We propose a new non-recursive identification scheme for uncertainty shocks, which exploits breaks in the unconditional volatility of macroeconomic variables. Such identification approach allows us to simultaneously address two major questions in the empirical literature on uncertainty: (i) Does...

A new mixture autoregressive model based on Student's t-distribution is proposed. A key feature of our model is that the conditional t-distributions of the component models are based on autoregressions that have multivariate t-distributions as their (low-dimensional) stationary distributions....

I develop an ex-post conservativism measure (that identifies conservatism based on whether firms understate or overstate earnings) rather than an ex-ante conservatism measure (that suffices as a governance mechanism [such as conservative accounting rules] to compel managers to be conservative)....

The weekly change of several German milkbased commodity prices do not only exhibit traditional patterns like mean dependence and volatility clustering, but also a high frequency of zero changes which can not be explained by well known ARIMA-GARCH models. Therefore, we develop a new mixture model...

We propose a new non-recursive identification scheme for uncertainty shocks, which exploits breaks in the unconditional volatility of macroeconomic variables. Such identification approach allows us to simultaneously address two major questions in the empirical literature on uncertainty: (i) Does...

We consider robust inference for an autoregressive parameter in a stationary autoregressive model with GARCH innovations when estimation is based on least squares estimation. As the innovations exhibit GARCH, they are by construction heavy-tailed with some tail index κ. The rate of consistency...

In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (BPQ). In BPQ the volatility dynamics modeled as a HAR is augmented by a term involving quarticity in order to correct measurement errors in realized variance. We show that the model is...

A method for estimation of heteroscedastic systems of equations that are more efficient than SUR and produce robust covariance matrices for parameter estimators is developed and applied to the Klein railroad data

In this paper, we develop a new asymptotic theory of the long run variance estimator obtained by fitting a vector autoregressive model to the transformed moment processes in a GMM framework. In contrast to the conventional asymptotics where the VAR lag order p goes to infinity but at a slower...