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Applied Economic Forecasting using Time Series Methods
By: Eric Ghysels, Massimiliano Marcellino
Hardcover | 12 April 2018
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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.
Industry Reviews
ISBN: 9780190622015
ISBN-10: 0190622016
Published: 12th April 2018
Format: Hardcover
Language: English
Number of Pages: 616
Audience: Professional and Scholarly
Publisher: Oxford University Press USA
Country of Publication: GB
Dimensions (cm): 19.0 x 26.1 x 5.0
Weight (kg): 1.48
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