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.
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.
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.
Inhaltsangabe
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
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
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