Time Series for Economics and Finance

Auteur
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.

Texte du rabat

Written for advanced undergraduate and graduate students in economics and finance, this textbook provides comprehensive training in time series analysis using modern techniques from data science. Core material is enhanced with topical examples and challenging exercises to reinforce key concepts.

Résumé
Focusing on methods for data that are ordered in time, this textbook provides a comprehensive guide to analyzing time series data using modern techniques from data science. It is specifically tailored to economics and finance applications, aiming to provide students with rigorous training. Chapters cover Bayesian approaches, nonparametric smoothing methods, machine learning, and continuous time econometrics. Theoretical and empirical exercises, concise summaries, bolded key terms, and illustrative examples are included throughout to reinforce key concepts and bolster understanding. Ancillary materials include an instructor's manual with solutions and additional exercises, PowerPoint lecture slides, and datasets. With its clear and accessible style, this textbook is an essential tool for advanced undergraduate and graduate students in economics, finance, and statistics.

Contenu
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.