ERN: Nonparametric Methods (Topic)最新文献_第2页

Index Option Returns and Generalized Entropy Bounds 索引选项返回和广义熵界

ERN: Nonparametric Methods (Topic) Pub Date : 2019-09-04 DOI: 10.2139/ssrn.2149265

Yan Liu

引用次数: 3

Bayesian Nonparametric Clustering as a Community Detection Problem 作为社区检测问题的贝叶斯非参数聚类

ERN: Nonparametric Methods (Topic) Pub Date : 2019-07-16 DOI: 10.2139/ssrn.3424529

S. Tonellato

{"title":"Bayesian Nonparametric Clustering as a Community Detection Problem","authors":"S. Tonellato","doi":"10.2139/ssrn.3424529","DOIUrl":"https://doi.org/10.2139/ssrn.3424529","url":null,"abstract":"It is well known that a wide class of bayesian nonparametric priors lead to the representation of the distribution of the observable variables as a mixture density with an infinite number of components, and that such a representation induces a clustering structure in the observations. However, cluster identification is not straightforward a posteriori and some post-processing is usually required. In order to circumvent label switching, pairwise posterior similarity has been introduced, and it has been used in order to either apply classical clustering algorithms or estimate the underlying partition by minimising a suitable loss function. This paper proposes to map observations on a weighted undirected graph, where each node represents a sample item and edge weights are given by the posterior pairwise similarities. It will be shown how, after building a particular random walk on such a graph, it is possible to apply a community detection algorithm, known as map equation method, by optimising the description length of the partition. A relevant feature of this method is that it allows for both the quantification of the posterior uncertainty of the classification and the selection of variables to be used for classification purposes.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84754578","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: 2

Bayesian Nonparametric Graphical Models for Time-Varying Parameters VAR 时变参数VAR的贝叶斯非参数图模型

ERN: Nonparametric Methods (Topic) Pub Date : 2019-06-03 DOI: 10.2139/ssrn.3400078

Matteo Iacopini, L. Rossini

{"title":"Bayesian Nonparametric Graphical Models for Time-Varying Parameters VAR","authors":"Matteo Iacopini, L. Rossini","doi":"10.2139/ssrn.3400078","DOIUrl":"https://doi.org/10.2139/ssrn.3400078","url":null,"abstract":"Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between the variables of interest. Recently, Bayesian nonparametric techniques have become popular for modelling complex phenomena in a flexible and efficient manner, but only few attempts have been made in econometrics. In this paper, we provide an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. We include a Bayesian nonparametric dependent prior specification on the matrix of coefficients and the covariance matrix by mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio et al. (2019), our hierarchical prior overcomes over-parametrization and over-fitting issues by clustering the vector autoregressive (VAR) coefficients into groups and by shrinking the coefficients of each group toward a common location. Our BNP timevarying VAR model is based on a spike-and-slab construction coupled with dependent Dirichlet Process prior (DPP) and allows to: (i) infer time-varying Granger causality networks from time series; (ii) flexibly model and cluster non-zero time-varying coefficients; (iii) accommodate for potential non-linearities. In order to assess the performance of the model, we study the merits of our approach by considering a well-known macroeconomic dataset. Moreover, we check the robustness of the method by comparing two alternative specifications, with Dirac and diffuse spike prior distributions.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73577270","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: 1

(Non)-Parametric Regressions: Applications to Local Stochastic Volatility Models (非)参数回归:局部随机波动模型的应用

ERN: Nonparametric Methods (Topic) Pub Date : 2019-04-19 DOI: 10.2139/ssrn.3374875

P. Henry-Labordère

引用次数: 1

Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series 非线性动态经济时间序列的置换熵与信息恢复

ERN: Nonparametric Methods (Topic) Pub Date : 2019-03-12 DOI: 10.3390/ECONOMETRICS7010010

Miguel Henry, G. Judge

引用次数: 38

Estimation of a Nonparametric model for Bond Prices from Cross-section and Time series Information 从横截面和时间序列信息估计债券价格的非参数模型

ERN: Nonparametric Methods (Topic) Pub Date : 2019-02-25 DOI: 10.2139/ssrn.3341344

B. Koo, D. La Vecchia, O. Linton

引用次数: 3

Nonseparability Without Monotonicity: The Couterfactual Distribution Estimator for Causal Inference 非单调不可分性:因果推理的反事实分布估计量

ERN: Nonparametric Methods (Topic) Pub Date : 2019-02-13 DOI: 10.2139/ssrn.3343438

Nir Billfeld, Moshe Kim

{"title":"Nonseparability Without Monotonicity: The Couterfactual Distribution Estimator for Causal Inference","authors":"Nir Billfeld, Moshe Kim","doi":"10.2139/ssrn.3343438","DOIUrl":"https://doi.org/10.2139/ssrn.3343438","url":null,"abstract":"Nonparametric identiﬁcation strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature. This is important as when monotonicity is applied to the instrumental variables it limits their availability and when applied to the unobservables it can hardly be justiﬁed in the non-scalar case. Moreover, in cases where monotonicity is not satisﬁed the monotonicity-based estimators might be severely biased as shown in comparative Monte Carlo simulation. The key idea in the proposed identiﬁcation and estimation strategy is to uncover the counterfactual distribution of the dependent variable, which is not directly observed in the data. We oﬀer a two-step M-Estimator based on a resolution-dependent reproducing symmetric kernel density estimator rather than on the bandwidth-dependent classical kernel and thus, less sensitive to bandwidth choice. Additionally, the average marginal eﬀect of the endogenous covariate on the outcome variable is identiﬁed directly from the noisy data which precludes the need to employ additional estimation steps thereby avoiding potential error accumulation. Asymptotic properties of the counterfactual M-Estimator are established.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88681655","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: 0

Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach 高采样频率下局部分布的推断:一种自举方法

ERN: Nonparametric Methods (Topic) Pub Date : 2018-10-11 DOI: 10.2139/ssrn.3285701

Ulrich Hounyo, R. T. Varneskov

{"title":"Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach","authors":"Ulrich Hounyo, R. T. Varneskov","doi":"10.2139/ssrn.3285701","DOIUrl":"https://doi.org/10.2139/ssrn.3285701","url":null,"abstract":"Abstract We study inference for the local innovations of Ito semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the effect of “large” jumps. Our locally dependent wild bootstrap (LDWB) accommodate issues related to the stochastic scale and jumps as well as account for a special block-wise dependence structure induced by sampling errors. We show that the LDWB replicates first and second-order limit theory from the usual empirical process and the stochastic scale estimate, respectively, in addition to an asymptotic bias. Moreover, we design the LDWB sufficiently general to establish asymptotic equivalence between it and a nonparametric local block bootstrap, also introduced here, up to second-order distribution theory. Finally, we introduce LDWB-aided Kolmogorov–Smirnov tests for local Gaussianity as well as local von-Mises statistics, with and without bootstrap inference, and establish their asymptotic validity using the second-order distribution theory. The finite sample performance of CLT and LDWB-aided local Gaussianity tests is assessed in a simulation study and an empirical application. Whereas the CLT test is oversized, even in large samples, the size of the LDWB tests is accurate, even in small samples. The empirical analysis verifies this pattern, in addition to providing new insights about the fine scale distributional properties of innovations to equity indices, commodities and exchange rates.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91406993","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: 11

Nonparametric Estimation in Panel Data Models with Heterogeneity and Time-Varyingness 异质性和时变面板数据模型的非参数估计

ERN: Nonparametric Methods (Topic) Pub Date : 2018-07-15 DOI: 10.2139/ssrn.3214046

Fei Liu, Jiti Gao, Yanrong Yang

{"title":"Nonparametric Estimation in Panel Data Models with Heterogeneity and Time-Varyingness","authors":"Fei Liu, Jiti Gao, Yanrong Yang","doi":"10.2139/ssrn.3214046","DOIUrl":"https://doi.org/10.2139/ssrn.3214046","url":null,"abstract":"Panel data subject to heterogeneity in both cross-sectional and time-serial directions are commonly encountered across social and scientific fields. To address this problem, we propose a class of time-varying panel data models with individual-specific regression coefficients and interactive common factors. This results in a model capable of describing heterogeneous panel data in terms of time-varyingness in the time-serial direction and individual-specific coefficients among crosssections. Another striking generality of this proposed model relies on its compatibility with endogeneity in the sense of interactive common factors. Model estimation is achieved through a novel duple least-squares (DLS) iteration algorithm, which implements two least-squares estimation recursively. Its unified ability in estimation is nicely illustrated according to flexible applications on various cases with exogenous or endogenous common factors. Established asymptotic theory for DLS estimators benefits practitioners by demonstrating effectiveness of iteration in eliminating estimation bias gradually along with iterative steps. We further show that our model and estimation perform well on simulated data in various scenarios as well as an OECD healthcare expenditure dataset. The time-variation and heterogeneity among cross-sections are confirmed by our analysis.","PeriodicalId":11744,"journal":{"name":"ERN: Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82334648","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: 3

Measuring Treatment Effects with Big N Panels 用大N板测量处理效果

ERN: Nonparametric Methods (Topic) Pub Date : 2018-06-29 DOI: 10.2139/ssrn.3205224

C. Adams

引用次数: 1