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Author: Teresa Corzo
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Book Description
We present and estimate, using nonparametric regression techniques, a model of short term interest rate dynamics and use the estimation to value bonds. We study the case of two European countries - Spain and Italy - that belong to EMU, and compare the resulting bond prices of a one factor model with that of a two factor, the second factor being a stochastic mean. The pricing errors for both models are 34% smaller than those reported on the parametric literature. Furthermore, the two factor model, which takes into account the convergence with Europe of the domestic economies, obtains better results than the one factor model. Our findings give strong support to the importance of a correct specification of the volatility of interest rates.
Author: Teresa Corzo
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Book Description
We present and estimate, using nonparametric regression techniques, a model of short term interest rate dynamics and use the estimation to value bonds. We study the case of two European countries - Spain and Italy - that belong to EMU, and compare the resulting bond prices of a one factor model with that of a two factor, the second factor being a stochastic mean. The pricing errors for both models are 34% smaller than those reported on the parametric literature. Furthermore, the two factor model, which takes into account the convergence with Europe of the domestic economies, obtains better results than the one factor model. Our findings give strong support to the importance of a correct specification of the volatility of interest rates.
Author: Chris Downing
Publisher:
ISBN:
Category : Interest rates
Languages : en
Pages : 60
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Book Description
Author: G.G Roussas
Publisher: Springer Science & Business Media
ISBN: 9401132224
Category : Mathematics
Languages : en
Pages : 691
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Book Description
About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.
Author: Aman Ullah
Publisher: Springer Science & Business Media
ISBN: 3642518486
Category : Business & Economics
Languages : en
Pages : 180
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Book Description
Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).
Author: Denis Bosq
Publisher: Springer Science & Business Media
ISBN: 146840489X
Category : Mathematics
Languages : en
Pages : 181
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Book Description
This book provides a mathematically rigorous treatment of the theory of nonparametric estimation and prediction for stochastic processes. It discusses discrete time and continuous time, and the emphasis is on the kernel methods. Several new results are presented concerning optimal and superoptimal convergence rates. How to implement the method is discussed in detail and several numerical results are presented. This book will be of interest to specialists in mathematical statistics and to those who wish to apply these methods to practical problems involving time series analysis.
Author: Masayuki Hirukawa
Publisher: Springer
ISBN: 9811054665
Category : Business & Economics
Languages : en
Pages : 117
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Book Description
This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics addressed include the bias-variance tradeoff, smoothing parameter choices, achieving rate improvements with bias reduction techniques, and estimation with weakly dependent data. Further, the large- and finite-sample properties of estimators and test statistics smoothed by asymmetric kernels are compared with those smoothed by symmetric kernels. Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data. Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic and financial data are nonnegative and violate the presumed conditions of conventional methods. Examples include incomes, wages, short-term interest rates, and insurance claims. Such observations are often concentrated near the boundary and have long tails with sparse data. Smoothing with asymmetric kernel functions has increasingly gained attention, because the approach successfully addresses the issues arising from distributions that have natural boundaries at the origin and heavy positive skewness. Offering an overview of recently developed kernel methods, complemented by intuitive explanations and mathematical proofs, this book is highly recommended to all readers seeking an in-depth and up-to-date guide to nonparametric estimation methods employing asymmetric kernel smoothing.
Author: Randall L. Eubank
Publisher:
ISBN:
Category : Convergence
Languages : en
Pages : 30
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Book Description
Rates of convergence are studied for estimation in certain partial linear models that include nonparametric regression models with discontinuous derivatives. The asymptotic behavior of two smoothing spline related estimators of the regression coefficient and regression function in these models are examined. Lower bounds are then derived for rates of convergence in estimating the size of jump discontinuities in a regression function or its derivative. The latter rates are nonparametric which indicates that parametric convergence rates are not possible in such instances.
Author: Joel L. Horowitz
Publisher: Springer Science & Business Media
ISBN: 0387928707
Category : Business & Economics
Languages : en
Pages : 278
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Book Description
Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
Author: P. Groeneboom
Publisher: Birkhäuser
ISBN: 3034886217
Category : Mathematics
Languages : en
Pages : 129
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Book Description
This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several models are introduced, but not pursued in any detail; many problems remain. Part II, based on Piet Groeneboom's lectures, focuses on non parametric maximum likelihood estimates (NPMLE's) for certain inverse problems. The first chapter deals with the interval censoring problem.
Author: Jiti Gao
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
