Time series for economics and finance
Linton, Oliver B
Time series for economics and finance - New York Cambridge University Press 2025 - xxii, 430 p.
Table of contents:
Frontmatter
1 - Introduction
pp 1-13
2 - Stationarity and Mixing
pp 14-41
3 - Linear Time Series Models
pp 42-101
4 - Spectral Analysis
pp 102-128
5 - Inference under Heterogeneity and Weak Dependence
pp 129-150
6 - Nonstationary Processes, Trends, and Seasonality
pp 151-195
7 - Multivariate Linear Time Series
pp 196-237
8 - State Space Models and the Kalman Filter
pp 238-251
9 - Bayesian Methods
pp 252-265
10 - Nonlinear Time Series Models
pp 266-302
11 - Nonparametric Methods and Machine Learning
pp 303-346
12 - Continuous-Time Processes
pp 347-373
13 - Forecasting
pp 374-396
Appendices
pp 397-410
Appendix A - Fourier Analysis
pp 397-397
Appendix B - Matrices and Multivariate Normal
pp 398-400
Appendix C - Laws of Large Numbers and Central Limit Theorems
pp 401-404
Appendix D - Data and Data Sources
pp 405-408
Appendix E - A Short Introduction to EViews
pp 409-410
Bibliography
pp 411-427
Index
(https://www.cambridge.org/highereducation/books/time-series-for-economics-and-finance/149D2AF6A765ACE8DB51D5AEDD0C4AAD#contents)
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.
(https://www.cambridge.org/highereducation/books/time-series-for-economics-and-finance/149D2AF6A765ACE8DB51D5AEDD0C4AAD#overview)
9781009396264
Economics
Finance
330.03 / LIN
Time series for economics and finance - New York Cambridge University Press 2025 - xxii, 430 p.
Table of contents:
Frontmatter
1 - Introduction
pp 1-13
2 - Stationarity and Mixing
pp 14-41
3 - Linear Time Series Models
pp 42-101
4 - Spectral Analysis
pp 102-128
5 - Inference under Heterogeneity and Weak Dependence
pp 129-150
6 - Nonstationary Processes, Trends, and Seasonality
pp 151-195
7 - Multivariate Linear Time Series
pp 196-237
8 - State Space Models and the Kalman Filter
pp 238-251
9 - Bayesian Methods
pp 252-265
10 - Nonlinear Time Series Models
pp 266-302
11 - Nonparametric Methods and Machine Learning
pp 303-346
12 - Continuous-Time Processes
pp 347-373
13 - Forecasting
pp 374-396
Appendices
pp 397-410
Appendix A - Fourier Analysis
pp 397-397
Appendix B - Matrices and Multivariate Normal
pp 398-400
Appendix C - Laws of Large Numbers and Central Limit Theorems
pp 401-404
Appendix D - Data and Data Sources
pp 405-408
Appendix E - A Short Introduction to EViews
pp 409-410
Bibliography
pp 411-427
Index
(https://www.cambridge.org/highereducation/books/time-series-for-economics-and-finance/149D2AF6A765ACE8DB51D5AEDD0C4AAD#contents)
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.
(https://www.cambridge.org/highereducation/books/time-series-for-economics-and-finance/149D2AF6A765ACE8DB51D5AEDD0C4AAD#overview)
9781009396264
Economics
Finance
330.03 / LIN