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Applied economic forecasting using time series methods

Author: Eric Ghysels; Massimiliano Marcellino
Publisher: New York, NY : Oxford University Press, [2018] ©2018
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ghysels, Eric, 1956-
Applied economic forecasting using time series methods.
©2018
(DLC) 2017046487
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Eric Ghysels; Massimiliano Marcellino
ISBN: 9780190622022 0190622024 9780190879518 0190879513
OCLC Number: 1028637796
Description: 1 online resource
Contents: Cover; Half title; Applied Economic Forecasting using Time Series Methods; Copyright page; Contents; Preface; Part I Forecasting with the Linear Regression Model; 1 The Baseline Linear Regression Model; 1.1 Introduction; 1.2 The basic specification; 1.3 Parameter estimation; 1.4 Measures of model fit; 1.5 Constructing point forecasts; 1.6 Interval and density forecasts; 1.7 Parameter testing; 1.8 Variable selection; 1.9 Automated variable selection procedures; 1.9.1 Forward selection (FWD); 1.9.2 Least angle regressions (LARS); 1.9.3 LASSO and elastic net estimator (NET). 1.10 Multicollinearity1.11 Example using simulated data; 1.11.1 Data simulation procedure; 1.12 Empirical examples; 1.12.1 Forecasting Euro area GDP growth; 1.12.2 Forecasting US GDP growth; 1.13 A hint of dynamics; 1.13.1 Revisiting GDP forecasting; 1.13.2 Forecasting default risk; 1.14 Concluding remarks; 2 Model Mis-Specification; 2.1 Introduction; 2.2 Heteroskedastic and correlated errors; 2.2.1 The Generalized Least Squares (GLS) estimator and the feasible GLS estimator; 2.3 HAC estimators; 2.4 Some tests for homoskedasticity and no correlation; 2.5 Parameter instability. 2.5.1 The effects of parameter changes2.5.2 Simple tests for parameter changes; 2.5.3 Recursive methods; 2.5.4 Dummy variables; 2.5.5 Multiple breaks; 2.6 Measurement error and real-time data; 2.7 Instrumental variables; 2.8 Examples using simulated data; 2.9 Empirical examples; 2.9.1 Forecasting Euro area GDP growth; 2.9.2 Forecasting US GDP growth; 2.9.3 Default risk; 2.10 Concluding remarks; 3 The Dynamic Linear Regression Model; 3.1 Introduction; 3.2 Types of dynamic linear regression models; 3.3 Estimation and testing; 3.4 Model specification; 3.5 Forecasting with dynamic models. 3.6 Examples with simulated data3.7 Empirical examples; 3.7.1 Forecasting Euro area GDP growth; 3.7.2 Forecasting US GDP growth; 3.7.3 Default risk; 3.8 Concluding remarks; 4 Forecast Evaluation and Combination; 4.1 Introduction; 4.2 Unbiasedness and efficiency; 4.3 Evaluation of fixed event forecasts; 4.4 Tests of predictive accuracy; 4.5 Forecast comparison tests; 4.6 The combination of forecasts; 4.7 Forecast encompassing; 4.8 Evaluation and combination of density forecasts; 4.8.1 Evaluation; 4.8.2 Comparison; 4.8.3 Combination; 4.9 Examples using simulated data; 4.10 Empirical examples. 4.10.1 Forecasting Euro area GDP growth4.10.2 Forecasting US GDP growth; 4.10.3 Default risk; 4.11 Concluding remarks; Part II Forecasting with Time Series Models; 5 Univariate Time Series Models; 5.1 Introduction; 5.2 Representation; 5.2.1 Autoregressive processes; 5.2.2 Moving average processes; 5.2.3 ARMA processes; 5.2.4 Integrated processes; 5.2.5 ARIMA processes; 5.3 Model specification; 5.3.1 AC/PAC based specification; 5.3.2 Testing based specification; 5.3.3 Testing for ARCH; 5.3.4 Specification with information criteria; 5.4 Estimation; 5.5 Unit root tests; 5.6 Diagnostic checking.
Responsibility: Eric Ghysels and Massimiliano Marcellino.

Abstract:

Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series  Read more...

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"This book, by two masters of applied time-series forecasting, is modern, well-balanced, and insightful. And special chapters on things like forecasting in Big Data and/or mixed-frequency data Read more...

 
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