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by Eric Ghysels (Author), Massimiliano Marcellino (Author)
About the Author
Eric Ghysels is the Edward M. Bernstein Distinguished Professor of Economics at UNC Chapel Hill, Professor of Finance at the Kenan-Flagler Business School and CEPR Fellow.
Massimiliano Marcellino is Professor of Econometrics at Bocconi University, fellow of CEPR and IGIER.
About this book
Economic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable.
Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics.
This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online at authors' website.
Brief contents
Part I Forecasting with the Linear Regression Model
1 The Baseline Linear Regression Model 3
2 Model Mis-Speci cation 57
3 The Dynamic Linear Regression Model 119
4 Forecast Evaluation and Combination 145
Part II Forecasting with Time Series Models
5 Univariate Time Series Models 173
6 VAR Models 253
7 Error Correction Models 301
8 Bayesian VAR Models 345
Part III TAR, Markov Switching, and State Space Models
9 TAR and STAR Models 369
10 Markov Switching Models 399
11 State Space Models 419
Part IV Mixed Frequency, Large Datasets, and Volatility
12 Models for Mixed-Frequency Data 453
13 Models for Large Datasets 503
14 Forecasting Volatility 531
Bibliography 559
Subject Index 587
Author Index 592
Pages: 616 pages
Publisher: Oxford University Press (April 20, 2018)
Language: English
ISBN-10: 0190622016
ISBN-13: 978-0190622015
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