Applied Economic Forecasting using Time Series Methods

Eric Ghysels

ISBN 10: 0190622016 / ISBN 13: 9780190622015

Edité par Oxford University Press Inc Feb 2020, 2020

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780190622015

Détails bibliographiques

• Titre: Applied Economic Forecasting using Time Series Methods
• Éditeur: Oxford University Press Inc Feb 2020
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 0190622016
• ISBN à 13 chiffres: 9780190622015
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 261 L x 182 H x 48 P (en millimètres) 261 millimètres de largeur par 182 millimètres de hauteur par 48 millimètres de profondeur
• Poids: 1448 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.