How Effective are Unemployment Benefit Sanctions? Looking Beyond Unemployment Exit

This paper provides a comprehensive evaluation of the effects of benefit sanctions on post-unemployment outcomes such as post-unemployment employment stability and earnings. We use rich register data which allow us to distinguish between a warning that a benefit reduction may take place in the near future and the actual withdrawal of unemployment benefits. Adopting a multivariate mixed proportional hazard approach to address selectivity, we find that warnings do not affect subsequent employment stability but do reduce post-unemployment earnings. Actual benefit reductions lower the quality of post-unemployment jobs both in terms of job duration as well as in terms of earnings. Copyright © 2012 John Wiley & Sons, Ltd.

Fourth order pseudo maximum likelihood methods

We extend PML theory to account for information on the conditional moments up to order four, but without assuming a parametric model, to avoid a risk of misspecification of the conditional distribution. The key statistical tool is the quartic exponential family, which allows us to generalize the PML2 and QGPML1 methods proposed in Gourieroux et al. (1984) to PML4 and QGPML2 methods, respectively. An asymptotic theory is developed. The key numerical tool that we use is the Gauss-Freud integration scheme that solves a computational problem that has previously been raised in several fields. Simulation exercises demonstrate the feasibility and robustness of the methods [Authors]

Winner's curse in toll road concessions

We empirically assess the effect of the winner's curse in auctions for toll road concessions, taking into account, to our knowledge for the first time, problems of commitment and enforcement, using a unique dataset of 49 worldwide road concessions.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

Three essays in dynamic corporate finance

The three essays constituting this thesis focus on financing and cash management policy. The first essay aims to shed light on why firms issue debt so conservatively. In particular, it examines the effects of shareholder and creditor protection on capital structure choices. It starts by building a contingent claims model where financing policy results from a trade-off between tax benefits, contracting costs and agency costs. In this setup, controlling shareholders can divert part of the firms' cash ows as private benefits at the expense of minority share- holders. In addition, shareholders as a class can behave strategically at the time of default leading to deviations from the absolute priority rule. The analysis demonstrates that investor protection is a first order determinant of firms' financing choices and that conflicts of interests between firm claimholders may help explain the level and cross-sectional variation of observed leverage ratios. The second essay focuses on the practical relevance of agency conflicts. De- spite the theoretical development of the literature on agency conflicts and firm policy choices, the magnitude of manager-shareholder conflicts is still an open question. This essay proposes a methodology for quantifying these agency conflicts. To do so, it examines the impact of managerial entrenchment on corporate financing decisions. It builds a dynamic contingent claims model in which managers do not act in the best interest of shareholders, but rather pursue private benefits at the expense of shareholders. Managers have discretion over financing and dividend policies. However, shareholders can remove the manager at a cost. The analysis demonstrates that entrenched managers restructure less frequently and issue less debt than optimal for shareholders. I take the model to the data and use observed financing choices to provide firm-specific estimates of the degree of managerial entrenchment. Using structural econometrics, I find costs of control challenges of 2-7% on average (.8-5% at median). The estimates of the agency costs vary with variables that one expects to determine managerial incentives. In addition, these costs are sufficient to resolve the low- and zero-leverage puzzles and explain the time series of observed leverage ratios. Finally, the analysis shows that governance mechanisms significantly affect the value of control and firms' financing decisions. The third essay is concerned with the documented time trend in corporate cash holdings by Bates, Kahle and Stulz (BKS,2003). BKS find that firms' cash holdings double from 10% to 20% over the 1980 to 2005 period. This essay provides an explanation of this phenomenon by examining the effects of product market competition on firms' cash holdings in the presence of financial constraints. It develops a real options model in which cash holdings may be used to cover unexpected operating losses and avoid inefficient closure. The model generates new predictions relating cash holdings to firm and industry characteristics such as the intensity of competition, cash flow volatility, or financing constraints. The empirical examination of the model shows strong support of model's predictions. In addition, it shows that the time trend in cash holdings documented by BKS can be at least partly attributed to a competition effect.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.