Time series for economics and finance

By:

Linton, Oliver B

Material type: Text

TextPublication details: Cambridge University Press New York 2025Description: xxii, 430 pISBN:

9781009396264

Subject(s):

DDC classification:

330.03 LIN

Summary: 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)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Public Policy & General Management	330.03 LIN (Browse shelf(Opens below))	1	Available		007979

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)

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