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 4: Unit roots, non-linearities and structural breaks

Niels Haldrup, Robinson Kruse, Timo Teräsvirta and Rasmus T. Varneskov

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


It is widely accepted that many time series in economics and finance exhibit trending behaviour in the level (or mean) of the series. Typical examples include asset prices, exchange rates, real GDP, real wage series and so forth. In a recent paper White and Granger (2011) reflect on the nature of trends and make a variety of observations that seem to characterize these. Interestingly, as also noted by Phillips (2005), even though no one understands trends everybody still sees them in the data. In economics and other disciplines, almost all observed trends involve stochastic behaviour and purely deterministic trends are rare. However, a combination of stochastic and deterministic elements including structural changes seems to be a model class which is likely to describe the data well. Potentially the series may contain non-linear features and even the apparent deterministic parts like level and trend may be driven by an underlying stochastic process that determines the timing and the size of breaks. In recent years there has been a focus on stochastic trend models caused by the presence of unit roots. A stochastic trend is driven by a cumulation of historical shocks to the process and hence each shock will have a persistent effect. This feature does not necessarily characterize other types of trends where the source of the trend can be different and some or all shocks may only have a temporary effect.

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