ERN: Semiparametric & Nonparametric Methods (Topic)最新文献_第6页

Kernel-Smoothed Conditional Quantiles of Correlated Bivariate Discrete Data 相关二元离散数据的核平滑条件分位数

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2011-01-17 DOI: 10.2139/ssrn.1742230

J. De Gooijer, A. Yuan

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引用次数: 0

Quality of Match for Statistical Matches Used in the 1992 and 2007 Limew Estimates for the United States 1992年和2007年美国Limew估计中使用的统计匹配的匹配质量

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2010-09-21 DOI: 10.2139/ssrn.1680409

Thomas Masterson

引用次数: 9

On Marginal Effects in Semiparametric Censored Regression Models 半参数截尾回归模型的边际效应

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2008-09-01 DOI: 10.2139/ssrn.1394384

Bo E. Honoré

引用次数: 32

VAR Modeling for Dynamic Loadings Driving Volatility Strings 动态负载驱动波动串的VAR建模

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2008-07-01 DOI: 10.1093/JJFINEC/NBN004

R. Brüggemann, W. Härdle, Julius Mungo, Carsten Trenkler

引用次数: 20

Bayesian Semiparametric Stochastic Volatility Modeling 贝叶斯半参数随机波动模型

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2008-06-01 DOI: 10.2139/ssrn.1151239

Mark J. Jensen, J. Maheu

引用次数: 126

Inverse Probability Tilting for Moment Condition Models with Missing Data 缺失数据时刻条件模型的逆概率倾斜

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2008-05-01 DOI: 10.1093/RESTUD/RDR047

Daniel Egel, B. Graham, Cristine Campos de Xavier Pinto

引用次数: 172

Kernel Convergence Estimates for Diffusions with Continuous Coefficients 连续系数扩散的核收敛估计

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2007-11-01 DOI: 10.2139/ssrn.1026612

C. Albanese

{"title":"Kernel Convergence Estimates for Diffusions with Continuous Coefficients","authors":"C. Albanese","doi":"10.2139/ssrn.1026612","DOIUrl":"https://doi.org/10.2139/ssrn.1026612","url":null,"abstract":"Bidirectional valuation models are based on numerical methods to obtain kernels of parabolic equations. Here we address the problem of robustness of kernel calculations vis a vis floating point errors from a theoretical standpoint. We are interested in kernels of one-dimensional diffusion equations with continuous coefficients as evaluated by means of explicit discretization schemes of uniform step h > 0 in the limit as h → 0. We consider both semidiscrete triangulations with continuous time and explicit Euler schemes with time step so small that the Courant condition is satisfied. We find uniform bounds for the convergence rate as a function of the degree of smoothness. We conjecture these bounds are indeed sharp. The bounds also apply to the time derivatives of the kernel and its first two space derivatives. The proof is constructive and is based on a new technique of path conditioning for Markov chains and a renormalization group argument. We make the simplifying assumption of time-independence and use longitudinal Fourier transforms in the time direction. Convergence rates depend on the degree of smoothness and Holder differentiability of the coefficients. We find that the fastest convergence rate is of order O(h2) and is achieved if the coefficients have a bounded second derivative. Otherwise, explicit schemes still converge for any degree of Holder differentiability except that the convergence rate is slower. Holder continuity itself is not strictly necessary and can be relaxed by an hypothesis of uniform continuity.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114420959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

News - Good or Bad - and its Impact Over Multiple Horizons 新闻——好或坏——及其在多个领域的影响

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2007-07-03 DOI: 10.2139/ssrn.998209

Xilong Chen, Eric Ghysels

{"title":"News - Good or Bad - and its Impact Over Multiple Horizons","authors":"Xilong Chen, Eric Ghysels","doi":"10.2139/ssrn.998209","DOIUrl":"https://doi.org/10.2139/ssrn.998209","url":null,"abstract":"It is difficult to define news, and many definitions are model-based since part of what is announced is anticipated. Therefore, news is typically defined as a residual within the context of some type of prediction model, and the prediction model locks in the sampling frequency that is the reference time scale for analyzing propagation mechanisms. We try to accomplish two goals: (1) characterize news as much as possible as a model-free observation, and (2) measure the impact of news over any arbitrary horizon of interest. We revisit the concept of news impact curves introduced by Engle and Ng (1993), in the current high frequency data environment of financial market time series. Instead of taking a single horizon fixed parametric specification, we recast many of the original ideas in a very flexible multi-horizon semi-parametric setting. Technically speaking we introduce semi-parametric MIDAS regressions and study their asymptotic properties. The analysis relates to and extends recent work by Linton and Mammen (2005). In addition we also introduce various new parametric models. We find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries we find have profound implications for current volatility prediction models that are based on in-sample asymptotic analysis developed over recent years. In this context we discuss the link between diffusions and news impact curves.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123428577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Estimation and Inference by the Method of Projection Minimum Distance 投影最小距离法的估计与推断

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2007-07-01 DOI: 10.2139/ssrn.1001957

Ò. Jordà, S. Kozicki

引用次数: 19

KRLS: A Stata Package for Kernel-Based Regularized Least Squares KRLS:一个基于核的正则化最小二乘的Stata包

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 1900-01-01 DOI: 10.2139/ssrn.2325523

Jeremy Ferwerda, Jens Hainmueller, C. Hazlett

引用次数: 14