Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations. An exception...

This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model...

The objective of this paper is to examine the effects of knowledge interaction on different types of business innovation. We first identified three indicators that reflect on the quality of the interaction between customers and technological knowledge, and then classified business innovations as...

This paper proposes an ex post volatility estimator, called mixed interval realized variance (MIRV), that uses high-frequency data to provide measurements robust to the idiosyncratic noise of stock markets caused by market microstructures. The theoretical properties of the new volatility...

We propose a shrinkage estimator for covariance matrices designed to minimize estimation error of the Global Minimum Variance (GMV) portfolio. Implementing the GMV portfolio requires estimating the asset covariance matrix and using this to obtain variance-minimizing portfolio weights. Standard...

Time-Varying parameter models have become more popular in recent years, especially as they are adapted to accommodate larger datasets. However, all recent developments use standard priors, specifically the Inverse-Wishart class of priors over the parameter error covariance matrix. In this paper,...

Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the over-rejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that...

Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, or other social sciences. They are employed to adjust the inference following estimation of a standard least-squares regression or...