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

tvReg: Time-varying Coefficient Linear Regression for Single and Multi-Equations in R 基于时变系数线性回归的单方程和多方程

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2019-04-01 DOI: 10.2139/ssrn.3363526

Isabel Casas, R. Fernández-Casal

引用次数: 19

Appendix to 'Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building' 附录“交易指标表现稳健吗?”来自半参数情景构建的证据

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2019-01-02 DOI: 10.2139/ssrn.3246671

Andrea Thomann

{"title":"Appendix to 'Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building'","authors":"Andrea Thomann","doi":"10.2139/ssrn.3246671","DOIUrl":"https://doi.org/10.2139/ssrn.3246671","url":null,"abstract":"This is the online Appendix to \"Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building\" We provide additional empirical results from other trading indicators. Abstract of \"Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building\" This paper challenges widely applied trading indicators in their ability to generate robust performance. In this study we use a semi-parametric scenario building approach to simulate artificial price series based on the characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for pro forma out-of-sample testing, our price simulations provide a back-testing environment to test trading strategies on artificially created prices. This provides an additional performance assessment by allowing to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics as the observed price series. We find that many trading indicators deliver robust results for certain performance metrics, however, are unable to deliver robust results and improvements across all reported performance metrics. On top, most trading strategies influence the higher order moments of the return distribution; while they improve the skewness—thereby increasing the number of positive returns—in most cases they also increase the kurtosis, introducing undesired additional observations in the tail of the return distributions.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132736745","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

High Dimensional Semiparametric Moment Restriction Models 高维半参数矩约束模型

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-11-22 DOI: 10.2139/ssrn.3045063

Chaohua Dong, Jiti Gao, O. Linton

引用次数: 6

Semiparametric Maximum Likelihood Sieve Estimator for Correction of Endogenous Truncation Bias 修正内生截断偏差的半参数最大似然筛估计

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-11-18 DOI: 10.2139/ssrn.3286553

Nir Billfeld, Moshe Kim

{"title":"Semiparametric Maximum Likelihood Sieve Estimator for Correction of Endogenous Truncation Bias","authors":"Nir Billfeld, Moshe Kim","doi":"10.2139/ssrn.3286553","DOIUrl":"https://doi.org/10.2139/ssrn.3286553","url":null,"abstract":"Semiparametric correction for a sample selection bias in the presence of endogenous truncation is known to be much more difficult in the case of a binary selection variable than in the case of a continuous selection variable. This paper proposes a simple bandwidth-free semiparametric methodology to correct for a self-selection bias in a truncated sample, without any prior knowledge of the marginal density functions of the selection model’s random disturbances. Each of the unknown marginal density functions is estimated using Sieve estimator, utilizing Hermite polynomials as basis functions. The aforementioned procedure is appropriate for both binary and continuous selection variables cases under the covariate shift assumption. We consider a double hurdle model, which is a combination of two selection rules. The ﬁrst is propagated by a truncation in the dependent variable of the substantive equation. The second is propagated by endogenous self-selection. The suggested correction procedure produces estimates that are of high accuracy and consistent based on Monte Carlo simulations. The random disturbances are not restricted to being symmetric and their marginal distribution functions are unknown. Thus, in the data generation process we verify the applicability of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed. These disturbances are constructed as realizations of non-symmetric distribution functions.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114866471","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

A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models 半参数模型的一致异方差稳健LM型规格检验

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-10-17 DOI: 10.2139/ssrn.3458412

I. Korolev

引用次数: 2

Semiparametric Identification in Panel Data Discrete Response Models 面板数据离散响应模型的半参数辨识

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-08-17 DOI: 10.2139/ssrn.3420016

E. Aristodemou

引用次数: 19

Semiparametric Testing With Highly Persistent Predictors 具有高度持续性预测因子的半参数检验

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-07-13 DOI: 10.2139/ssrn.3213484

B. Werker, Bo Zhou

引用次数: 4

Varying-Coefficient Panel Data Models with Partially Observed Factor Structure 部分观测因子结构的变系数面板数据模型

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2018-01-15 DOI: 10.2139/ssrn.3102631

Chaohua Dong, Jiti Gao, B. Peng

引用次数: 4

On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator scad惩罚经验似然估计的收敛速度

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2017-11-29 DOI: 10.2139/ssrn.3079386

T. Ando, N. Sueishi

引用次数: 3

Semiparametric Quantile Averaging in the Presence of High-Dimensional Predictors 高维预测因子存在下的半参数分位数平均

ERN: Semiparametric & Nonparametric Methods (Topic) Pub Date : 2017-10-04 DOI: 10.2139/ssrn.3057523

J. De Gooijer, D. Zerom

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引用次数: 4