Most asset prices are subject to significant volatility. Arrival of new information is viewed as the main source of volatility. As new information is continually released, financial asset prices exhibit volatility persistence, which affects financial risk analysis and risk management strategies....

Some interventions or population attributes negate the effects of a treatment. This paper shows that incorporating these, what we call antidotal variables (AV), into a causal treatment effects analysis can with one cross-sectional regression identify the true causal effect, in addition to...

The paper reconsiders the Hodrick-Prescott filter and the issue of a suitable choice of its smoothing parameter λ for quarterly data. To this end stochastic processes generate artificial data with a known growth trend and cyclical component, and a battery of Monte Carlo experiments tests what...

We study semi-parametric estimation and inference in cointegrated panels with endogenous feedback, allowing for general time-series and cross-section dependence and heterogeneity.Central to this literature are the fully-modified OLS of Phillips and Hansen (1990) that use a spectral...

We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation. The...

A recent article by J.D. Hamilton from 2018 attracted a great deal of attention, not only because of its telling title, "Why you should never use the Hodrick- Prescott filter", but also because it offered an alternative approach to detrending, the Hamilton regression filter (HRF). His...

I provide conditions under which the trimmed FDQML estimator, advanced by McCloskey (2010) in the context of fully parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of additive low-frequency contamination in log-squared...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this article it is argued that one...

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an "in-sample"...