Professor Yongmiao Hong received his B.S. in Physics (1985) and M.A. in Economics (1988) from Xiamen University, and his Ph.D. in Economics (1993) from the University of California, San Diego. He joined Cornell University in 1993 as a tenure-track Assistant Professor in the Department of Economics and Department of Statistics and Data Science, was promoted to tenured Associate Professor in 1998, and to Full Professor in 2001. From 2003 to 2020, he was a field member of the Center for Applied Mathematics. He later served as the Ernest S. Liu Professor of Economics and International Studies (2010–2020). In December 2020, he moved from Cornell to UCAS.

Professor Hong is a Fellow of the World Academy of Sciences (TWAS) for the advancement of science in developing countries, the Econometric Society, the International Association of Applied Econometrics (IAAE), and the Asia-Pacific Artificial Intelligence Association (AAIA), as well as a Senior Fellow of the Rimini Center for Economic Analysis (RCEA) and the Asian Bureau of Finance and Economic Research (ABFER). He served as the President of the Chinese Economists Society in North America (2009–2010).

Professor Hong's research fields are econometrics, financial econometrics, time series analysis, and statistics. He publishes refereed articles in mainstream economics, finance and statistics 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), Management Science, Quarterly Journal of Economics, Review of Economic Studies, Review of Economics and Statistics, and Review of Financial Studies.

CURRICULUM VITAE

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

On model specification testing and evaluation, Hong develops a class of sophisticated semiparametric tests for econometric models of cross-sectional, time series, and panel data. The basic idea is to compare a null econometric model with a flexible nonparametric alternative, developing tests with 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 is able to detect various functional form misspecifications. This methodology is extended in Hong (1996, Econometrica) to test serial correlation of unknown form in a dynamic linear regression model. This is achieved by comparing a nonparametric kernel estimator for the spectral density of the estimated model residuals 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 detect serial correlation of unknown form in a panel data regression model, where wavelets are used in nonparametric spectral density estimation. Hong and Lee (2013, Annals of Statistics) propose a loss function-based specification test which is asymptotically more efficient than a generalized likelihood ratio test approach including those based on comparing sums of squared residuals.

One main area of Hong's research is nonlinear time series and generalized spectral analysis. Observing that many economic and financial time series are serially uncorrelated but not serially independent, Hong (1998, 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 provides a solid theoretical foundation for the "shadow correlation coefficient" proposed by Nobel Laureate Sir Clive W. J. Granger as a statistical tool to identify significant lags in nonlinear time series analysis.

On an important development, Hong (1999, Journal of American Statistical Association; 2000, Journal of Royal Statistical Society, Series B) proposes a new analytic tool for nonlinear time series—the generalized spectrum. The basic idea is to transform the original time series data via a complex-valued exponential function or an indicator 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 require restrictive moment conditions. Real data applications (e.g., Hong and Lee, 2003, 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 applied to the development of powerful procedures for nonlinear time series analysis. For example, Hong and Lee (2003, Econometric Theory) use it to check for any neglected dependence structure in the estimated standardized residuals of a nonlinear time series model, and Hong and Lee (2005, Review of Economic Studies; 2007, Econometric Theory) 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 (2003, Econometric Theory) won the Koopman Econometric Theory Prize 2006. Following Hong's (1999, 2000) generalized spectrum, a new field on Fourier analysis of nonlinear time series has emerged, using various terms such as Laplace-based, Copula-based, Quantile-based and Rank-based spectral densities or spectral density kernels. Hong's (1999, 2000) work has been viewed as "a first step" or "pioneering contribution" in this direction (e.g., Kley et al., 2016, Bernoulli; Birr et al., 2017, Journal of Royal Statistical Society, Series B; Birr et al, 2018, Journal of Time Series Analysis; Birr et al., 2019, Journal of Multivariate Analysis; Goto et al., 2022, Annals of Statistics).

Hong also works on financial econometrics. Hong (2001, Journal of Econometrics) proposes a test for volatility spillover. 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 when the continuous-time model is correctly specified. Hong, Tu and Zhou (2007, Review of Financial Studies) develop correlation-based tests for asymmetric dependence in asset returns and assess their economic implications. Hong, Liu and Wang (2009, Journal of Econometrics) introduce the concept of Granger causality in risk and propose a test for it. The aforementioned tests have been widely used in empirical studies, especially in economics and finance.

A recent research area of Hong is structural changes and time-varying econometrics. Building upon the assumption that most economic time series, even after transformation (e.g., detrending or first-differencing), may remain nonstationary, Hong has devoted himself to developing econometric theory for inference on smooth structural changes or smooth structural changes with a finite number of abrupt structural breaks. Chen and Hong (2012, Econometrica) propose a smoothed nonparametric test for parameter consistency in a linear time series regression model. A similar approach is used to test parameter consistency of a GARCH model (Chen and Hong, 2016, Econometric Theory) and a moment condition model (Li, Zhou and Hong, 2024, Journal of Econometrics), as well as strict stationarity (Hong, Wang and Wang, 2017, International Economic Review). Fu and Hong (2019, Journal of Econometrics) and Fu, Hong and Wang (2023, Journal of Econometrics) propose a Discrete Fourier Transform (DFT) approach to test structural changes in a nonparametric regression model with endogeneity and a high-dimensional factor model, avoiding smoothed nonparametric estimation.

Model averaging or forecast combination has been an effective way to tackle model uncertainty and achieve robust forecasting. When structural changes exist, a model may forecast better than others in some periods and worse in others. As a result, a large weight should be assigned to a model when it forecasts well, and a small weight should be assigned when it forecasts poorly. Hong and his co-authors (e.g., Sun et al., 2021, 2023, Journal of Econometrics; Hong et al., 2024, Journal of Econometrics) develop novel optimal time-varying model averaging methods. In a time-varying GMM framework where model parameters are allowed to change smoothly except for finitely many abrupt structural breaks, Cui, Feng and Hong (2024, International Economic Review) use a fuse-ridge regularization approach to estimate all time-varying model parameters simultaneously by utilizing a large cross-section of moment conditions.

Hong has also worked on modeling interval-valued time series data. An interval-valued observation contains more information than a point-valued observation. Examples of interval data include the maximum and minimum temperatures in climate change, the hypertension and hypotension in medical science, 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 rates, and the 90th- and 10th- percentile incomes of a cohort. Interval forecasts may be of direct interest in practice, as they contain 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 et al., 2018, Journal of Econometrics) propose a class of (threshold) 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 or threshold ACI model. Both simulation and empirical studies show that the use of interval time series data can provide more accurate estimation for model parameters and more accurate out-of-sample forecasts.

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(in English)

1. "Regularized interval-valued time series modeling," with H. Bao, Y. Sun, and S. Wang, Journal of Business & Economic Statistics (2026), forthcoming, doi: 10.1080/07350015.2026.2675481.
2. "Time-varying model averaging for FAVAR models with smooth structural changes," with Q. Chen, H. Li, and X. Wang, Journal of Business & Economic Statistics (2026), forthcoming, doi: 10.1080/07350015.2026.2674157.
3. "Do asset prices help predict inflation? Evidence from individual sock prices," with Y. Cui, and N. Huang, Journal of Business & Economic Statistics (2026), forthcoming, doi:10.1080/07350015.2026.2654899.
4. "Forecasting GDP growth rates using accounting earnings: A large panel microdata approach," with Y. Cui, N. Huang, and Y. Wang, Management Science (2026): 1-17, https://doi.org/10.1287/mnsc.2025.01549.
5. "Inference for time-varying factor models under local stationarity," with W. Wu, and Z. Zhou, Journal of Econometrics 253 (2026): 106154.
6. "Text-based modeling reveals the sector-specific benefits of emerging technologies for extreme flood adaptation, " with Y. Zhong, W. Shang, S. Cui, X. Bai, Q. Lu, F. Meng, L. Wang, F. Jiang, S. Sun, J. Wang, H. Huang, and S. Wang, Communications Earth & Environment (2025), https://doi.org/10.1038/s43247-025-03077-4.
7. "Estimating and testing multiple structural breaks in nonparametric regressions," with Y. Cao, Z. Fu, X. Wang, and X. Zhang, Journal of Time Series Analysis (2025), https://doi.org/10.1111/jtsa.70037.
8. "Modeling and forecasting interval-valued data in finance: a half-century review," with D. Zhang, Y. Sun, and S. Wang, China Finance Review International (2025), 1-25, https://doi.org/10.1108/CFRI-10-2024-0622.
9. "Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure," with S. Dai, H. Li, and C. Zheng, Journal of Econometrics 251 (2025): 106082.
10. "Structural stability of functional data-a new adjusted-range-based self-normalization approach," with J. Sun, Z. Lin, and W. Xu, Economics Letters 253 (2025): 112350.
11. "Time-varying complete subset averaging in a data-rich environment," with H. Li, J. Zhang, and X. Chen, Econometric Theory (2025): 1-56. doi:10.1017/S0266466625000064
12. "A novel hybrid nonlinear forecasting model for interval-valued gas prices," with H. Bao, Y. Sun, and S. Wang, Journal of Forecasting, 44.5 (2025): 1826-1848.
13. "Estimating and testing for smooth structural changes in moment condition models, " with H. Li, and J. Zhou, Journal of Econometrics 246.1-2 (2024), 105896.
14. "Forecasting inflation using economic narratives," with F Jiang, L Meng, and B Xue, Journal of Business and Economic Statistics (2024), https://doi.org/10.1080/07350015.2024.2347619.
15. "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach,"with K. Yang, Y. Sun, and S. Wang, Energy Economics (2024), 107952.
16. "The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis," with Z. Cheng, M. Li, R. Cui, Y. Wei, and S. Wang, International Review of Financial Analysis 95 (2024), 103454.
17. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," with Z. Cheng, M. Li, Y. Sun, and S. Wang, Energy Economics 134 (2024), 107612.
18. "Does environmental regulation affect analyst forecast bias? Evidence from China's low-carbon pilot policy," with Y. Sun, K. Wu, and S. Liu, Journal of Environmental Management 353 (2024), 120134.
19. "Post-averaging inference for optimal model averaging estimator in generalized linear models," with D. Yu, H. Lian, Y. Sun, and X. Zhang, Econometric Reviews 43.2-4 (2024), 98-122.
20. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," with Q. Chen, and H. Li, Journal of Econometrics 240.1 (2024), 105693.
21. "Kolmogorov-Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach," with B. McCabe, J. Sun, and S. Wang, Journal of Econometrics 238.2 (2024), 105603.
22. "Regularized GMM for time-varying models with applications to asset pricing," with L. Cui, and G. Feng, International Economic Review 65.2 (2024), 851-883.
23. "The discussion meting on probabilistic and statistical aspects of machine learning,"w ith O. Linton, J. Sun, and M. Zhu, Journal of the Royal Statistical Society Series B: Statistical Methodology 86.2 (2024), 320-321.
24. "A regularized high-dimensional positive definite covariance estimator with high-frequency data," with L. Cui, Y. Li, and J. Wang, Management Science 70.10 (2024), 7242-7264.
25. "The climate impact of high seas shipping," with Y. Li, P. Jia, S. Jiang, H. Li, H. Kuang, S. Wang, X. Zhao, and D. Guan, National Science Review 10.3 (2023), nwac279.
26. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," with D. Zhang, Y. Sun, H. Duan and S. Wang, International Review of Financial Analysis 88 (2023): 102700.
27. "Fast estimation of a large TVP-VAR model with score-driven volatilities," with T. Zheng, and S. Ye, Journal of Economic Dynamics and Control 157 (2023), 104762.
28. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," with Z. Fu, and X. Wang, Journal of Econometrics 1 (2023), 302-331.
29. "On multiple structural breaks in distribution: An empirical characteristic function approach,” with Z. Fu, and X. Wang, Econometric Theory 39.3 (2023), 534-581.
30. "Penalized time-varying model averaging," with Y. Sun, S. Wang, and X. Zhang, Journal of Econometrics 235.2 (2023), 1355-1377.
31. "Specification tests for time-varying coefficient models," with Z. Fu, L. Su, and X. Wang, Journal of Econometrics 235.2 (2023), 720-744.
32. "Adjusted-range self-normalized confidence interval construction for censored dependent data," with J. Sun, O. Linton, and X. Zhao, Economics Letters 220 (2022), 110873.
33. "Forecasting interval-valued crude oil prices using asymmetric interval models," with Q. Lu, Y. Sun, and S. Wang, Quantitative Finance 22.11 (2022), 2047-2061.
34. "A score statistic for testing the presence of a stochastic trend in conditional variances," with O. Linton, B. McCabe, and J. Sun, Economics Letters, 213 (2022), 110394.
35. "Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information,” with J. Heng, J. Hu, and S. Wang, Applied Energy, 306 (2022), 118029.
36. "Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models," with Y. He, A. Han, Y. Sun and S. Wang, Econometric Reviews, 40.6 (2021), 584-606.
37. "Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China," with S. Jiang, Y. Li, Q. Lu, D. Guan, Y. Xiong and S. Wang, Nature Communications 12, 1938 (2021). 
38. "Solving Euler equations via two-stage nonparametric penalized splines," with L. Cui and Y. Li, Journal of Econometrics 222.2 (2021), 1024–1056.
39. "Time-varying model averaging," with Y. Sun, T. Lee, S. Wang and X. Zhang, Journal of Econometrics 222.2, (2021), 974–992.
40. "Estimating functions and derivatives via adaptive penalized splines," with L. Yang, M. Ding, and X. Wang, Communications in Statistics-Simulation and Computation 50.7 (2021): 2054-2071.
41. "A model-free consistent test for structural change in regression possibly with endogeneity," with Z. Fu, Journal of Econometrics 211 (2019), 206-242.
42. "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.
43. "Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares," with Y. Sun and S. Wang, Journal of Management Science and Engineering, 4.1 (2019), 1-11.
44. "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.
45. "Advance in theoretical econometrics—Essays in honor of Takeshi Amemiya," with Z. Cai and C. Hsiao, Journal of Econometrics 206 (2018), 279-281.
46. "Econometric modeling and economic forecasting," with Z. Cai and S. Wang, Journal of Management Science and Engineering 3 (2018), 179-182.
47. "Threshold autoregressive models for interval-valued time series data," with Y. Sun, A. Han, and S. Wang, Journal of Econometrics 206 (2018), 414-446.
48. "Characteristic function based testing for conditional independence: A nonparametric regression approach," with X. Wang, Econometric Theory 34 (2018), 815-849.
49. "Testing strict stationarity with applications to macroeconomic time series," with X. Wang and S. Wang, International Economic Review 58 (2017), 1227-1277.
50. "A general approach to testing volatility models in time series," with Y. J. Lee, Journal of Management Science and Engineering 2 (2017), 1-33.
51. "An efficient integrated nonparametric entropy estimator of serial dependence," with X. Wang, W. Zhang and S. Wang, Econometric Reviews 36 (2017), 728–780.
52. "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.
53. "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.
54. "Analysis of crisis impact on crude oil prices: A new approach with interval time series modelling," with W. Yang, A. Han and S. Wang, Quantitative Finance 16 (2016), 1917-1928.
55. "Detecting for smooth structural changes in GARCH models," with B. Chen, Econometric Theory 32 (2016), 740-791.
56. "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.
57. "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.
58. "A unified approach to validating univariate and multivariate conditional distribution models in time series," with B. Chen, Journal of Econometrics 178 (2014), 22-44.
59. "A loss function approach to model specification testing and its relative efficiency," with Y. Lee, Annals of Statistics 41 (2013), 1166-1203.
60. "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.
61. "Productivity spillovers among linked sectors," with L. Peng, China Economic Review 25 (2013), 44-61.
62. "Testing for smooth structural changes in time series models via nonparametric regression," with B. Chen, Econometrica 80 (2012), 1157-1183.
63. "Testing for the Markov property in time series," with B. Chen, Econometric Theory 28 (2012), 130-178.
64. "Are corporate bond market returns predictable?" with H. Lin and C. Wu, Journal of Banking and Finance 36 (2012), 2216-2232.
65. "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.
66. "Generalized spectral testing for multivariate continuous-time models," with B. Chen, Journal of Econometrics 164 (2011), 268-293.
67. "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.
68. "Characteristic function-based testing for multifactor continuous-time Markov models via nonparametric regression," with B. Chen, Econometric Theory 26 (2010), 1115-1179.
69. "Modeling the dynamics of Chinese spot interest rates," with H. Lin and S. Wang, Journal of Banking and Finance 34 (2010), 1047-1061.
70. "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.
71. "Central limit theorems for generalized U-statistics with applications in nonparametric specification," with J. Gao, Journal of Nonparametric Statistics 20 (2008), 61-76.
72. "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.
73. "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.
74. "Serial correlation and serial dependence," in The New Palgrave Dictionary in Economics, 2008, 2nd Edition, (eds.) S. N. Durlauf and L. E. Blume, New York: Palgrave Macmillan, 2005, pp.5857–5867.
75. "Model-free evaluation of directional predictability in foreign exchange markets," with J. Chung, Journal of Applied Econometrics 22 (2007), 855-889.
76. "Asymmetries in stock returns: Statistical tests and economic evaluation," with J. Tu and G. Zhou, Review of Financial Studies 20 (2007), 1547-1581.
77. "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.
78. "An improved generalized spectral test for conditional mean models in time series models with conditional heteroskedasticity of unknown form," with Y. Lee, Econometric Theory 23 (2007), 106-154.
79. "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.
80. "Asymptotic distribution theory for nonparametric entropy measures of serial dependence," with H. White, Econometrica 73 (2005), 837-901.
81. "Generalized spectral tests for conditional mean models in time series with conditional heteroskedasticity of unknown form," with Y. Lee, Review of Economic Studies 72 (2005), 499-541.
82. "Nonparametric specification testing for continuous-time models with applications to spot interest rates," with H. Li, Review of Financial Studies 18 (2005), 37-84.
83. "Wavelet-Based testing for serial correlation of unknown form in panel models," with C. Kao, Econometrica 72 (2004), 1519-1563.
84. "Out-of-sample performance of discrete-time spot interest rate models," with H. Li and F. Zhao, Journal of Business and Economic Statistics 22 (2004), 457-473.
85. "Inference on predictability of foreign exchange rates via generalized spectrum and nonlinear time series models," with T. H. Lee, Review of Economics and Statistics, 85 (2003), 1048-1062.
86. "Diagnostic checking for the adequacy of nonlinear time series models," with T.H. Lee, Econometric Theory 19 (2003), 1065-1121.
87. "Nonparametric methods in 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.
88. "Testing for independence between two stationary time series via the empirical characteristic function," Annals of Economics and Finance 2 (2001), 123-164.
89. "One-sided testing for ARCH effects using wavelets," with J. Lee, Econometric Theory 17 (2001), 1051-1081.
90. "A test for volatility spillover with application to exchange rates," Journal of Econometrics 103 (2001), 183-224.
91. "Testing for serial correlation of unknown form using wavelet methods," with J. Lee, Econometric Theory 17 (2001), 386-423.
92. "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.
93. "Generalized spectral tests for serial dependence," Journal of the Royal Statistical Society, Series B (Statistical Methodology) 62 (2000), 557-574.
94. "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.
95. "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.
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97. "Testing for pairwise serial independence via the empirical distribution function," Journal of the Royal Statistical Society Series B (Statistical Methodology) 60 (1998), 429-453.
98. "One-sided testing for conditional heteroskedasticity in time series models," Journal of Time Series Analysis 18 (1997), 253-277.
99. "Testing for independence between two covariance stationary time series," Biometrika 83 (1996), 615-625.
100. "Consistent testing for serial correlation of unknown form," Econometrica 64 (1996), 837-864.
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102. "China's evolving managerial labor market," with T. Groves, J. McMillan and B. Naughton, Journal of Political Economy  103 (1995), 873-892.
103. "Productivity growth in Chinese state-run industry," with T. Groves, J. McMillan and B. Naughton, in China's State-owned Enterprise Reforms, (eds.) F. Dong, C. Lin, B. Naughton, London: Macmillan, 1995.
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​

(in English)

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.

"This book is a nice textbook on modern econometrics. It is essentially based on the author's lecture notes taught at Cornell University and several universities in China … The text provides a clear, understandable introduction to key concepts of econometrics."

----zbMATH

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

 Available at World Scientific and Amazon.

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

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

 Available at World Scientific and Amazon.

3. Information Spillover Effect and Autoregressive Conditional Duration Models, with Xiangli Liu, Yanhui Liu, and Shouyang 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

 Available at Routledge and Amazon.

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(in English)

1. Probability and Statistics for Economists

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.

Course Homepage

2. Advanced Econometrics

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.

Course Homepage

3. An Introduction to Nonparametric Analysis in Time Series Econometrics

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 NotesLecture_Notes_on_Nonparametric_Analysis

Course Homepage

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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. "Testing the structure of conditional correlations in multivariate GARCH models," with N. McCloud, International Economic Review 52.4 (2011), 991-1037.

PDF    CODE

4. "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

5. "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

6. "Wavelet-based testing for serial correlation of unknown form in panel models," with C. Kao, Econometrica 72 (2004), 1519-1563.

PDF    CODF

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

PDF    CODE

8. "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)

Yongmiao Hong

Room 404 South Building

Academy of Mathematics and Systems Science

Chinese Academy of Sciences

Haidian District

Beijing 100190, China

Emails: yh20@cornell.edu; ymhong@amss.ac.cn

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