I discuss nonlinear difference-in-differences models, arguing their interpretation depends on the context of their application. When parallel trends are assumed in the natural scale of the dependent variable, I contend the treatment effect is the interaction effect (a cross-difference), while if...

This paper estimates a model of the real exchange rate including standard fundamentals as well as two alternative measures of inflation expectations for five inflation targeting countries (UK, Canada, Australia, New Zealand, Sweden) over the period January 1993-July 2019. Both a benchmark linear...

We prove the well-posedness results, i.e. existence, uniqueness, and stability, of the solutions to a class of nonlocal fully nonlinear parabolic partial differential equations (PDEs), where there is an external time parameter $t$ on top of the temporal and spatial variables $(s,y)$ and thus the...

I demonstrate that Ai and Norton's (2003) point about cross differences is not relevant for the estimation of the treatment effect in nonlinear difference-in-differences models such as probit, logit or tobit, because the cross difference is not equal to the treatment effect, which is the...

Implications of nonlinearity, nonstationarity and misspecification are considered from a forecasting perspective. Our model allows for small departures from the martingale difference sequence hypothesis by including a nonlinear component, formulated as a general, integrable transformation of the...

The object of research: The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data.Investigated problem: There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear...

In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to...

The paper derives a structural identification of a vector smooth transition autoregression that yields a low-dimension and certainty-equivalent nonlinear optimization problem. Optimal rules are calculated with the state dependent coefficients approach, which allows linear methods to solve...

This paper examines how different categories of COVID–19 news sentiment differentially impact the behavior of cryptocurrency returns. A nonlinear technique of transfer entropy is applied to investigate the relationship between the top 30 cryptocurrencies by market capitalization and COVID–19...