Star variables, such as potential output and the neutral real interest rate, are fundamental to economic policymaking but challenging to identify due to their latent nature. Buncic, Pagan, and Robinson (2023) highlight the difficulty of identifying star variables within short macroeconomic...

We propose a numerical method, based on indirect inference, for checking the identification of a DSGE model. Monte Carlo samples are generated from the model's true structural parameters and a VAR approximation to the reduced form estimated for each sample. We then search for a different set of...

This paper studies the computational complexity of Bayesian and quasi-Bayesian estimation in large samples carried out using a basic Metropolis random walk. The framework covers cases where the underlying likelihood or extremum criterion function is possibly non-concave, discontinuous, and of...

Bandwidth plays an important role in determining the performance of local linear estimators. In this paper, we propose a Bayesian approach to bandwidth selection for local linear estimation of time-varying coefficient time series models, where the errors are assumed to follow the Gaussian kernel...

We ask whether Bayesian estimation creates a potential estimation bias as compared with standard estimation techniques based on the data, such as maximum likelihood or indirect estimation. We investigate this with a Monte Carlo experiment in which the true version of a New Keynesian model may...

This paper studies computationally and theoretically attractive estimators referred here as to the Laplace type estimators (LTE). The LTE include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions,...

In this work we study the large sample properties of the posterior-based inference in the curved exponential family under increasing dimension. The curved structure arises from the imposition of various restrictions, such as moment restrictions, on the model, and plays a fundamental role in...

This article looks at the theory and empirics of extremal quantiles in economics, in particular value-at-risk. The theory of extremes has gone through remarkable developments and produced valuable empirical findings in the last 20 years. In the discussion, we put a particular focus on...

In this paper we study inference for a conditional model with a jump in the conditional density, where the location and size of the jump are described by regression lines. This interesting structure is shared by several structural econometric models. Two prominent examples are the standard...

We study the estimation of heterogeneous effects of group-level policies, using quantile regression with interactive fixed effects. Our approach can identify distributional policy effects, particularly effects on inequality, under a type of difference-in-differences assumption. We provide...