Bokus
Oliver Linton - Time Series for Economics and Finance, Häftad

Häftad, Engelska, 2024

Time Series for Economics and Finance

Av Oliver Linton

770 kr

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Beskrivning
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.
Produktinformation
  • Utgivningsdatum: 2024-12-19
  • Mått: 178 x 254 x 23 mm
  • Vikt: 940 g
  • Format: Häftad
  • Språk: Engelska
  • Antal sidor: 450
  • Förlag: Cambridge University Press
  • ISBN: 9781009396264
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Mer om författaren

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.

Recensioner i media

'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

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