We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with n. In addition to the case of known error-variance, we define and study versions of the...

An omnibus test for spherical symmetry in R2 is proposed, employing localized empirical likelihood. The thus obtained test statistic is distri- bution-free under the null hypothesis. The asymptotic null distribution is established and critical values for typical sample sizes, as well as the...

The 2008 crisis underscored the interconnectedness of the international business cycle, with U.S. shocks leading to the largest global slowdown since the 1930s. We estimate spillover effects across major advanced country regions in a structural VAR (SVAR) using pre-crisis data. Our new method...

We study the sovereign default model that has been used to account for the cyclical behavior of interest rates in emerging market economies. This model is often solved using the discrete state space technique with evenly spaced grid points. We show that this method necessitates a large number of...

The study presents an analysis of the information content of IMF’s Data Quality Assessment Framework (DQAF) indicators. There are significant differences in the quantity of information between DQAF dimensions and sub-dimensions. The most informative DQAF dimension is accessibility,...

This paper estimates an empirical nonstationary panel regression model that tests long-run consumption risk sharing across a sample of OECD and emerging market (EM) countries. This is in contrast to the existing literature on consumption risk sharing, which is mainly about risks at business...

In this paper, we study semiparametric estimation for a single-index panel data model where the nonlinear link function varies among the individuals. We propose using the so-called refined minimum average variance estimation based on a local linear smoothing method to estimate both the...

The empirical saddlepoint likelihood (ESPL) estimator is introduced. The ESPL provides improvement over one-step GMM estimators by including additional terms to automatically reduce higher order bias. The first order sampling properties are shown to be equivalent to efficient two-step GMM. New...

The empirical saddlepoint likelihood (ESPL) estimator is introduced. The ESPL provides improvement over one-step GMM estimators by including additional terms to automatically reduce higher order bias. The first order sampling properties are shown to be equivalent to efficient two-step GMM. New...