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 13: Temporal aggregation in macroeconomics

Michael A. Thornton and Marcus J. Chambers

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


When trying to draw inferences about economic behaviour from a set of data it is important to disentangle the process driving the economic variables of interest from extraneous effects generated by the process of data collection itself. Such considerations are familiar to microeconometricians, who must contend with the effects of sampling, measurement error, mis-reporting, truncation and other causes of bias, but they have not received the same level of attention in macroeconomics. This chapter examines one such feature that has, however, received considerable attention: the frequency of data collection. Very often the data of interest to the macroeconomist are based on observations taken quarterly, possibly monthly or annually, but in a modern economy the activity to which they relate is ongoing. These macroeconomic data stand in stark contrast to some financial data, which are available in real time. For example, one can find the market price of an asset for any given moment when the market is open, but there is no measure of inflation or gross domestic product for that moment, only one relating to the month or quarter in which the moment falls. Having such gaps between observations is a limitation in the data, especially when underlying events are fast moving and the economic relationships are dynamic.

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