Econometrics and Time Series Methods: Theory, Applications, and R Implementation
Published in 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.
The authors are Yongmiao Hong, Oliver Linton, and Jiajing Sun, listed alphabetically by surname.
The textbook is supported by a complete teaching ecosystem, including open-source lecture slides and a dedicated course website (https://quantinar.com/course/1033/econometrics-and-time-series-methods-theory-applications-and-r-implementation), as well as an accompanying GitHub repository of R code (https://github.com/QuantLet/Econometrics_R). These resources make it easy for instructors to adopt the book and for students to engage in self-study, especially in fully English-taught and international programs.
Full book materials are not hosted on this website due to copyright restrictions.
Recommended citation: Hong, Y., Linton, O., & Sun, J. (2026 forthcoming). "Econometrics and Time Series Methods: Theory, Applications, and R Implementation." Springer.