictability in foreign exchange markets,” with J. Chung, Journal of Applied Econometrics 22 (2007), 855-889.
37. “Asymmetries in stock returns: Statistical tests and economic evaluation,” with J. Tu and G. Zhuo, Review of Financial Studies 20 (2007), 1547-1581.
38. “Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates,” with H. Li and F. Zhao, Journal of Econometrics 141 (2007), 736-776.
39. “An improved generalized spectral test for time series models with conditional heteroskedasticity of unknown form,” with Y. Lee, Econometric Theory 23 (2007), 106-154.
40. “Validating forecasts of the joint probability density of bond yields: Can affine term structure models beat random walk?” with A. Egorov and H. Li, Journal of Econometrics 135 (2006), 255-284.
41. “Asymptotic theory for nonparametric entropy-based measure of serial dependence,” with H. White, Econometrica 73 (2005), 837-901.
42. “Generalized spectral testing for conditional mean models in time series with conditional heteroskedasticity of unknown form,” with Y. Lee, Review of Economic Studies 72 (2005), 499-541.
43. “Nonparametric specification testing for continuous-time models with applications to spot interest rates,” with H. Li, Review of Financial Studies 18 (2005), 37-84.
44. “Wavelet-Based testing for serial correlation of unknown form in panel models,” with C. Kao, Econometrica 72 (2004), 1519-1563.
45. “Out-of-sample performance of discrete-time short-term interest models,” with H. Li and F. Zhao, Journal of Business and Economic Statistics 22 (2004), 457-473.
46. “Inference on predictability of exchange rates via generalized spectrum and nonlinear time series models,” with T. H. Lee, Review of Economics and Statistics, 85 (2003), 1048-1062.
47. “Diagnostic checking for the adequacy of nonlinear time series models,” with T.H. Lee, Econometric Theory 19 (2003), 1065-1121.
48. “Nonparametric methods if continuous-time finance: A selective review,” with Z. Cai, in Recent Advances and Trends in Nonparametric Statistics, (eds.) M. Akritas and D. Politis, Elsevier: New York, 2003, pp.283-302.
49. “Testing for independence between two stationary time series via the empirical characteristic function,” Annals of Economics and Finance 2 (2001), 123-164.
50. “One-sided testing for ARCH effects using wavelets,” with J. Lee, Econometric Theory 17 (2001), 1051-1081.
51. “A test for volatility spillover with application to exchange rates,” Journal of Econometrics 103 (2001), 183-224.
52. “Testing serial correlation of unknown form via wavelet methods,” with J. Lee, Econometric Theory 17 (2001), 386-423.
53. “Modeling the impact of overnight surprises on intra-daily stock returns,” with G. Gallo and T.-H. Lee, Proceedings for Business and Economic Statistics (2001), American Statistical Association.
54. “Generalized spectral tests for serial dependence,” Journal of the Royal Statistical Society, Series B (Statistical Methodology) 62 (2000), 557-574.
55. “Hypothesis testing in time series via the empirical characteristic function: A generalized spectral density approach,” Journal of the American Statistical Association 94 (1999), 1201-1220.
56. “M-testing using finite and infinite dimensional parameter estimators,” with H. White, in Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W. J. Granger, (eds.) R. F. Engle and H. White, London: Oxford University Press, 1999, pp.326-365.
57. “A new ARCH test and its finite sample performance,” with R. Shehadeh, Journal of Business and Economic Statistics 17 (1999), 91-108.
58. “Testing for pairwise serial independence via the empirical distribution function,” Journal of the Royal Statistical Society Series B (Statistical Methodology) 60 (1998), 429-453.
59. “One-sided testing for autoregressive conditional heteroskedasticity in time series models,” Journal of Time Series Analysis 18 (1997), 253-277.
60. “Testing for independence between two covariance stationary time series,” Biometrika 83 (1996), 615-625.
61. “Consistent testing for serial correlation of unknown form,” Econometrica 64 (1996), 837-864.
62. “Consistent specification testing via nonparametric series regressions,” with H. White, Econometrica 63 (1995), 1133-1159.
63. “China’s evolving managerial labor market,” with T. Groves, J. McMillan and B. Naughton, Journal of Political Economy 103 (1995), 873-892.
64. “Productivity growth in Chinese state-run industry,” with T. Groves, J. McMillan and B. Naughton, in Studies on China’s State-owned Enterprise System Reforms, (eds.) F. Dong, Z. Tang and H. Du, Beijing: People’s Press, 1995.

 

Books

1. Foundations of Modern Econometrics: A Unified Approach, Singapore: World Scientific Publishing Company, 2020. 

Modern economies are full of uncertainties and risk. Economics studies resource allocations in an uncertain market environment. As a generally applicable quantitative analytic tool for uncertain events, probability and statistics have been playing an important role in economic research. Econometrics is statistical analysis of economic and financial data. In the past four decades or so, economics has witnessed a so-called 'empirical revolution' in its research paradigm, and as the main methodology in empirical studies in economics, econometrics has been playing an important role. It has become an indispensable part of training in modern economics, business and management. This book develops a coherent set of econometric theory, methods and tools for economic models. It is written as a textbook for graduate students in economics,business, management, statistics, applied mathematics, and related fields. It can also be used as a reference book on econometric theory by scholars who may be interested in both theoretical and applied econometrics. 

Contents

Chapter 1. Introduction to Econometrics

Chapter 2. General Regression Analysis

Chapter 3. Classical Linear Regression Models

Chapter 4. Linear Regression Models with Independent Observations

Chapter 5. Linear Regression Models with Dependent Observations

Chapter 6. Linear Regression Models Under Conditional Heteroskedasticity and Autocorrelation

Chapter 7. Instrumental Variables Regression

Chapter 8. Generalized Method of Moments Estimation

Chapter 9. Maximum Likelihood Estimation and Quasi-Maximum Likelihood Estimation

Chapter 10. Modern Econometrics: Retrospect and Prospect 

 

2. Probability and Statistics for Economists, Singapore: World Scientific Publishing Company, 2017.

 Probability and Statistics have been widely used in various fields of science, including economics. Like advanced calculus and linear algebra, probability and statistics are indispensable mathematical tools in economics. Statistical inference in economics, namely econometric analysis, plays a crucial methodological role in modern economics, particularly in empirical studies in economics. 

This textbook covers probability theory and statistical theory in a coherent framework that will be useful in graduate studies in economics, statistics and related fields. As a most important feature, this textbook emphasizes intuition, explanations and applications of probability and statistics from an economic perspective. 

"A focus on issues that are important in economic theory or finance is clear throughout the book, and is likely to be a precious guideline for students of economic disciplines. Graduate students in economics and finance will find this book a valuable tool which will provide them with a strong motivation to deepen their knowledge of probability and statistics, leading to a better understanding of economic and financial theory."

---- Mathematical Reviews Clippings

Probability and Statistics for Economists by Yongmiao Hong made it to BookAuthority's best Probability and Statistics books of all time! (100 Best Probability and Statistics Books of All Time)

Contents

Chapter 1. Introduction to Probability and Statistics

Chapter 2. Foundation of Probability Theory

Chapter 3. Random Variables and Univariate Probability Distributions

Chapter 4. Important Probability Distributions

Chapter 5. Multivariate Probability Distributions

Chapter 6. Introduction to Sampling Theory

Chapter 7. Convergences and Limit Theorems

Chapter 8. Parameter Estimation and Evaluation

Chapter 9. Hypothesis Testing

Chapter 10. Classical Linear Regression

 

3. Information Spillover Effect and Autoregressive Conditional Duration Models, with X. Liu, Y. Liu, and S. Wang, Oxfordshire: Routledge, 2015. 

This book studies the information spillover among financial markets and explores the intraday effect and ACD models with high frequency data. This book also contributes theoretically by providing a new statistical methodology with comparative advantages for analyzing co-movements between two time series. It explores this new method by testing the information spillover between the Chinese stock market and the international market, futures market and spot market. Using the high frequency data, this book investigates the intraday effect and examines which type of ACD model is particularly suited in capturing financial duration dynamics. 

Contents

Chapter 1. Introduction 

Chapter 2. Methodology to Detect Extreme Risk Spillover 

Chapter 3. VaR Estimation 

Chapter 4. Extreme Risk Spillover Between Chinese Stock Markets and International Stock Markets 

Chapter 5. Information Spillover Effects Between Chinese Futures Market and Spot Market 

Chapter 6. How Well Can Autoregressive Duration Models Capture the Price Durations Dynamics of Foreign Exchanges 

Chapter 7. Intraday Effect 

Chapter 8. Conclusions and Perspective Studies

 

Teaching & MOOC

COURSES TAUGHT

Advanced Topics on Nonparametric Analysis (Graduate)

Advanced Topics on Time Series Econometrics (Graduate)

Chinese Economy (Undergraduate)

Econometrics (Undergraduate and Graduate)

Financial Econometrics (Graduate)

Mathematical Economics (Undergraduate and Graduate)

Probability and Statistics for Econometrics (Undergraduate and Graduate)

Time Series Econometrics (Graduate)

 

TEACHING AND RESEARCH EXPERIENCE

December 2020-Present   Distinguished Research Fellow, Academy of Mathematics and Systems Science and Center for Forecasting Science, Chinese Academy of Sciences

December 2020-Present   Special-Term Professor, School of Economics and Management, University of Chinese Academy of Sciences

November 2010-December 2020   The Ernest S. Liu Professor of Economics and International Studies, Department of Economics, Cornell University

July 2016-June 2019   Director of Graduate Studies in Field of Economics, Cornell University

May 2007    Visiting Chair Professor, Department of Economics, National University of Singapore

July 2005-December 2020   Founding Director, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University

May 2003-December 2020  Member in Field of Applied Mathematics, Center of Applied Mathematics, Cornell University

April 2002-July 2005    Visiting Special-term Professor of Economics, School of Economics and Management, Tsinghua University

July 2001-December 2020 Professor, Department of Economics and Department of Statistical Science, Cornell University

January 1999-January 2000 Visiting Associate Professor, Department of Economics, Hong Kong University of Science and Technology

July 1998-June 2001 Tenured Associate Professor, Department of Economics and Department of Statistical Science, Cornell University

July 1997-June 1998 Assistant Professor and Member in Field of Statistics, Department of Statistical Science, Cornell University

July 1993-June 1998 Assistant Professor, Department of Economics, Cornell University

 

Online Courses 

1. Advanced Econometrics

https://www.icourse163.org/en/mooc/course/XMU1-1458062167

Syllabus

1. Introduction to Econometrics

2. General Regression Analysis

3. Classical Linear Regression Models

4. Linear Regression Models with I.I.D. Observations

5. Linear Regression Models with Dependent Observations

6. Linear Regression Models under Conditional Heteroskedasticity and Autocorrelation

7. Instrumental Variables Regression

8. Generalized Method of Moments Estimation

Textbook

Foundations of Modern Econometrics: A Unified Approach,, Singapore: World Scientific Publishing Company, 2020.

2. Probability and Statistics for Economists

https://www.icourse163.org/course/XMU-1206678826

https://www.bilibili.com/video/BV11t411A7bp

Syllabus

1. Introduction to Statistics and Econometrics

2. Foundation of Probability Theory

3. Random Variables and Univariate Probability Distributions

4. Important Probability Distributions

5. Multivariate Probability Distributions

6. Introduction to Statistic

7. Convergences and Limit Theorems

8. Parameter Estimation and Evaluation

9. Hypothesis Testing

10. Big Data, Machine Learning and Statistics

Textbook

Probability and Statistics for Economists, Singapore: World Scientific Publishing Company, 2017.

3. An Introduction to Nonparamteric Analysis in Time Series Econometrics

https://www.bilibili.com/video/BV1dp4y1S7G1

Syllabus

0. Course Introduction

1. Motivation

2. Kernel Density Method

3. Nonparametric Regression Estimation

4. Nonparametric Estimation of Time-Varying Models

5. Nonparametric Estimation in Frequency Domain

6. Conclusion

Lecture Notes

https://nonparametric.xmu.edu.cn/

 

Computer Codes

This section contains some of Professor Yongmiao Hong’s publications (in PDF format) and downloadable computer codes to implement the proposed econometric tests.

1.“Testing for smooth structural changes in time series models via nonparametric regression,” with B. Chen,Econometrica 80 (2012), 1157-1183.

PDF    CODE

2.“Testing for the Markov property in time series,” with B. Chen, Econometric Theory 28 (2012), 130-178.

PDF    CODE

3.“Granger causality in risk and detection of extreme risk spillover between financial markets,” with Y. Liu and S. Wang,Journal of Econometrics 150 (2009), 271–287.

PDF    CODE

4.“Nonparametric specification testing for continuous-time models with applications to spot interest rates,” with H. Li,Review of Financial Studies 18 (2005), 37-84.

PDF    CODE

5.“Diagnostic checking for the adequacy of nonlinear time series models,” with T.H. Lee, Econometric Theory 19 (2003), 1065-1121.

PDF    CODE

6.“Hypothesis testing in time series via the empirical characteristic function: A generalized spectral density approach,”Journal of the American Statistical Association 94(1999), 1201–1220.

[Generalized spectrum has been included as a basic program in “dCovTS: Distance Covariance/Correlation for Time Series”, written by M. Pitsillou and K. Fokianos (The R Journal, 8.2 (2016), 324-340.]

PDF    CODE (by M. Pitsillou and K. Fokianos)

 

Contact Information

Yongmiao Hong

Room 404 South Building

Academy of Mathematics and Systems Science

Chinese Academy of Sciences

Beijing 100190, China

Email: ymhong@amss.ac.cn