Time Series for Economics and Finance (Paperback)
暫譯: 經濟與金融的時間序列分析 (平裝本)

Oliver Linton

Time Series for Economics and Finance (Paperback)
  • 出版商: Cambridge
  • 出版日期: 2024-12-19
  • 售價: $1,680
  • 貴賓價: 9.8 折 $1,646
  • 語言: 英文
  • ISBN: 1009396269
  • ISBN-13: 9781009396264
  • 相關分類: 經濟學 Economy

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商品描述

Description

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.

  • Connects theory and practice by applying concepts to particular and real-world settings
  • Focuses on contemporary problems such as climate science and COVID-19
  • Includes numerous and wide-ranging theoretical and empirical exercises to allow for choice of level or focus
  • Covers a broad range of material from different areas using a common language and notation to aid student comprehension

商品描述(中文翻譯)

描述

本教科書專注於時間序列數據的分析方法，提供了一個全面的指南，使用數據科學的現代技術來分析時間序列數據。它特別針對經濟學和金融應用，旨在為學生提供嚴謹的訓練。各章節涵蓋貝葉斯方法、非參數平滑方法、機器學習和連續時間計量經濟學。全書包含理論和實證練習、簡明摘要、加粗的關鍵術語以及示例，以加強關鍵概念並增進理解。附加材料包括帶有解答和額外練習的教師手冊、PowerPoint 講義幻燈片和數據集。以其清晰易懂的風格，本教科書是經濟學、金融學和統計學高年級本科生及研究生的重要工具。


  • 通過將概念應用於特定的現實情境，連結理論與實踐

  • 專注於當前問題，如氣候科學和 COVID-19

  • 包含大量多樣的理論和實證練習，以便選擇不同的難度或重點

  • 使用共同的語言和符號涵蓋來自不同領域的廣泛材料，以幫助學生理解

目錄大綱

  • 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.

目錄大綱(中文翻譯)


  • 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.

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