SALE Time Series for Economics and Finance
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
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
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
