Econometrics and Time Series Methods
Chapter guides, ten lecture-slide decks, and 68 reproducible R examples for the forthcoming Springer textbook.
Books
This page lists my books, textbooks, and book chapters. Full book materials are not hosted on this website due to copyright restrictions, but selected public resources and companion websites are linked where available.
Featured resources
Official companion websites
Regression with R and Python
Structured learning materials, exercises, and R and Python code supporting all 14 chapters.
Regression with R and Python: Description, Prediction, and Causal Analysis in Social Science and Medicine
Publishing agreement signed with Springer, 2026
This forthcoming textbook introduces regression analysis through three connected goals: description, prediction, and causal analysis. It combines statistical foundations with practical implementation in both R and Python, and is designed for readers who want to connect regression theory with modern empirical practice in social science and medicine.
Recommended citation: Johansson, P., & Sun, J. (forthcoming 2026). "Regression with R and Python: Description, Prediction, and Causal Analysis in Social Science and Medicine." Springer.
Econometrics and Time Series Methods: Theory, Applications, and R Implementation
Forthcoming with Springer, 2026
“Econometrics and Time Series Methods: Theory, Applications, and R Implementation” covers a wide range of topics including regression models, univariate and multivariate time series, volatility modeling, nonparametric and semiparametric methods, HAR inference, autoregressive filtering and state space models, nonstationary processes, continuous-time finance, and machine learning. The book emphasizes hands-on implementation in R, with extensive examples based on real financial and macroeconomic data, aiming to integrate theory, methods, empirical applications, and computation in a unified way.
Recommended citation: Hong, Y., Linton, O., & Sun, J. (2026 forthcoming). "Econometrics and Time Series Methods: Theory, Applications, and R Implementation." Springer.
金融科技学 (FinTech)
Forthcoming from 高等教育出版社（Higher Education Press）, 2026
This is a Chinese-language textbook on financial technology.
Recommended citation: Sun, J., Hong, Y., Wang, S., & Yang, Y. (forthcoming 2026). 金融科技学 (FinTech). Higher Education Press.
金融计量经济学：理论、案例与 R 语言 (Financial Econometrics: Theory, Cases, and R Language)
Forthcoming from 高等教育出版社（Higher Education Press）, 2026
This is a Chinese-language textbook on financial econometrics.
Recommended citation: Sun, J., Hong, Y., & Linton, O. (forthcoming 2026). 金融计量经济学：理论、案例与 R 语言 (Financial Econometrics: Theory, Cases, and R). Higher Education Press.
Mechanisms and performance of Chinese bear markets and policy suggestions
Published in Science Press, Beijing, China, 2015
This book chapter examines Chinese bear market episodes and their underlying mechanisms, and provides policy-oriented suggestions based on China’s market environment. The discussion connects market behavior with institutional settings and regulatory context, offering a structured reference for understanding downside market dynamics.
Recommended citation: Li, Z., & Sun, J. (2015). Mechanisms and performance of Chinese bear markets and policy suggestions. In Li, Z., & Cheng, S. (Eds.), China's Stock Market Review and Outlook: 2002–2013. Science Press.
Mechanisms and performance of Chinese bear markets and policy suggestions
Published in Palgrave Macmillan, London, UK, 2015
In this chapter, which concerned the 2006–2007 bull market, we analyzed and discussed a number of features related to bull markets in China. Specifically, their formation mechanism, performance characteristics, relevant empirical observations related to index design, as well as their terminal signals. This chapter is structured as follows. In the first section, we will detect the bearish/bullish phases of Chinese stock markets and provide some background information and historical facts. In the second section, we will analyze the ‘risk-return’ trade-off relationships in Chinese stock markets with a focus on the differences of these relationship between bearish and bullish phases. In the third section, we will derive an asset pricing model based on information diffusion; and establish a tri-variate trade-off relationship in terms of amplitude, duration and volatility persistence.1 Finally, we will analyze some institutional problems of Chinese stock markets that have emerged during their adjustment phases.
Recommended citation: Li, Z., Sun, J., & Cole, M. (2015). Mechanisms and performance of Chinese bear markets and policy suggestions. In Li, Z., & Cheng, S. (Eds.), The Chinese Stock Market Volume II (pp. 125–189). Palgrave Macmillan.