Econometrics: Econometric & Statistical Methods - General eJournal最新文献_第10页

Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations 用Wasserstein生成对抗网络设计蒙特卡洛仿真

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-09-05 DOI: 10.3386/w26566

S. Athey, G. Imbens, Jonas Metzger, Evan Munro

{"title":"Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations","authors":"S. Athey, G. Imbens, Jonas Metzger, Evan Munro","doi":"10.3386/w26566","DOIUrl":"https://doi.org/10.3386/w26566","url":null,"abstract":"When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design. In recent years a new class of generative models emerged in the machine learning literature, termed Generative Adversarial Networks (GANs) that can be used to systematically generate artificial data that closely mimics real economic datasets, while limiting the degrees of freedom for the researcher and optionally satisfying privacy guarantees with respect to their training data. In addition if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she may wish to assess the performance, e.g., the coverage rate of confidence intervals or the bias of the estimator, using simulated data which resembles her setting. Tol illustrate these methods we apply Wasserstein GANs (WGANs) to compare a number of different estimators for average treatment effects under unconfoundedness in three distinct settings (corresponding to three real data sets) and present a methodology for assessing the robustness of the results. In this example, we find that (i) there is not one estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, and (ii) systematic simulation studies can be helpful for selecting among competing methods in this situation.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85227769","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: 63

Tests for Structural Breaks in Time Series Analysis: A Review of Recent Development 时间序列分析中结构断裂的检验:最新发展综述

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-31 DOI: 10.34293/economics.v7i4.628

Muthuramu P, Uma Maheswari T

引用次数: 11

Estimation of Large Dimensional Conditional Factor Models in Finance 金融中大维度条件因子模型的估计

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-27 DOI: 10.2139/ssrn.3443426

P. Gagliardini, P. Gagliardini, Elisa Ossola, O. Scaillet, O. Scaillet

{"title":"Estimation of Large Dimensional Conditional Factor Models in Finance","authors":"P. Gagliardini, P. Gagliardini, Elisa Ossola, O. Scaillet, O. Scaillet","doi":"10.2139/ssrn.3443426","DOIUrl":"https://doi.org/10.2139/ssrn.3443426","url":null,"abstract":"This chapter provides an econometric methodology for inference in large-dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of disaggregated data for individual securities motivates our focus on methodologies for a large number of assets. The beginning of the chapter outlines the concept of approximate factor structure in the presence of conditional information, and develops an arbitrage pricing theory for large-dimensional factor models in this framework. Then we distinguish between two different cases for inference depending on whether factors are observable or not. We focus on diagnosing model specification, estimating conditional risk premia, and testing asset pricing restrictions under increasing cross-sectional and time series dimensions. At the end of the chapter, we review some of the empirical findings and contrast analysis based on individual stocks and standard sets of portfolios. We also discuss the impact on computing time-varying cost of equity for a firm, and summarize differences between results for developed and emerging markets in an international setting.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87798191","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: 19

Locally Bayesian Learning in Networks 网络中的局部贝叶斯学习

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-27 DOI: 10.3982/te3273

Wei Li, Xu Tan

引用次数: 2

Pulling Starters 拉发

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-22 DOI: 10.2139/ssrn.3441872

Duncan Finigan, Brian Mills, Daniel F. Stone

{"title":"Pulling Starters","authors":"Duncan Finigan, Brian Mills, Daniel F. Stone","doi":"10.2139/ssrn.3441872","DOIUrl":"https://doi.org/10.2139/ssrn.3441872","url":null,"abstract":"We study a fundamental strategic decision in baseball: when (if at all) to make the ``call to the bullpen'' and pull the starting pitcher. We first use a simple theoretical model to show that at the optimal time to pull the starter, the pitching change should yield a emph{strict }improvement in current pitching quality (i.e., a strict decrease in runs allowed in the current inning). We then use detailed pitch-level data from the 2008-2017 seasons to estimate the effects of pulling the starter on both runs allowed in the current inning and on win probability. We argue that the pulling starter decision is plausibly ``as good as random'' conditional on the large set of included covariates, but acknowledge the lack of true randomization. We find that the predicted effect of pulling the starter on runs allowed is indeed negative, but the effect on win probability is a precise zero. We then examine how these choices are affected by game situations and recent game events, including a measure of lucky hitting performance, and find only scattered and limited evidence of biases. We interpret the results to imply that call to the bullpen decisions are approximately Bayesian-optimal. However, there was a steady downward trend in the mean inning that starters were pulled over a period of decades prior to our sample time-frame. Thus, even if managers make approximately Bayesian-optimal choices now, this is likely due to not only learning from their own experiences, but also learning from prior generations and the long-term stability of the baseball context.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77919892","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

Why Does Skewness Matter? Ask Kurtosis. 为什么偏度很重要?问峰度。

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-20 DOI: 10.2139/ssrn.3440159

Roberto Stein

引用次数: 0

Estimating Economic Damages with Linear Regression and Bayesian Networks (Part 2) 用线性回归和贝叶斯网络估计经济损失(下)

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-07 DOI: 10.2139/ssrn.3434042

Kurt S. Schulzke

引用次数: 0

GMM Quantile Regression GMM分位数回归

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-02 DOI: 10.2139/ssrn.3435264

Sergio Firpo, A. Galvao, Cristine Campos de Xavier Pinto, Alexandre Poirier, Graciela Sanromán

{"title":"GMM Quantile Regression","authors":"Sergio Firpo, A. Galvao, Cristine Campos de Xavier Pinto, Alexandre Poirier, Graciela Sanromán","doi":"10.2139/ssrn.3435264","DOIUrl":"https://doi.org/10.2139/ssrn.3435264","url":null,"abstract":"This paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models when allowing for general parametric restrictions on the parameters of interest over a set of quantiles. First, we suggest a GMM estimator for simultaneous estimation across multiple quantiles. This estimator exploits a partition of the quantile space, which induces a weighting matrix that is independent of the parameters of interest and the number of partitions. The GMM estimator is designed to estimate a fixed number of quantiles simultaneously, is flexible since it allows for imposing restrictions on the parameters of interest over a set of moments indexed by the quantiles, and accounts for information across quantiles to improve efficiency. Second, we study the properties of the GMM estimator when the number of partitions diverge to infinity, and derive its efficiency bound. Third, we suggest an alternative smooth GMM estimation procedure to be used with many moments. We establish the asymptotic properties of both GMM estimators. These methods have the advantage of being simple to implement in practice. Monte Carlo simulations show numerical evidence of the finite sample properties of the methods. Finally, we apply the proposed methods to estimate the effects of various covariates on birthweight of live infants at the extreme bottom of the conditional distribution.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"240 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82617927","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: 16

Auto-Regressive Distributed Lag Model for Long-Run U.S. Household Debt Determinants 长期美国家庭债务决定因素的自回归分布滞后模型

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-08-01 DOI: 10.21511/IMFI.16(3).2019.05

E. Swanepoel

引用次数: 0

Bias Correction and Robust Inference in Semiparametric Models 半参数模型中的偏差校正和鲁棒推理

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2019-07-30 DOI: 10.2139/ssrn.3429256

Jungjun Choi, Xiye Yang

引用次数: 0