A recursive approach for estimating missing observations in an univariate time series

A linear recursive technique that does not use the Kalman filter approach is proposed to estimate missing observations in an univariate time series. It is assumed that the series follows an invertible ARIMA model. The procedure is based on the restricted forecasting approach, and the recursive linear estimators are optimal in terms of minimum mean-square error.     

Minimum Distance Estimation of Search Costs Using Price Distribution

ABSTRACT

It has been shown that equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observedprices alone. These quantiles can be difficult to estimate in practice. This article uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least-square problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogenous markets are available. We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues’ matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. Supplementary materials for this article are available online.     

An automatic classification and robust segmentation procedure of spatial objects

This paper proposes a statistical procedure for the automatic volumetric primitives classification and segmentation of 3D objects surveyed with high density laser scanning range measurements. The procedure is carried out in three main phases: first, a Taylor’s expansion nonparametric model is applied to study the differential local properties of the surface so to classify and identify homogeneous point clusters. Classification is based on the study of the surface Gaussian and mean curvature, computed for each point from estimated differential parameters of the Taylor’s formula extended to second order terms. The geometrical primitives are classified into the following basic types: elliptic, hyperbolic, parabolic and planar. The last phase corresponds to a parametric regression applied to perform a robust segmentation of the various primitives. A Simultaneous AutoRegressive model is applied to define the trend surface for each geometric feature, and a Forward Search procedure puts in evidence outliers or clusters of non stationary data. An erratum to this article can be found at     

Neo-development of underdevelopment: Brazil and the political economy of South American integration under the Workers’ Party

ABSTRACT

This article critically assesses Brazil’s role in the South American regional integration process. My hypothesis is that despite the rhetoric of Brazil’s Workers’ Party (PT) governments about a ‘new developmentalism’ project to support ‘post-neoliberal’ regional integration, the structural continuities imposed by neoliberal macroeconomic policies have constrained all possibilities of overcoming underdevelopment. In the realm of regional integration, the driving force has been the internationalization of oligopolic Brazilian business in a process that promised Brazil a leadership role in the subcontinent. This frame has fostered business based on the overexploitation of labour and the destruction of the environment, enforcing trends that deepen the structures of economic dependency and social conflict. The political outcome of that process is that the PT has contributed to contain social pressures, both in the domestic and in regional contexts, as Brazil has played a moderating role in South America’s so-called progressive wave.     

Public–private versus public ownership and economic performance: evidence from Italian local utilities

In the last century local public services have often been reformed. The declared outcome of the most recent reforms is the privatization and liberalization of the sector. However, in almost all European countries, the privatization of local public services has been only partial, because local governments have sought to privatise a minority stake in the public owned-companies, while remaining committed to retaining public ownership and control over the longer-term as a means of protecting public interest. The phenomenon of mixed public–private companies emerged as a result of this process. In this context, the article investigates whether differences in financial performance can be found between public–private companies and totally public-owned enterprises. Empirical quantitative studies on this particular topic are quite lacking at the moment. The present study tries to fill this gap through an empirical analysis on a sample of 623 Italian local utilities. The results of the study suggest that there are differences in economic performance between local utility companies with varying degrees of public ownership. In particular, public–private utilities show better economic performance than publicly owned firms, especially in terms of profitability. The results also seem to suggest that the majority private ownership is not necessary for better performance. In other terms, public–private partnership—and not private majority ownership—seems to be the key point for good performance.     

Illegal Immigrants and the Informal Economy in Italy

ABSTRACT: The present work is divided into three parts. In the first part the (limited) empirical data relevant to the phenomenon in question is presented, that is, the growing illegality of Italian immigration and the illegal work that immigrants are destined to take up. Obviously the illegality of their presence and the íllicitness of the work are, by definition, phenomena which are difficult to quantify. Some estimates, albeit limited to certain regions, are nevertheless available and the picture that emerges is coherent with the present hypothesis. The hypothesis is then explained at a theoretical level, in the second part, where a simple model of general equilibrium is presented, referring to a two-sector economy with a single good. The model has the sole aim of illustrating the plausible characteristics of an economy characterised by the phenomenon that this present paper attempts to clarify. In the third part, to give an example, a simulation is put forward, utilising, in the model, values not incoherent with estimates based on Italy as a whole.     

Robust estimation in long-memory processes under additive outliers

In this paper, we introduce an alternative semiparametric estimator of the fractional differencing parameter in ARFIMA models which is robust against additive outliers. The proposed estimator is a variant of the GPH estimator [Geweke, J., Porter-Hudak, S., 1983. The estimation and application of long memory time series model. Journal of Time Series Analysis 4, 221–238]. In particular, we use the robust sample autocorrelations of Ma, Y. and Genton, M. [2000. Highly robust estimation of the autocovariance function. Journal of Time Series Analysis 21, 663–684] to obtain an estimator for the spectral density of the process. Numerical results show that the estimator we propose for the differencing parameter is robust when the data contain additive outliers.