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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.

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AMAZONOxford University press


Each chapter in Applied Economic Forecasting Using Time Series Methods starts with a review of the main theoretical results to
prepare the reader for the various applications. Examples involving simulated data follow, to make the reader familiar with
application using at rst stylized settings where one knows the true data generating process and one learns how to apply the techniques
introduced in each chapter. The simulated examples are followed by real data applications – focusing on macroeconomic and financial topics.
Some of the examples run across different chapters, particularly in the early part of the book. All data are public domain and cover Euro area,
UK, and US examples, including forecasting US GDP growth, default risk, inventories, effective federal funds rates, composite index of
leading indicators, industrial production, Euro area GDP growth, UK term structure of interest rates, to mention the most prominent examples.
The link below provides companion EViews and R codes, for almost all the simulated and empirical examples in the book.
In addition, we also provides slides for the first 6 chapters of the book.


Mixed Frequencies

Regression models, and other econometric methods, involving data sampled at different frequencies are of general interest. Ghysels, Santa-Clara, and Valkanov (2004 Disc. Paper, 2005, J.Fin.Ec., 2006, J. Econometrics) introduced MIDAS – meaning Mi(xed) Da(ta) S(ampling) – regressions and related econometric methods.

Mixed data sampling (MIDAS) related links

>MIDAS entry
>Nowcasting entry
>MIDAS regression EViews

Midas code

Links below provide codes for running such regressions based on a framework put forward in recent work by Ghysels, Santa-Clara, and Valkanov (2002), Ghysels, Santa-Clara, and Valkanov (2006) and Andreou, Ghysels, and Kourtellos (2008a) using so called MIDAS, meaning Mi(xed) Da(ta) S(ampling), regressions.

Codes for Running MIDAS Regressions and Related Econometric Methods

> Matlab Toolbox
> R Toolbox (Midasr)
> MIDAS EViews
> MIDAS Gretl