The source code of the package tvReg is publicly available for download from the Comprehensive R Archive Network. The five basic functions in this package are the tvLM, tvAR, tvSURE, tvPLM, tvVAR and tvIRF, which cover a large range of semiparametric models with time-varying coefficients. Moreover, this package provides methods for the graphical display of results, extraction of the residuals and fitted values, bandwidth selection, nonparametric estimation of the time-varying variance-covariance matrix of the error term, and four estimation procedures: the time-varying ordinary least squares implemented in the tvOLS, the time-varying generalised least squares in the tvGLS, the time-varying random effects in the tvRE and the time-varying fixed effects in the tvFE method. Applications to risk management, portfolio management, asset management and monetary policy are used as examples of these functions usage.
{"title":"tvReg: Time-varying Coefficient Linear Regression for Single and Multi-Equations in R","authors":"Isabel Casas, R. Fernández-Casal","doi":"10.2139/ssrn.3363526","DOIUrl":"https://doi.org/10.2139/ssrn.3363526","url":null,"abstract":"The source code of the package tvReg is publicly available for download from the Comprehensive R Archive Network. The five basic functions in this package are the tvLM, tvAR, tvSURE, tvPLM, tvVAR and tvIRF, which cover a large range of semiparametric models with time-varying coefficients. Moreover, this package provides methods for the graphical display of results, extraction of the residuals and fitted values, bandwidth selection, nonparametric estimation of the time-varying variance-covariance matrix of the error term, and four estimation procedures: the time-varying ordinary least squares implemented in the tvOLS, the time-varying generalised least squares in the tvGLS, the time-varying random effects in the tvRE and the time-varying fixed effects in the tvFE method. Applications to risk management, portfolio management, asset management and monetary policy are used as examples of these functions usage.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122772485","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}
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.
{"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\"<br><br>We provide additional empirical results from other trading indicators.<br><br>Abstract of \"Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building\"<br><br>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}
We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved. We propose an estimation method based on the sieve generalized method of moments (sieve-GMM). We establish consistency and asymptotic normality for the estimated quantities when the number of parameters increases modestly with sample size. We also consider the case where the number of potential parameters/covariates is very large, i.e., increases rapidly with sample size, but the true model exhibits sparsity. We use a penalized sieve GMM approach to select the relevant variables, and establish the oracle property of our method in this case. We also provide new results for inference. We propose several new test statistics for the over-identification and establish their large sample properties. We provide a simulation study and an application to data from the NLSY79 used by Carneiro et al. [14].
{"title":"High Dimensional Semiparametric Moment Restriction Models","authors":"Chaohua Dong, Jiti Gao, O. Linton","doi":"10.2139/ssrn.3045063","DOIUrl":"https://doi.org/10.2139/ssrn.3045063","url":null,"abstract":"We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved. We propose an estimation method based on the sieve generalized method of moments (sieve-GMM). We establish consistency and asymptotic normality for the estimated quantities when the number of parameters increases modestly with sample size. We also consider the case where the number of potential parameters/covariates is very large, i.e., increases rapidly with sample size, but the true model exhibits sparsity. We use a penalized sieve GMM approach to select the relevant variables, and establish the oracle property of our method in this case. We also provide new results for inference. We propose several new test statistics for the over-identification and establish their large sample properties. We provide a simulation study and an application to data from the NLSY79 used by Carneiro et al. [14].","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133627905","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}
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.
{"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}
This paper develops a consistent heteroskedasticity robust Lagrange Multiplier (LM) type specification test for semiparametric conditional mean models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The proposed test statistic is straightforward to compute and is asymptotically standard normal under the null. Compared with the earlier literature on series-based specification tests in parametric models, I rely on the projection property of series estimators and derive a different normalization of the test statistic. Compared with the recent test in Gupta (2018), I use a different way of accounting for heteroskedasticity. I demonstrate using Monte Carlo studies that my test has superior finite sample performance compared with the existing tests. I apply the test to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.
{"title":"A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models","authors":"I. Korolev","doi":"10.2139/ssrn.3458412","DOIUrl":"https://doi.org/10.2139/ssrn.3458412","url":null,"abstract":"This paper develops a consistent heteroskedasticity robust Lagrange Multiplier (LM) type specification test for semiparametric conditional mean models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The proposed test statistic is straightforward to compute and is asymptotically standard normal under the null. Compared with the earlier literature on series-based specification tests in parametric models, I rely on the projection property of series estimators and derive a different normalization of the test statistic. Compared with the recent test in Gupta (2018), I use a different way of accounting for heteroskedasticity. I demonstrate using Monte Carlo studies that my test has superior finite sample performance compared with the existing tests. I apply the test to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124345589","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}
This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables, point-identification fails but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identified set changes as the support of the explanatory variables varies.
{"title":"Semiparametric Identification in Panel Data Discrete Response Models","authors":"E. Aristodemou","doi":"10.2139/ssrn.3420016","DOIUrl":"https://doi.org/10.2139/ssrn.3420016","url":null,"abstract":"This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables, point-identification fails but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identified set changes as the support of the explanatory variables varies.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128426700","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}
We address the issue of semiparametric efficiency in the bivariate regression problem with a highly persistent predictor, where the joint distribution of the innovations is regarded an infinite-dimensional nuisance parameter. Using a structural representation of the limit experiment and exploiting invariance relationships therein, we construct invariant point-optimal tests for the regression coefficient of interest. This approach naturally leads to a family of feasible tests based on the component-wise ranks of the innovations that can gain considerable power relative to existing tests under non-Gaussian innovation distributions, while behaving equivalently under Gaussianity. When an i.i.d. assumption on the innovations is appropriate for the data at hand, our tests exploit the efficiency gains possible. Moreover, we show by simulation that our test remains well behaved under some forms of conditional heteroskedasticity.
{"title":"Semiparametric Testing With Highly Persistent Predictors","authors":"B. Werker, Bo Zhou","doi":"10.2139/ssrn.3213484","DOIUrl":"https://doi.org/10.2139/ssrn.3213484","url":null,"abstract":"We address the issue of semiparametric efficiency in the bivariate regression problem with a highly persistent predictor, where the joint distribution of the innovations is regarded an infinite-dimensional nuisance parameter. Using a structural representation of the limit experiment and exploiting invariance relationships therein, we construct invariant point-optimal tests for the regression coefficient of interest. This approach naturally leads to a family of feasible tests based on the component-wise ranks of the innovations that can gain considerable power relative to existing tests under non-Gaussian innovation distributions, while behaving equivalently under Gaussianity. When an i.i.d. assumption on the innovations is appropriate for the data at hand, our tests exploit the efficiency gains possible. Moreover, we show by simulation that our test remains well behaved under some forms of conditional heteroskedasticity.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134286768","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}
In this paper, we study a varying-coefficient panel data model with nonstationarity, wherein a factor structure is adopted to capture different effects of time invariant variables over time. The methodology employed in this paper fills a gap of dealing with the mixed I(1)/I(0) regressors and factors in the literature. For comparison purposes, we consider the scenarios where the factors are either observable or unobservable, respectively. We propose an estimation method for both the unknown coefficient functions involved and the unknown factors before we establish the corresponding theory. We then evaluate the finite-sample performance of the proposed estimation theory through extensive Monte Carlo simulations. In an empirical study, we use our newly proposed model and method to study the returns to scale of large commercial banks in the U.S.. Some overlooked modelling issues in the literature of production econometrics are addressed.
{"title":"Varying-Coefficient Panel Data Models with Partially Observed Factor Structure","authors":"Chaohua Dong, Jiti Gao, B. Peng","doi":"10.2139/ssrn.3102631","DOIUrl":"https://doi.org/10.2139/ssrn.3102631","url":null,"abstract":"In this paper, we study a varying-coefficient panel data model with nonstationarity, wherein a factor structure is adopted to capture different effects of time invariant variables over time. The methodology employed in this paper fills a gap of dealing with the mixed I(1)/I(0) regressors and factors in the literature. For comparison purposes, we consider the scenarios where the factors are either observable or unobservable, respectively. We propose an estimation method for both the unknown coefficient functions involved and the unknown factors before we establish the corresponding theory. We then evaluate the finite-sample performance of the proposed estimation theory through extensive Monte Carlo simulations. In an empirical study, we use our newly proposed model and method to study the returns to scale of large commercial banks in the U.S.. Some overlooked modelling issues in the literature of production econometrics are addressed.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122411398","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}
This paper investigates the asymptotic properties of a penalized empirical likelihood estimator for moment restriction models when the number of parameters ( p n ) and/or the number of moment restrictions increases with the sample size. Our main result is that the SCAD-penalized empirical likelihood estimator is n / p n -consistent under a reasonable condition on the regularization parameter. Our consistency rate is better than the existing ones. This paper also provides sufficient conditions under which n / p n -consistency and an oracle property are satisfied simultaneously. As far as we know, this paper is the first to specify sufficient conditions for both n / p n -consistency and the oracle property of the penalized empirical likelihood estimator.
本文研究了矩约束模型在参数数目(p n)和/或矩约束数目随样本量增加时的惩罚经验似然估计的渐近性质。我们的主要结果是，scad惩罚的经验似然估计量在正则化参数的合理条件下是n / p n一致的。我们的一致性比现有的好。本文还给出了同时满足n / p n -一致性和一个oracle性质的充分条件。据我们所知，本文首次给出了惩罚经验似然估计的n / p n -一致性和预言性的充分条件。
{"title":"On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator","authors":"T. Ando, N. Sueishi","doi":"10.2139/ssrn.3079386","DOIUrl":"https://doi.org/10.2139/ssrn.3079386","url":null,"abstract":"This paper investigates the asymptotic properties of a penalized empirical likelihood estimator for moment restriction models when the number of parameters ( p n ) and/or the number of moment restrictions increases with the sample size. Our main result is that the SCAD-penalized empirical likelihood estimator is n / p n -consistent under a reasonable condition on the regularization parameter. Our consistency rate is better than the existing ones. This paper also provides sufficient conditions under which n / p n -consistency and an oracle property are satisfied simultaneously. As far as we know, this paper is the first to specify sufficient conditions for both n / p n -consistency and the oracle property of the penalized empirical likelihood estimator.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116025471","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}
Abstract The paper proposes a method for forecasting conditional quantiles. In practice, one often does not know the “true” structure of the underlying conditional quantile function, and in addition, we may have a large number of predictors. Focusing on such cases, we introduce a flexible and practical framework based on penalized high-dimensional quantile averaging. In addition to prediction, we show that the proposed method can also serve as a predictor selector. We conduct extensive simulation experiments to asses its prediction and variable selection performances for nonlinear and linear time series model designs. In terms of predictor selection, the approach tends to select the true set of predictors with minimal false positives. With respect to prediction accuracy, the method competes well even with the benchmark/oracle methods that know one or more aspects of the underlying quantile regression model. We further illustrate the merit of the proposed method by providing an application to the out-of-sample forecasting of U.S. core inflation using a large set of monthly macroeconomic variables based on FRED-MD database. The application offers several empirical findings.
{"title":"Semiparametric Quantile Averaging in the Presence of High-Dimensional Predictors","authors":"J. De Gooijer, D. Zerom","doi":"10.2139/ssrn.3057523","DOIUrl":"https://doi.org/10.2139/ssrn.3057523","url":null,"abstract":"Abstract The paper proposes a method for forecasting conditional quantiles. In practice, one often does not know the “true” structure of the underlying conditional quantile function, and in addition, we may have a large number of predictors. Focusing on such cases, we introduce a flexible and practical framework based on penalized high-dimensional quantile averaging. In addition to prediction, we show that the proposed method can also serve as a predictor selector. We conduct extensive simulation experiments to asses its prediction and variable selection performances for nonlinear and linear time series model designs. In terms of predictor selection, the approach tends to select the true set of predictors with minimal false positives. With respect to prediction accuracy, the method competes well even with the benchmark/oracle methods that know one or more aspects of the underlying quantile regression model. We further illustrate the merit of the proposed method by providing an application to the out-of-sample forecasting of U.S. core inflation using a large set of monthly macroeconomic variables based on FRED-MD database. The application offers several empirical findings.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125715181","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}
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