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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