Jesus Orbe

Length of time spent in Chapter 11 bankruptcy: a censored partial regression model

This paper investigates original issuers of high yield bonds in Chapter 11 bankruptcy to determine which factors affect the length of time spent in Chapter 11. In order to do this analysis a flexible new duration model is proposed, the censored partial regression model. This model allows consideration of the effect of some variables on the duration using a nonparametric functional form. It is found that the choice of prepackaged Chapter 11, the length of time negotiating before filling for Chapter 11, the profitability, the highly leveraged transactions, the participation on different disputes, the role of vulture funds and some institutional changes turn out to be relevant to analyse this duration.

Modelling the duration of firms in Chapter 11 bankruptcy using a flexible model

Abstract This paper analyzes the duration of firms in Chapter 11 bankruptcy using a flexible model without assuming any probability distribution. We use bootstrap techniques to make inference on the estimators, and propose a new bootstrap procedure for censored samples.

Using the bootstrap for finite sample confidence intervals of the log periodogram regression

Log periodogram regression is widely applied in empirical applications to estimate the memory parameter, d, of long memory time series. This estimator is consistent for d<1 and pivotal asymptotically normal for d<3/4. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Finite sample improvements in the construction of confidence intervals can be achieved by different nonparametric bootstrap procedures based on the residuals of log periodogram regression. In addition to the basic residual bootstrap, the local and block bootstraps seem adequate for replicating the structure that may arise in the errors of the regression when the series shows weak dependence in addition to long memory. The performances of different bias correcting bootstrap techniques and a bias reduced log periodogram regression are also analyzed with a view to adjusting the bias caused by that structure. Finally, an application to the Nelson and Plosser US macroeconomic data is included.

Alternative approaches to study lifetime data under different scenarios: from the PH to the modified semiparametric AFT model

The problem of lifetime data in which censored observations are present is considered. In addition, it introduces the different characteristics that censored data have, together with the different scenarios that would lead to the application of the appropriate statistical approaches. The analysis of these scenarios will be mainly centered on the knowledge of the distribution for the survival time and the functional relationship between the survival time and the different covariates available in heterogeneous populations. The proposals are applied to a real data set where the survival time of AIDS-diagnosed patients in the Basque Country (Spain) is studied.

Bootstrap-based bandwidth choice for log-periodogram regression

The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins. Copyright 2009 Blackwell Publishing Ltd

A bootstrap approximation for the distribution of the Local Whittle estimator

The asymptotic properties of the Local Whittle estimator of the memory parameter d have been widely analysed and its consistency and asymptotic distribution have been obtained for values of d ź ( - 1 / 2 , 1 in a wide range of situations. However, the asymptotic distribution may be a poor approximation of the exact one in several cases, e.g. with small sample sizes or even with larger samples when d 0.75 . In other situations the asymptotic distribution is unknown, as for example in a noninvertible context or in some nonlinear transformations of long memory processes, where only consistency is obtained. For all these cases a bootstrap strategy based on resampling a (perhaps locally) standardised periodogram is proposed. A Monte Carlo analysis shows that this strategy leads to a good approximation of the exact distribution of the Local Whittle estimator in those situations where the asymptotic distribution is not reliable.

Confidence Intervals on Regression Models with Censored Data

Stute (1993, Consistent estimation under random censorship when covariables are present. Journal of Multivariate Analysis 45, 89–103) proposed a new method to estimate regression models with a censored response variable using least squares and showed the consistency and asymptotic normality for his estimator. This article proposes a new bootstrap-based methodology that improves the performance of the asymptotic interval estimation for the small sample size case. Therefore, we compare the behavior of Stutes asymptotic confidence interval with that of several confidence intervals that are based on resampling bootstrap techniques. In order to build these confidence intervals, we propose a new bootstrap resampling method that has been adapted for the case of censored regression models. We use simulations to study the improvement the performance of the proposed bootstrap-based confidence intervals show when compared to the asymptotic proposal. Simulation results indicate that, for the new proposals, coverage percentages are closer to the nominal values and, in addition, intervals are narrower.

Statistical Time to Event Analysis in the Social Sciences: Modeling Hazard Rate and Duration in Finance

Abstract. The relevance of statistical time to event analysis in the social sciences has proved to be of great importance in the last few years, especially in applications related to labor-market analysis, employment and/or unemployment issues, duration of strikes, and survival of new firms, and in financial applications related to the time a company spends in a given status, for example, bankruptcy. We review some of the techniques that have proved to be adequate for analyzing this type of data and the conditions they require for their proper use. In addition, we extend these techniques in order to be able to analyze specific and more complex situations by using a more general and flexible model. All of these techniques and their extensions are illustrated with an example that studies the duration of firms under bankruptcy in the United States.

A New Model for AIDS Survival Analysis

In this work we present a new methodology proposed to study the survival time of Acquired Immune-Deficiency Syndrome diagnosed patients. This methodology is very flexible because it does not need the assumption of proportional hazards and the estimation is carried out without assuming any probability distribution for the variable of interest. The inference in the model has been put forward using bootstrap techniques. The main conclusion of the study is that the age of the patients and the period of diagnosis are relevant variables to explain the survival time for these patients.