Two separate branches of research have shown the empirical benefits of nonlinearities in macroeconomic models and bounded rationality, respectively. This paper bridges these literatures by developing a methodology for estimating DSGE models when expectations are formed by adaptive learning...

We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise...

This note looks at estimation of spatial autoregressive models for non-negative and count outcomes with multiplicative fixed effects. We show that in presence of significant proportion of zeros in the count variable, control function and Instrumental Variable estimation to model such spatial...

We show how solutions to a large class of partial differential equations with nonlocal Riccati-type nonlinearities can be generated from the corresponding linearized equations, from arbitrary initial data. It is well known that evolutionary matrix Riccati equations can be generated by projecting...

This note looks at estimation of count models with multiplicative fixed effects and spatial dependence in the form of spatial lag autocorrelation. We show that in presence of significant proportion of zeros in the count variable, control function estimation to model such spatial dependence is...

We use a Bayesian Threshold Vector Autoregression model identified through sign and narrative restrictions to uncover non-linearities in the propagation of energy supply shocks. We find that the transmission of energy supply shocks on consumer prices is stronger in high-inflation regimes,...