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Volatility and Time Series EconometricsEssays in Honor of Robert Engle$
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Tim Bollerslev, Jeffrey Russell, and Mark Watson

Print publication date: 2010

Print ISBN-13: 9780199549498

Published to Oxford Scholarship Online: May 2010

DOI: 10.1093/acprof:oso/9780199549498.001.0001

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Macroeconomics and ARCH

Macroeconomics and ARCH

(p.79) 5 Macroeconomics and ARCH
Volatility and Time Series Econometrics

James D. Hamilton

Oxford University Press

Studying volatility has traditionally been a much lower priority for macroeconomists than for researchers in financial markets because the former's interest is primarily in describing the first moments. There seems to be an assumption among many macroeconomists that, if your primary interest is in the first moment, autoregressive conditional heteroskedasticity (ARCH) has little relevance apart from possible GARCH-M effects. This chapter suggests that even if our primary interest is in estimating the conditional mean, having a correct description of the conditional variance can still be quite important, for two reasons. First, hypothesis tests about the mean in a model in which the variance is mis-specified will be invalid. Second, by incorporating the observed features of the heteroskedasticity into the estimation of the conditional mean, substantially more efficient estimates of the conditional mean can be obtained.

Keywords:   autoregressive conditional heteroskedasticity, GARCH-M effects, volatility, conditional variance, conditional mean

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