Generalized Tobit models: diagnostics and application in econometrics

The standard Tobit model is constructed under the assumption of a normal distribution and has been widely applied in econometrics. Atypical/extreme data have a harmful effect on the maximum likelihood estimates of the standard Tobit model parameters. Then, we need to count with diagnostic tools to evaluate the effect of extreme data. If they are detected, we must have available a Tobit model that is robust to this type of data. The family of elliptically contoured distributions has the Laplace, logistic, normal and Student-t cases as some of its members. This family has been largely used for providing generalizations of models based on the normal distribution, with excellent practical results. In particular, because the Student-t distribution has an additional parameter, we can adjust the kurtosis of the data, providing robust estimates against extreme data. We propose a methodology based on a generalization of the standard Tobit model with errors following elliptical distributions. Diagnostics in the Tobit model with elliptical errors are developed. We derive residuals and global/local influence methods considering several perturbation schemes. This is important because different diagnostic methods can detect different atypical data. We implement the proposed methodology in an R package. We illustrate the methodology with real-world econometrical data by using the R package, which shows its potential applications. The Tobit model based on the Student-t distribution with a small quantity of degrees of freedom displays an excellent performance reducing the influence of extreme cases in the maximum likelihood estimates in the application presented. It provides new empirical evidence on the capabilities of the Student-t distribution for accommodation of atypical data.     

The Characteristics of Takeover Targets: The Spanish Experience 1991–1997

In this paper we analyse the characteristicspresented by Spanish firms that have been thesubject of takeover bids that allow them to bedifferentiated from other firms. To that end,we use a sample made-up of 69 non-financialfirms that were the subject of takeover bids inSpain during the period 1991–1997, togetherwith a further 69, selected randomly on anindustrial and time basis, that were not. Theresults show that the firms that were subjectto a takeover bid were not, in general,characterised by having lower profitability ora worse market valuation than other firmsoperating in the same sector. This resultmakes it difficult to argue that takeovers inSpain have in their majority been driven bydisciplinary or speculative motives. Bycontrast, certain variables that exert aninfluence over the cost of the transaction,such as the size and the ownership structure ofthe firm, do appear to play a relevant role inthe selection of the target on the part of thebidding firm.     

Educating Administrators to Interact with Citizens: A Research Note

This article examines the place of citizen participation courses in American Masters of Public Administration programs. A survey of course syllabi shows that very few programs provide courses that focus on citizen participation. While the evidence on course content is more ambiguous, course goals tend to focus on imparting cognitive knowledge rather than developing skills or engendering psychological transformation—outcomes that may be key to having administrators who are comfortable working with citizens. An argument is made for a more visible place for citizen-participation courses to show that the ability to involve citizens is part of administrative competence.     

Optimal classification of Gaussian processes in homo- and heteroscedastic settings

A procedure to derive optimal discrimination rules is formulated for binary functional classification problems in which the instances available for induction are characterized by random trajectories sampled from different Gaussian processes, depending on the class label. Specifically, these optimal rules are derived as the asymptotic form of the quadratic discriminant for the discretely monitored trajectories in the limit that the set of monitoring points becomes dense in the interval on which the processes are defined. The main goal of this work is to provide a detailed analysis of such optimal rules in the dense monitoring limit, with a particular focus on elucidating the mechanisms by which near-perfect classification arises. In the general case, the quadratic discriminant includes terms that are singular in this limit. If such singularities do not cancel out, one obtains near-perfect classification, which means that the error approaches zero asymptotically, for infinite sample sizes. This singular limit is a consequence of the orthogonality of the probability measures associated with the stochastic processes from which the trajectories are sampled. As a further novel result of this analysis, we formulate rules to determine whether two Gaussian processes are equivalent or mutually singular (orthogonal).

     

Model selection for stock prices data

The geometric Brownian motion (GBM) is very popular in modeling the dynamics of stock prices. However, the constant volatility assumption is questionable and many models with nonconstant volatility have been developed. In the papers [7 M.L. Esquível and P.P. Mota, On some auto-induced regime switching double-threshold glued diffusions, J. Stat. Theory Pract. 8 (2014), pp. 760–771. doi: 10.1080/15598608.2013.854184.[Taylor &; Francis Online] , [Google Scholar],12 P. P. Mota and M.L. Esquível, On a continuous time stock price model with regime switching, delay, and threshold, Quant. Financ. 14 (2014), pp. 1479–1488. doi: 10.1080/14697688.2013.879990.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] the authors introduce a regime switching process where in each regime the process is driven by GBM and the change in regime is defined by the crossing of a threshold. In this paper we used Akaike's and Bayesian information criteria to show that the GBM with regimes provides a better fit than the GBM. We also perform a forecasting comparison of the models for two selected companies.     

The Impacts of State Surveillance on Political Assembly and Association: A Socio-Legal Analysis

Based on group interviews conducted in 2006 that included 71 social justice organizations, this paper analyzes the impact of surveillance on the exercise of assembly and association rights. We link these protected legal activities with analytic frameworks from social movements scholarship in order to further a socio-legal conception of political violence against social movements.

Crimes and violence in Mexico: Evidence from panel data

Drug-related violence is now an everyday occurrence in Mexico's northern border-states. While it often appears that all crimes are on the rise in the border region, the actual number of alleged offenders caught and brought to court differs significantly for different types of crime. Using panel data for Mexico's 31 states plus the Federal District from 2004 to 2008, it was found that there were more alleged offenders caught per 100,000 inhabitants involving theft, property damage, and carrying prohibited arms, in Mexico's northern border-states compared to interior states. The effects of other socioeconomic variables on crime in Mexico, such as GDP, incarceration rates, births to single mothers and expenditures on public security, were also examined.     

Accuracy of Simulations for Stochastic Dynamic Models

This paper is concerned with accuracy properties of simulations of approximate solutions for stochastic dynamic models. Our analysis rests upon a continuity property of invariant distributions and a generalized law of large numbers. We then show that the statistics generated by any sufficiently good numerical approximation are arbitrarily close to the set of expected values of the model's invariant distributions. Also, under a contractivity condition on the dynamics, we establish error bounds. These results are of further interest for the comparative study of stationary solutions and the estimation of structural dynamic models.