Econometrics: Econometric & Statistical Methods - General eJournal最新文献_第4页

Proxy SVAR Identification of Monetary Policy Shocks – Monte Carlo Evidence and Insights for the US 货币政策冲击的代理SVAR识别-蒙特卡洛证据和对美国的见解

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-10-06 DOI: 10.2139/ssrn.3714542

H. Herwartz, H. Rohloff, Shu Wang

{"title":"Proxy SVAR Identification of Monetary Policy Shocks – Monte Carlo Evidence and Insights for the US","authors":"H. Herwartz, H. Rohloff, Shu Wang","doi":"10.2139/ssrn.3714542","DOIUrl":"https://doi.org/10.2139/ssrn.3714542","url":null,"abstract":"In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets andWouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures effectively reduced the GDP deflator within three years (i.e. by -3.06 units until 1982Q3). While the empirical analysis largely conditions ona small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86679696","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

Statistica Afacerilor: Aplicaţii, partea întâi (Business Statistics: Exercises, Part 1)

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-10-05 DOI: 10.2139/ssrn.3705457

R. Stefanescu, Ramona Dumitriu

引用次数: 0

Sports Prediction and Betting Models in the Machine Learning Age: The Case of Tennis

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-09-22 DOI: 10.2139/ssrn.3506302

S. Wilkens

{"title":"Sports Prediction and Betting Models in the Machine Learning Age: The Case of Tennis","authors":"S. Wilkens","doi":"10.2139/ssrn.3506302","DOIUrl":"https://doi.org/10.2139/ssrn.3506302","url":null,"abstract":"Machine learning and its numerous variants have meanwhile become established tools in many areas of society. Several attempts have been made to apply machine learning to the prediction of the outcome of professional sports events and to exploit “inefficiencies” in the corresponding betting markets. On the example of tennis, this paper extends previous research by conducting one of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches. The paper shows that the average prediction accuracy cannot be increased to more than about 70%. Irrespective of the used model, most of the relevant information is embedded in the betting markets, and adding other match- and player-specific data does not lead to any significant improvement. Returns from applying predictions to the sports betting market are subject to high volatility and mainly negative over the longer term. This conclusion holds across most tested models, various money management strategies, and for backing the match favorites or outsiders. The use of model ensembles that combine the predictions from multiple approaches proves to be the most promising choice.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90151773","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: 15

Heterogeneous Variable Selection in Nonlinear Panel Data Models: A Semi-Parametric Bayesian Approach 非线性面板数据模型中的异质变量选择:半参数贝叶斯方法

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-09-22 DOI: 10.2139/ssrn.3697480

A. Castelein, D. Fok, R. Paap

{"title":"Heterogeneous Variable Selection in Nonlinear Panel Data Models: A Semi-Parametric Bayesian Approach","authors":"A. Castelein, D. Fok, R. Paap","doi":"10.2139/ssrn.3697480","DOIUrl":"https://doi.org/10.2139/ssrn.3697480","url":null,"abstract":"In this paper, we develop a general method for heterogeneous variable selection in Bayesian nonlinear panel data models. Heterogeneous variable selection refers to the possibility that subsets of units are unaffected by certain variables. It may be present in applications as diverse as health treatments, consumer choice-making, macroeconomics, and operations research. Our method additionally allows for other forms of cross-sectional heterogeneity. We consider a two-group approach for the model's unit-specific parameters: each unit-specific parameter is either equal to zero (heterogeneous variable selection) or comes from a Dirichlet process (DP) mixture of multivariate normals (other cross-sectional heterogeneity). We develop our approach for general nonlinear panel data models, encompassing multinomial logit and probit models, poisson and negative binomial count models, exponential models, among many others. For inference, we develop an efficient Bayesian MCMC sampler. In a Monte Carlo study, we find that our approach is able to capture heterogeneous variable selection whereas a ``standard'' DP mixture is not. In an empirical application, we find that accounting for heterogeneous variable selection and non-normality of the continuous heterogeneity leads to an improved in-sample and out-of-sample performance and interesting insights. These findings illustrate the usefulness of our approach.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80361259","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

Rate-Efficient Asymptotic Normality for the Fourier Estimator of the Leverage Process 杠杆过程傅里叶估计量的高效率渐近正态性

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-09-14 DOI: 10.2139/ssrn.3692631

Giacomo Toscano, M. Mancino

引用次数: 5

Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects 具有交互效应的线性面板数据模型的两阶段工具变量估计

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-09-09 DOI: 10.2139/ssrn.3692123

Guowei Cui, Milda Norkute, Vasilis Sarafidis, Takashi Yamagata

{"title":"Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects","authors":"Guowei Cui, Milda Norkute, Vasilis Sarafidis, Takashi Yamagata","doi":"10.2139/ssrn.3692123","DOIUrl":"https://doi.org/10.2139/ssrn.3692123","url":null,"abstract":"This paper puts forward a new instrumental variables (IV) approach for linear panel data models with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically eliminates the interactive effects in the error term and in the regressors separately in two stages. We propose a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis for the models with homogeneous slopes reveals that: (i) the sqrt{NT}-consistent 2SIV estimator is free from asymptotic bias that could arise due to the correlation between the regressors and the estimation error of the interactive effects; (ii) under the same set of assumptions, existing popular estimators, which eliminate interactive effects either jointly in the regressors and the error term, or only in the error term, can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as the bias-corrected version of estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results and competent performance of 2SIV and MGIV in comparison with existing estimators. Furthermore, it demonstrates that the bias-corrections can be imprecise and noticeably inflate the dispersion of the estimators in finite samples.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76209589","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: 13

Combining Observational and Experimental Data Using First-stage Covariates 使用第一阶段协变量结合观测和实验数据

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-08-31 DOI: 10.2139/ssrn.3662061

George Gui

{"title":"Combining Observational and Experimental Data Using First-stage Covariates","authors":"George Gui","doi":"10.2139/ssrn.3662061","DOIUrl":"https://doi.org/10.2139/ssrn.3662061","url":null,"abstract":"Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that combines experimental and observational datasets when 1) units from these two datasets are sampled from the same population and 2) some characteristics of these units are observed. I show that if these characteristics can partially explain treatment assignment in the observational data, they can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline three estimators (weighting, shrinkage, or GMM) for implementing this strategy, and show that my methods can reduce variance by up to 50% in typical experimental designs; therefore, only half of the experimental sample is required to attain the same statistical precision. If researchers are allowed to design experiments differently, I show that they can further improve the precision by directly leveraging this correlation between characteristics and assignment. I apply the method to a search listing dataset from Expedia that studies the causal effect of search rankings, and show that my method can substantially improve the precision.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72668316","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

Comment on Glenn Shafer's 'Testing by Betting' 评格伦·谢弗的《赌博测试》

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-08-30 DOI: 10.2139/ssrn.3684664

V. Vovk

引用次数: 25

Comparing Old and New Partial Derivative Estimates from Nonlinear Nonparametric Regressions 比较非线性非参数回归的新旧偏导数估计

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-08-26 DOI: 10.2139/ssrn.3681104

H. Vinod, Fred Viole

引用次数: 0

Identification of Auction Models Using Order Statistics 使用订单统计识别拍卖模型

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-07-16 DOI: 10.2139/ssrn.3599045

Yao Luo, Ruli Xiao

引用次数: 11