The paper studies inference in nonlinear models where identification loss presents in multiple parts of the parameter space. For uniform inference, we develop a local limit theory that models mixed identification strength. Building on this non-standard asymptotic approximation, we suggest robust...

This paper analyzes the properties of standard estimators, tests, and confidence sets (CS's) in a class of models in which the parameters are unidentified or weakly identified in some parts of the parameter space. The paper also introduces methods to make the tests and CS's robust to such...

The reaction coefficients in the forecast-based monetary policy reaction function are only weakly identified when the smoothing coefficient for the nominal interest rate is close to unity. This situation also causes the nominal interest rate to be highly persistent. Inference based on the...

This paper estimates the curvature of the Earth, defined as one over its radius, without using any physics. The orthodox model is that the Earth is nearly spherical with a curvature of π/20, 000 km. By contrast, the heterodox flat-Earth model stipulates a curvature of zero. Abstracting from the...

A family of threshold nonlinear generalised autoregressive conditionally heteroscedastic models is considered, that allows smooth transitions between regimes, capturing size asymmetry via an exponential smooth transition function. A Bayesian approach is taken and an efficient adaptive sampling...

This paper develops particle-based methods for sequential inference in nonlinear models. Sequential inference is notoriously difficult in nonlinear state space models. To overcome this, we use auxiliary state variables to slice out nonlinearities where appropriate. This induces a Fixed-dimension...

This paper explores the uniformity of inference for parameters of interest in nonlinear models with endogeneity. The notion of uniformity is fundamental in these models because due to potential endogeneity, the behavior of standard estimators of these parameters is shown to vary with where they...

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM...

Event studies have become pivotal in securities fraud litigation. The typical use-cases are single firm / single event (SFSE) applications. These differ from the standard event study methodology in that inference on abnormal returns can only be based on the time-series variance of abnormal...

This paper is concerned with tests and confidence intervals for parameters that are not necessarily point identified and are defined by moment inequalities. In the literature, different test statistics, critical-value methods, and implementation methods (i.e., the asymptotic distribution versus...