Given a nonparametric regression model, we assume that the number of covariates $d\rightarrow\infty$ but only some of these covariates are relevant for the model. Our goal is to identify the relevant covariates and to obtain some information about the structure of the model. We propose a new...

I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) methods, introduce a Mata function for per- forming adaptive MCMC, amcmc(), and a suite of functions amcmc_*() allowing an implementation of adaptive MCMC using a structure. To ease use in...

The sampling design strategies used for estimation of economic indicators in short-term statistics domain are heterogeneous depending on the characteristic the variable to be estimated and timeliness required for the indices. On the occasion of change of the base year for the turnover indices...

The Italian National Institute of Statistics (Istat) has set up a survey on income and living conditions (It-Silc), mainly composed of a cross-sectional and a longitudinal component.This paper aims at proving, by a decomposition of Gini concentration index, also known as Analysis of Gini...

Financial returns exhibit common behavior described at best by factor models, but also fat tails, which may be captured by α-stable distributions. This paper concentrates on estimating factor models with multivariate α-stable distributed and independent factors and idiosyncratic noises under...

This document reviews and applies recently developed techniques for Bayesian estimation and model selection in the context of Time Series modelingfor Stochastic volatility. After the literature review on Generalized Conditional Autoregressive models, Stochastic Volatility models, and the...

In the context of an autoregressive panel data model with fixed effect, we examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the transformation of the fixed effect proposed by Lancaster...

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....

This paper derives six different forms of message length functions for general linear regression model. In so doing, two different prior densities and the idea of parameter orthogonality are employed.

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined...