Abstract
Copyright: © 2012 Rahman S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Volatility has gained lots of attention in finance literature, as, for example, in studies of the relation between stock market returns and risk, but it has been a much lower priority for applied macroeconomists. In general, the existing macroeconometrics research is concerned mainly with the first moment (or mean) of the variables, while systematically ignoring the second moment (or variance). However, a correct specification of variance is still important for two reasons, as is explained by Hamilton and Herrera [1]. First, the test of hypothesis under misspecified variance is invalid. Second, it is possible to improve the efficiency of the conditional mean estimates by incorporating the observed feature of heteroscedasticity into the estimation process.
Highlights
There have been interesting empirical studies that examine the relationship between volatility of time series --- such as exchange rates, oil prices, stock prices, interest rates, or inflation --- and aggregate real output
A correct specification of variance is still important for two reasons, as is explained by Hamilton and Herrera [1]
It is possible to improve the efficiency of the conditional mean estimates by incorporating the observed feature of heteroscedasticity into the estimation process
Summary
There have been interesting empirical studies that examine the relationship between volatility of time series --- such as exchange rates, oil prices, stock prices, interest rates, or inflation --- and aggregate real output.
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