Table of Contents

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbooks of Research Methods and Applications series

Edited by Nigar Hashimzade and Michael A. Thornton

This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading.

Chapter 12: Conditional heteroskedasticity in macroeconomics data: UK inflation, output growth and their uncertainties

Menelaos Karanasos and Ning Zeng

Subjects: economics and finance, econometrics, research methods, research methods in economics

Extract

The conditional heteroskedasticity models are widely used in the financial economics and less frequently so in other fields, including macroeconomics. However, certain applications lend themselves naturally to the investigation of possible links between macroeconomic variables and their volatilities, and here the conditional heteroskedasticity approach proved to be a powerful tool. The basics of the univariate models with conditional heteroskedasticity have been introduced in Chapter 2 in this volume. In this chapter, we extend this to a bivariate model and illustrate how this approach can be used to investigate the link between UK inflation, growth and their respective uncertainties, using a particular bivariate model with conditional heteroskedasticity. For recent surveys on multivariate GARCH specifications and their importance in various areas such as asset pricing, portfolio selection, and risk management see, for example, Bauwens et al. (2006) and Silvennoinen and Teräsvirta (2007).

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