Applied Economic Forecasting using Time Series Methods
Oxford University Press Inc (Verlag)
978-0-19-062201-5 (ISBN)
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.
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.
Preface
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
| Erscheinungsdatum | 30.06.2018 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 257 x 178 mm |
| Gewicht | 1429 g |
| Themenwelt | Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie |
| ISBN-10 | 0-19-062201-6 / 0190622016 |
| ISBN-13 | 978-0-19-062201-5 / 9780190622015 |
| Zustand | Neuware |
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