Banks have always played an ambivalent role in financial markets. On the one hand, they provide essential services for the market; on the other hand, problems in the banking sector can send shock waves through the entire economy. Given this prominent role, it is not surprising that Pereira and...

This paper proposes a minimum distance (MD) estimator to estimate panel regression models with measurement error. The model considered is more general than examined in the literature in that (i) measurement error can be non-classical in the sense that they are allowed to be correlated with the...

Broadband availability data collected by the Federal Communications Commission (“FCC”) using its Form 477 are heavily criticized as inaccurate. These data certainly overstate availability since a census block is indicated as having broadband throughout the block even if only a single home in...

In analyses of minimum wages, positive "ripple effects" and subminimum wages are difficult to distinguish from measurement error. Indeed, prior work posits that a simple, symmetric measurement process may underlie both phenomena in Current Population Survey data for the full working age...

We capitalise on an opportunity in the UK Household Longitudinal Study, which asks respondents the same SAH question with identical wording two times. This is done once with a self-completion and once with an open interview mode within the same household interview over four waves. We estimate...

This paper proposes a convenient approach to measure the time-varying volatility connectedness indexes by developing the connectedness measures within a multivariate Heterogeneous Autoregressive model with measurement errors (VHAR-Q). We utilize this framework to examine the dynamic volatility...

Emerging literature focuses on insurers' earnings management using estimated liability for unpaid claims, known as loss reserve. An insurance company generally uses the traditional estimation methods with linear estimation to measure loss reserve error, but those methods are often criticized for...

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting earnings and returns presents difficult challenges. In the context of these challenges, we...