Applied economic forecasting using time series methods / Eric Ghysels, and Massimiliano Marcellino.

By: Ghysels, Eric
Contributor(s): Marcellino, Massimiliano
Material type: TextTextLanguage: English Publisher: New York : Oxford University, 2018Description: xviii, 597 p. : illus. ; 27 cmISBN: 9780190622015 (hardcover : alk. paper)Subject(s): Economic forecasting -- Mathematical models | Economic forecasting -- Statistical methodsDDC classification: 330.0151955 LOC classification: HB3730 | .G459 2018Online resources: WorldCat details
Contents:
Table of contents PART I: Forecasting with the Linear Regression Model. Chapter 1 -The Baseline Linear Regression Model. Chapter 2 -- Model Mis-Specification. Chapter 3 -- The Dynamic Linear Regression Model. Chapter 4 -- Forecast Evaluation and Combination. PART II: Forecasting with Time Series Models. Chapter 5 -- Univariate Time Series Models. Chapter 6 -- VAR Models. Chapter 7 -- Error Correction Models. Chapter 8 -- Bayesian VAR Models. PART III: TAR, Markov Switching and State Space Models. Chapter 9 -- TAR and STAR Models. Chapter 10 -- Markov Switching Models. Chapter 11 -- State Space Models and the Kalman Filter. PART IV: Mixed Frequency, Large Datasets and Volatility. Chapter 12 -- Models for Mixed Frequency Data. Chapter 13 -- Models for Large Datasets. Chapter 14 -- Forecasting Volatility.
Summary: 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 Read more...
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Non-fiction 330.0151955 GHA 2018 (Browse shelf) C-1 Not For Loan 29526
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Text Text EWU Library
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Includes bibliographical references (pages 559-586) and index.

Table of contents PART I: Forecasting with the Linear Regression Model. Chapter 1 -The Baseline Linear Regression Model. Chapter 2 --
Model Mis-Specification. Chapter 3 --
The Dynamic Linear Regression Model. Chapter 4 --
Forecast Evaluation and Combination. PART II: Forecasting with Time Series Models. Chapter 5 --
Univariate Time Series Models. Chapter 6 --
VAR Models. Chapter 7 --
Error Correction Models. Chapter 8 --
Bayesian VAR Models. PART III: TAR, Markov Switching and State Space Models. Chapter 9 --
TAR and STAR Models. Chapter 10 --
Markov Switching Models. Chapter 11 --
State Space Models and the Kalman Filter. PART IV: Mixed Frequency, Large Datasets and Volatility. Chapter 12 --
Models for Mixed Frequency Data. Chapter 13 --
Models for Large Datasets. Chapter 14 --
Forecasting Volatility.

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 Read more...

Economics

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