Lisa Crosato

Fitting Pareto II Distributions on Firm Size: Statistical Methodology and Economic Puzzles

We propose here a new implementation of the forward search, which is a powerful general method usually suitable for detecting extreme observations and for determining their effect on fitted models (Atkinson and Riani, 2000). Through the forward search we iteratively fit the Pareto II distribution to firm size data. In particular, a threshold is fixed to the fit of the Pareto II distribution through a progressive adaptation technique, performing at each iteration the χ 2 test to check for the acceptance of the null hypothesis. Yearly Zipf-plots of the truncated empirical distribution with superimposed theoretical Pareto II distribution highlight the adherence of the estimates to data for different size ranges. Possible economic interpretations of the results are then provided, referring in particular to the role of the stock market in shaping firm size distribution and to the firm size effect (Banz, 1981; Reingaum, 1981). More in general, we discuss possible implications of introducing our methodology in macroeconomic models.

Forecasting the Volatility of Electricity Prices by Robust Estimation: An Application to the Italian Market

Volatility of electricity prices has been often estimated through GARCH-type models which can be strongly affected by the presence of extreme observations. Although the presence of spikes is a well-known stylized effect observed on electricity markets, robust volatility estimators have not been applied so far. In this paper we try to fill this gap by suggesting a robust procedure to the study of the dynamics of electricity prices. The conditional mean of de-trended and seasonally adjusted prices is modeled through a robust estimator of SETAR processes based on a polynomial weighting function while a robust GARCH is used for the conditional variance. The robust GARCH estimator relies on the extension of the forward search by Crosato and Grossi. The robust SETAR-GARCH model is applied to the Italian day-ahead electricity market using data in the period spanning from 2013 to 2015.

Updating CPI Weights Through Compositional VAR Forecasts: An Application to the Italian Index

Worldwide, monthly CPIs are mostly calculated as weighted averages of price relatives with fixed base weights. The main source of estimation of CPI weights are National Accounts, whose complexity in terms of data collection, estimation of aggregates and validation procedures leads to several months of delay in the release of the figures. This ends up in a non completely consistent Laspeyres formula since the weights do not refer to the same period as the base prices do, being older by one year and then corrected by the elapsed inflation. In this paper we propose to forecast CPI weights via a compositional VAR model, to obtain more updated weights and, consequently, a more updated measure of inflation through CPIs.

Linking Administrative Tax Records and Survey Expenditure Data at the Local Level

The combined use of administrative data and sample surveys has become increasingly important in statistical applications. In this paper, we focus on the combination of administrative tax records and sample survey data collecting information on household expenditures. Our purpose is to create a new dataset containing information from both data sources following an almost-exact matching approach.

Robust Tests for Pareto Density Estimation

A common practice to determine the extension and heaviness of heavy tails of income, return and size distributions is the sequential estimation and fitting of one or several models, starting from a group of the largest observations and adding one observation at a time [14]. In the early stages this kind of procedure shows high sensitivity of the shape parameter estimates to single observations, the end of the search being fixed when the shape parameter value estimates reach a plateau. In this paper we propose a stepwise fitting of a heavy-tailed model, the Pareto II distribution [1], previously applied to the size distribution of business firms. The procedure, based on the forward search technique [2], is data-driven since observations to be added at each iteration are determined according to the results of the estimation carried out at the preceding step and not, as in sequential fitting, according to their rank.

Firm Size Distribution and Returns to Scale. Non-Parametric Frontier Estimates from Italian Manufacturing

This paper explores the relationship between firm size distribution and technology. We analyse firm technology across selected manufacturing industries by means of a non-parametric production analysis, the Free Disposal Hull approach (Deprins et al., 1984; Kerstens and Vanden Eeckaut, 1999) and appraise the links between size and scale elasticity, finding a clear inverse relationship. Building on this result, we inquire whether the shape of the firm size distribution is related to a particular pattern of scale elasticities. We rely on the Zipf Plot (Stanley et al., 1995) of the Pareto IV distribution, which is concave up to a given threshold, and then approximately linear. Firms in the concave part of the plot are overwhelmingly found to experience increasing returns to scale. On the contrary, firms in the linear part are mainly characterised by constant returns to scale.

Dimensione e concentrazione dei gruppi bancari italiani nell'ultimo decennio

Size and concentration of Italian banking groups over the last decade - The paper analyzes the change in the size distribution of Italian banking groups over the period 1999 to 2007 following a wave of MA size distribution of banks; measures of concentration; credit rationing of SME; mergers and acquisitions Parole chiave: struttura dell’industria bancaria; distribuzione per dimensione delle banche; misure della concentrazione; razionamento del credito alle PMI; fusioni e acquisizioni Jel Classification: G21 - L11