University of Calgary

Conditional Correlation Demand Systems


We address the estimation of singular demand systems with heteroscedastic disturbances. As in Serletis and Isakin (2017) and Serletis and Xu (2019) we assume that the covariance matrix of the errors of the demand system is time-varying, and contribute to the literature by considering the constant conditional correlation (CCC) and dynamic conditional correlation (DCC) parameterizations of the variance model. We derive a number of important practical results and also provide an empirical application to support our methodology.
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