Time Series for Economics and Finance

Oliver Linton is Chair of the Faculty of Economics, a Fellow of Trinity College, and Professor of Political Economy at the University of Cambridge. He has published two books and nearly 200 hundred articles on econometrics, statistics, and empirical finance. He was President of the Society for Financial Econometrics from 2021 to 2023 and is a Fellow of the Econometric Society, the Institute of Mathematical Statistics, and the British Academy.

'This is an excellent textbook for advanced undergraduate and graduate students in economics, finance, and statistics. It contains an up-to-date account of recent developments in time series econometrics, including an insightful presentation of machine learning, smoothing, and other nonparametric approaches, and methods for continuous-time models. The book is largely self-contained, comprehensive, and manages to present complex topics clearly and succinctly. It will be very useful both to readers who have not been previously exposed to time series and to those who wish to learn how the more advanced methods work and how they compare and interface with the classical methods.' Yacine Aït-Sahalia, Princeton University

• Preface; 1 Introduction; 2. Stationarity and mixing; 3. Linear time series models; 4. Spectral analysis; 5. Inference under heterogeneity and weak dependence; 6. Nonstationary processed, trends and seasonality; 7. Multivariate linear time series; 8. Stae space models and Kalman filter; 9. Bayesian methods; 10. Nonlinear time series models; 11. Nonparametric methods and machine learning; 12. Continuous time processes; Bibliography; Index.