This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future...

We propose Midastar models by combining the Mixed Data Sampling (MIDAS) and the threshold autoregression (TAR). The Midastar model of the first kind is designed for a low frequency target variable and a high frequency threshold variable. The proposed model can detect threshold effects...

Using data from a prominent online platform for launching new digital products, we document that the composition of the platform's ‘beta testers’ on the day a new product is launched has a systematic and persistent impact on the venture's success. Specifically, we use word embedding methods...

We studied the effects of sample size and distribution scale/shape for 3 types of skewness (g1, G1, and b1) and kurtosis (g2, G2, and b2) using 18 simulated probability distributions. In general, skewness and kurtosis always increased with increasing sample size. The order in the skewness...

When valuing American options with simulations and regressions, Rasmussen (2005) demonstrated that the variance-minimizing control variate is sampled at the recorded exercise time. The present article further discusses the application of optimal control variates in the context of the...

Maximum Likelihood (ML) shows both lower power and higher bias in small sample Monte Carlo experiments than Indirect Inference (II) and IIís higher power comes from its use of the model-restricted distribution of the auxiliary model coeffi cients (Le et al. 2016). We show here that IIís higher...