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Applied economic forecasting using time series methods /

by Ghysels, Eric; Marcellino, Massimiliano.
Material type: materialTypeLabelBookPublisher: New York : Oxford University, 2018Description: xviii, 597 p. : illus. ; 27 cm.ISBN: 9780190622015 (hardcover : alk. paper); .Subject(s): Economic forecasting -- Mathematical models | Economic forecasting -- Statistical methodsOnline 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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Item type Location Collection Call number Copy number Status Date due
Text Text Reserve Section Non Fiction 330.0151955 GHA 2018 (Browse shelf) C-1 Not For Loan
Text Text Circulation Section Non Fiction 330.0151955 GHA 2018 (Browse shelf) C-2 Available
Text Text Circulation Section Non Fiction 330.0151955 GHA 2018 (Browse shelf) C-3 Checked out 18/04/2019

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 Bank Management

Saifun Momota

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