This paper explores the statistical properties of household consumption-expenditure budget share distributions defined as the share of household total expenditure spent for purchasing a specific category of commodities for a large sample of Italian households in the period 1989-2004. We find...

This paper explores the statistical properties of household consumption-expenditure budget shares distributions (HBSDs) - defined as the share of household total expenditure spent for purchasing a specific category of commodities - for a large sample of Italian households in the period...

This paper explores the statistical properties of household consumption-expenditure budget shares distributions (HBSDs) defined as the share of household total expenditure spent for purchasing a specific category of commodities for a large sample of Italian households in the period 1989-2004. We...

This note discusses some problems possibly arising when approximating via MonteCarlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to reestimate unknown parameters on each simulated Monte-Carlo sample - and...

In this paper we explore the statistical properties of the distributions of consumption expenditures for a large sample of Italian households in the period 1989-2004. Goodness-of-fit tests show that household aggregate (and age-conditioned) consumption distributions are not log-normal. Rather,...

We propose new procedures for estimating the univariate quantities of interest in both additive and multiplicative nonparametric marker dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring. Our procedures are based on kernels...

We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding...

The question of whether empirical models are able to forecast the equity premium more accurately than the simple historical mean is intensively debated in the financial literature. The low prediction power is disappointing, even when using nonparametric models that make use of typical predictor...

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...