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Applied Economic Forecasting using Time Series Methods

Name: Applied Economic Forecasting using Time Series Methods
SKU: EB-9980310
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Applied economic forecasting using time series methods Ghysels Digital Instant Download

Author(s): Ghysels, Eric; Marcellino, Massimiliano

ISBN(s): 9780190622022, 9780190879518, 0190622024, 0190879513

File Details: PDF, 10.67 MB

Year: 2018

Language: English

SKU: EB-9980310 Category: Business & Economics - Econometrics Tags: Economic Forecasting, Eric Ghysels, Massimiliano Marcellino, Time Series Methods

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Applied Economic Forecasting using Time Series Methods – Ebook Instant Download/Delivery ISBN(s): 9780190622022,0190622024,9780190622039, 0190622032

Product details:

ISBN-10 ‏ : ‎ 0190622032
ISBN-13 ‏ : ‎9780190622039
Author: Eric Ghysels, Massimiliano Marcellino

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 at authors’ website.

Table 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