Сравнение точности оценок параметрических и полупараметрических методов коррекции многомерного смещения отбора
The paper presents the structural model of decision-making process on the residential mortgage market. We empirically estimates key drivers of mortgage borrowing, underwriting, and default process by jointly using market-level monthly data and loan-level data from regional branch of Agency of Home Mortgage Lending (AHML). The multistep estimation procedure allows correcting for sample selection bias and endogeneity and provides consistent parameter estimates. Obtained results shows that risk preferences are changing during the time and AHML borrowers are relatively high risky.
Most of existing scoring systems are based on binary choice models with sample selection. This setting does not allow for up-to-date information about loans to be used and a lot of observations becomes lost. In the paper a model of binary choice with sample selection is extended to the case of many periods. This extension allows for defaults to be modeled for each period that solves the problem of lost observations. This setting also can be used to estimate the effectiveness of existing scoring system of a bank. The model is estimated using data granted by one of commercial banks of Nizhny Novgorod. Sample consists of observations from January 2009 to March 2012.
This conference proceeding includes selected full papers from the 11th EBES Conference – Ekaterinburg. We have accepted papers among resubmitted full papers after the conference ended. In this proceeding you will find a snapshot of topics that are presented in the conference. As expected, our conference has been an intellectual hub for academic discussion for our colleagues in the areas of economics, finance, and business. Participants found an excellent opportunity for presenting new research, exchanging information and discussing current issues. We believe that this conference proceeding and our future conferences will improve further the development of knowledge in our fields.
We consider identification of nonparametric simultaneous equations model with the presence of sample selection. For the proposed model we introduce neccessary conditions for its identification if excluded variables for selection and outcome equations are available. Our approach extends well known class of nonparametric two-step identification procedures for the case of non-triangular simultaneous equations.
This paper proposes a new method for estimating the effect of education on an employee’s wage: with the help of the generalized Heckman model with switching. Application of this method makes it possible to avoid the selection bias due to the endogenous accounting for nonrandom consideration of individuals both as employed and having higher education. This model makes it possible to estimate whether it is worth- while for an individual to get a higher education in terms of changes in their expected income. Analysis of the distribution of the effect of the education level on wages among employees makes it possible to evaluate the efficiency of the higher education system in providing the population with skills and competencies that are significant in the labor market.
The mortgage crisis that started in the U.S. in 2007 and lasted until 2009 was characterized by an unusually large number of defaults on the subprime mortgage market. As a result, it developed into a global economic recession and placed the stability of the world banking system in jeopardy. Therefore, the issues of credit risk modeling showed the shortcomings of the current credit risk practice. Truncation, or partial observability, and simultaneous equations bias causes sample selection bias. As a result, parameter estimates are biased and inconsistent. Firstly, we provide an overview of current approaches in the mortgage literature to control for the sample selection bias correction, such as the Heckman model and bivariate probit model with selection. Secondly, a review of the most significant mortgage studies discussing this problem is introduced. Specifically, different structural models, specific datasets and empirical results are regarded. In addition, we discuss such key credit risk determinants as borrower characteristics, terms of the mortgage contract, mortgage characteristics, and macroeconomic conditions. Finally, we conclude the discussion with possible research questions.
This paper analyzes the problems of credit risk modeling on the Russian residential mortgage market. The structural model of the credit risk evaluation, which controls for the sample selection bias and endogeneity, is presented. It estimates based on the regional mortgage data. Obtained results can be used to develop the effective risk management systems in credit organizations.
We consider certain spaces of functions on the circle, which naturally appear in harmonic analysis, and superposition operators on these spaces. We study the following question: which functions have the property that each their superposition with a homeomorphism of the circle belongs to a given space? We also study the multidimensional case.
We consider the spaces of functions on the m-dimensional torus, whose Fourier transform is p -summable. We obtain estimates for the norms of the exponential functions deformed by a C1 -smooth phase. The results generalize to the multidimensional case the one-dimensional results obtained by the author earlier in “Quantitative estimates in the Beurling—Helson theorem”, Sbornik: Mathematics, 201:12 (2010), 1811 – 1836.
We consider the spaces of function on the circle whose Fourier transform is p-summable. We obtain estimates for the norms of exponential functions deformed by a C1 -smooth phase.