Time Series Econometrics

1. Front Matter

   Pages i-xxiv

   PDF

2. Univariate Time Series Analysis

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction and Basic Theoretical Concepts

      • Klaus Neusser

      Pages 3-24

   3. Autoregressive Moving-Average Models

      • Klaus Neusser

      Pages 25-44

   4. Forecasting Stationary Processes

      • Klaus Neusser

      Pages 45-66

   5. Estimation of the Mean and the Autocorrelation Function

      • Klaus Neusser

      Pages 67-85

   6. Estimation of ARMA Models

      • Klaus Neusser

      Pages 87-108

   7. Spectral Analysis and Linear Filters

      • Klaus Neusser

      Pages 109-132

   8. Integrated Processes

      • Klaus Neusser

      Pages 133-165

   9. Models of Volatility

      • Klaus Neusser

      Pages 167-193

3. Multivariate Time Series Analysis

   1. Front Matter

      Pages 195-195

      PDF

   2. Introduction

      • Klaus Neusser

      Pages 197-199

   3. Definitions and Stationarity

      • Klaus Neusser

      Pages 201-206

   4. Estimation of Mean and Covariance Function

      • Klaus Neusser

      Pages 207-214

   5. Stationary Time Series Models: Vector Autoregressive Moving-Average Processes (VARMA Processes)

      • Klaus Neusser

      Pages 215-224

   6. Estimation of Vector Autoregressive Models

      • Klaus Neusser

      Pages 225-239

   7. Forecasting with VAR Models

      • Klaus Neusser

      Pages 241-253

   8. Interpretation and Identification of VAR Models

      • Klaus Neusser

      Pages 255-294

   9. Cointegration

      • Klaus Neusser

      Pages 295-324

   10. State-Space Models and the Kalman Filter

       • Klaus Neusser

       Pages 325-352

This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text  devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussionof co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field.  Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. 

• time series analysis
• ARMA
• GARCH
• VAR
• SVAR
• co-integration
• Beveridge-Nelson
• Kalman Filter

“Neusser offers an important addition to the market for books on time series econometrics, and definitely fills a gap within the market and complements existing offerings. This is an excellent effort, and I have enjoyed the book.” (Benjamin Wong, Economic Record, Vol. 95 (310), September, 2019)



“The present monograph is a practical and comprehensive introduction to an area that lies at the core of econometrics. … It requires minimal prerequisites, and is almost surely accessible to senior undergraduate or beginning graduate students, and certainly to independent researchers … . I find this book to be a valuable addition to the monographic literature on time series.” (Giuseppe Castellacci, Mathematical Reviews, October, 2017)

• Bern, Switzerland

  Klaus Neusser

Prof. Klaus Neusser

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