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 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.

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