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"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"--
List of contents
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.
About the author
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.
Summary
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.
