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When are Google data useful to nowcast GDP? An approach via preselection and shrinkage 谷歌数据何时可用于预测国内生产总值？通过预选和收缩的方法
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-22 DOI: 10.1080/07350015.2022.2116025
Laurent Ferrara, Anna Simoni
{"title":"When are Google data useful to nowcast GDP? An approach via preselection and shrinkage","authors":"Laurent Ferrara, Anna Simoni","doi":"10.1080/07350015.2022.2116025","DOIUrl":"https://doi.org/10.1080/07350015.2022.2116025","url":null,"abstract":"<p><b>Abstract</b></p><p>Alternative data sets are widely used for macroeconomic nowcasting together with machine learning–based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them by Monte-Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.</p>","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141257379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric Option Pricing with Generalized Entropic Estimators* 使用广义熵估计器进行非参数期权定价*
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-22 DOI: 10.1080/07350015.2022.2115499
Caio Almeida, Gustavo Freire, Rafael Azevedo, Kym Ardison
{"title":"Nonparametric Option Pricing with Generalized Entropic Estimators*","authors":"Caio Almeida, Gustavo Freire, Rafael Azevedo, Kym Ardison","doi":"10.1080/07350015.2022.2115499","DOIUrl":"https://doi.org/10.1080/07350015.2022.2115499","url":null,"abstract":"<p><b>Abstract</b></p><p>We propose a family of nonparametric estimators for an option price that require only the use of underlying return data, but can also easily incorporate information from observed option prices. Each estimator comes from a risk-neutral measure minimizing generalized entropy according to a different Cressie-Read discrepancy. We apply our method to price S&amp;P 500 options and the cross-section of individual equity options, using distinct amounts of option data in the estimation. Estimators incorporating mild nonlinearities produce optimal pricing accuracy within the Cressie-Read family and outperform several benchmarks such as Black-Scholes and different GARCH option pricing models. Overall, we provide a powerful option pricing technique suitable for scenarios of limited option data availability.</p>","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141257398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Specification Testing of Regression Models with Mixed Discrete and Continuous Predictors 离散和连续混合预测回归模型的规格检验
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110879
Xuehu Zhu, Qiming Zhang, Lixing Zhu, Jun Zhang, Luoyao Yu
{"title":"Specification Testing of Regression Models with Mixed Discrete and Continuous Predictors","authors":"Xuehu Zhu, Qiming Zhang, Lixing Zhu, Jun Zhang, Luoyao Yu","doi":"10.1080/07350015.2022.2110879","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110879","url":null,"abstract":"Abstract This article proposes a nonparametric projection-based adaptive-to-model specification test for regressions with discrete and continuous predictors. The test statistic is asymptotically normal under the null hypothesis and omnibus against alternative hypotheses. The test behaves like a locally smoothing test as if the number of continuous predictors was one and can detect the local alternative hypotheses distinct from the null hypothesis at the rate that can be achieved by existing locally smoothing tests for regressions with only one continuous predictor. Because of the model adaptation property, the test can fully use the model structure under the null hypothesis so that the dimensionality problem can be significantly alleviated. A discretization-expectation ordinary least squares estimation approach for partial central subspace in sufficient dimension reduction is developed as a by-product in the test construction. We suggest a residual-based wild bootstrap method to give an approximation by fully using the null model and thus closer to the limiting null distribution than existing bootstrap approximations. We conduct simulation studies to compare it with existing tests and two real data examples for illustration.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48057158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
LASSO for Stochastic Frontier Models with Many Efficient Firms 具有多个有效企业的随机前沿模型的LASSO
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110881
W. Horrace, Hyunseok Jung, Yoonseok Lee
{"title":"LASSO for Stochastic Frontier Models with Many Efficient Firms","authors":"W. Horrace, Hyunseok Jung, Yoonseok Lee","doi":"10.1080/07350015.2022.2110881","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110881","url":null,"abstract":"Abstract We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L 1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection criterion and an efficient optimization algorithm based on coordinate descent. We apply the method to estimate a group of efficient police officers who are best at detecting contraband in motor vehicle stops (i.e., search efficiency) in Syracuse, NY.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48059493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Bagged Pretested Portfolio Selection 袋装预测试投资组合选择
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110880
Ekaterina Kazak, W. Pohlmeier
{"title":"Bagged Pretested Portfolio Selection","authors":"Ekaterina Kazak, W. Pohlmeier","doi":"10.1080/07350015.2022.2110880","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110880","url":null,"abstract":"Abstract This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41418356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs 多输入和多输出的非参数随机前沿模型
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-08-08 DOI: 10.1080/07350015.2022.2110882
L. Simar, P. W. Wilson
{"title":"Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs","authors":"L. Simar, P. W. Wilson","doi":"10.1080/07350015.2022.2110882","DOIUrl":"https://doi.org/10.1080/07350015.2022.2110882","url":null,"abstract":"Abstract Stochastic frontier models along the lines of Aigner et al. are widely used to benchmark firms’ performances in terms of efficiency. The models are typically fully parametric, with functional form specifications for the frontier as well as both the noise and the inefficiency processes. Studies such as Kumbhakar et al. have attempted to relax some of the restrictions in parametric models, but so far all such approaches are limited to a univariate response variable. Some (e.g., Simar and Zelenyuk; Kuosmanen and Johnson) have proposed nonparametric estimation of directional distance functions to handle multiple inputs and outputs, raising issues of endogeneity that are either ignored or addressed by imposing restrictive and implausible assumptions. This article extends nonparametric methods developed by Simar et al. and Hafner et al. to allow multiple inputs and outputs in an almost fully nonparametric framework while avoiding endogeneity problems. We discuss properties of the resulting estimators, and examine their finite-sample performance through Monte Carlo experiments. Practical implementation of the method is illustrated using data on U.S. commercial banks.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48001020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Identification of SVAR Models by Combining Sign Restrictions With External Instruments 符号约束与外部仪器相结合的SVAR模型识别
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-07-29 DOI: 10.1080/07350015.2022.2104857
R. Braun, R. Brüggemann
{"title":"Identification of SVAR Models by Combining Sign Restrictions With External Instruments","authors":"R. Braun, R. Brüggemann","doi":"10.1080/07350015.2022.2104857","DOIUrl":"https://doi.org/10.1080/07350015.2022.2104857","url":null,"abstract":"ABSTRACT We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further orthogonal shocks, or as an additional piece of information to pin down the shocks identified by the external instruments more precisely. In a second setting, we assume that proxy variables are only “plausibly exogenous” and suggest various types of inequality restrictions to bound the relation between structural shocks and the external variable. This can be combined with conventional sign restrictions to further narrow down the set of admissible models. Within a proxy-augmented SVAR, we conduct Bayesian inference and discuss computation of Bayes factors. They can be useful to test either the sign- or IV restrictions as overidentifying. We illustrate the usefulness of our methodology in estimating the effects of oil supply and monetary policy shocks.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44798055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Trends in Earnings Volatility Using Linked Administrative and Survey Data 使用关联管理和调查数据的收益波动趋势
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-07-15 DOI: 10.1080/07350015.2022.2102023
James P. Ziliak, Charles M. Hokayem, C. Bollinger
{"title":"Trends in Earnings Volatility Using Linked Administrative and Survey Data","authors":"James P. Ziliak, Charles M. Hokayem, C. Bollinger","doi":"10.1080/07350015.2022.2102023","DOIUrl":"https://doi.org/10.1080/07350015.2022.2102023","url":null,"abstract":"Abstract We document trends in earnings volatility separately by gender using unique linked survey data from the CPS ASEC and Social Security earnings records for the tax years spanning 1995–2015. The exact data link permits us to focus on differences in measured volatility from earnings nonresponse, survey attrition, and measurement between survey and administrative earnings data reports, while holding constant the sampling frame. Our results for both men and women suggest that the level and trend in volatility is similar in the survey and administrative data, showing substantial business-cycle sensitivity among men but no overall trend among continuous workers, while women demonstrate no change in earnings volatility over the business cycle but a declining trend. A substantive difference emerges with the inclusion of imputed earnings among survey nonrespondents, suggesting that users of the ASEC drop earnings nonrespondents.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42002407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity 具有未观测异质性分组模式的纵向数据的大规模广义线性模型
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-07-06 DOI: 10.1080/07350015.2022.2097913
T. Ando, Jushan Bai
{"title":"Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity","authors":"T. Ando, Jushan Bai","doi":"10.1080/07350015.2022.2097913","DOIUrl":"https://doi.org/10.1080/07350015.2022.2097913","url":null,"abstract":"ABSTRACT This article provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their heterogeneity is influenced by group-specific unobservable factor structures. The model includes, as special cases, probit, logit, and Poisson regressions with interactive fixed effects along with unknown group membership. We discuss a computationally efficient estimation method and derive the corresponding asymptotic theory. Uniform consistency of the estimated group membership is established. To test heterogeneous regression coefficients within groups, we propose a Swamy-type test that allows for unobserved heterogeneity. We apply the proposed method to the study of market structure of the taxi industry in New York City. Our method unveils interesting and important insights from large-scale longitudinal data that consist of over 450 million data points.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48151446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates 具有组合协变量的高维线性对数对比度模型的鲁棒信号恢复
IF 3 2区 数学
Journal of Business & Economic Statistics Pub Date : 2022-07-06 DOI: 10.1080/07350015.2022.2097911
Dongxiao Han, Jian Huang, Yuanyuan Lin, Lei Liu, Lianqiang Qu, Liuquan Sun
{"title":"Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates","authors":"Dongxiao Han, Jian Huang, Yuanyuan Lin, Lei Liu, Lianqiang Qu, Liuquan Sun","doi":"10.1080/07350015.2022.2097911","DOIUrl":"https://doi.org/10.1080/07350015.2022.2097911","url":null,"abstract":"Abstract In this article, we propose a robust signal recovery method for high-dimensional linear log-contrast models, when the error distribution could be heavy-tailed and asymmetric. The proposed method is built on the Huber loss with penalization. We establish the and consistency for the resulting estimator. Under conditions analogous to the irrepresentability condition and the minimum signal strength condition, we prove that the signed support of the slope parameter vector can be recovered with high probability. The finite-sample behavior of the proposed method is evaluated through simulation studies, and applications to a GDP satisfaction dataset an HIV microbiome dataset are provided.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49373682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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