Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a...

We summarize the recent developments on the statistical method of Lasso-Quantile Regression and we apply it to a Non-life Insurance problem. We discuss the asymptotic properties of the Quantile Regression estimator, the computational aspects related to the Linear Programming problem and the...

This paper develops a uniform test of linearity against threshold effects in the quantile regression framework. The test is based on the supremum of the Wald process over the space of quantile and threshold parameters. We establish the limiting null distribution of the test statistic for...

This paper addresses the problem of nonparametric estimation of the conditional expected shortfall (CES) that has gained popularity in financial risk management. We propose a new nonparametric estimator of the CES. The proposed estimator is defined as a conditional counterpart of the sample...

This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, <italic>n</italic>, and the number of time periods, <italic>T</italic>, are large. We first clarify the probability limit of the FE estimator and argue that this probability...

The main topic of this paper is "information variables" (or "indicators") of monetary policy, which work as criteria for setting the direction of monetary policy. After briefly surveying the notion and candidates of information variables, according to the studies mainly in the United States,...

The convexity arguments developed by Pollard [D. Pollard, Asymptotics for least absolute deviation regression estimators, Econometric Theory 7 (1991) 186-199], Hjort and Pollard [N.L. Hjort, D. Pollard, Asymptotics for minimizers of convex processes, 1993 (unpublished manuscript)], and Geyer...

We study the degrees of freedom in shrinkage estimation of regression coefficients. Generalizing the idea of the Lasso, we consider the problem of estimating the coefficients by minimizing the sum of squares with the constraint that the coefficients belong to a closed convex set. Based on a...