Econometrics: Econometric & Statistical Methods - General eJournal最新文献_第6页

On Bootstrapping Tests of Equal Forecast Accuracy for Nested Models 嵌套模型等预报精度的自举检验

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-03-19 DOI: 10.2139/ssrn.3556974

F. Doko Tchatoka, Qazi Haque

{"title":"On Bootstrapping Tests of Equal Forecast Accuracy for Nested Models","authors":"F. Doko Tchatoka, Qazi Haque","doi":"10.2139/ssrn.3556974","DOIUrl":"https://doi.org/10.2139/ssrn.3556974","url":null,"abstract":"The asymptotic distributions of the recursive out-of-sample forecast accuracy test statistics depend on stochastic integrals of Brownian motion when the models under comparison are nested. This often complicates their implementation in practice because the computation of their asymptotic critical values is costly. Hansen and Timmermann (2015, Econometrica) propose a Wald approximation of the commonly used recursive F-statistic and provide a simple characterization of the exact density of its asymptotic distribution. However, this characterization holds only when the larger model has one extra predictor or the forecast errors are homoscedastic. No such closed-form characterization is readily available when the nesting involves more than one predictor and heteroskedasticity is present. We first show both the recursive F-test and its Wald approximation have poor finite-sample properties, especially when the forecast horizon is greater than one. We then propose an hybrid bootstrap method consisting of a block moving bootstrap (which is nonparametric) and a residual based bootstrap for both statistics, and establish its validity. Simulations show that our hybrid bootstrap has good finite-sample performance, even in multi-step ahead forecasts with heteroscedastic or autocorrelated errors, and more than one predictor. The bootstrap method is illustrated on forecasting core inflation and GDP growth.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82495629","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 Note on Adaptive Group Lasso for Structural Break Time Series 结构断裂时间序列的自适应群套索问题

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-03-15 DOI: 10.2139/ssrn.3486104

Simon Behrendt, Karsten Schweikert

引用次数: 10

An Alternative Approach for Demand Estimation 需求估计的另一种方法

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-03-07 DOI: 10.2139/ssrn.3550415

Sizhong Sun

引用次数: 0

The Determinants of Redistribution Around the World 全球再分配的决定因素

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-03-01 DOI: 10.1111/roiw.12406

M. Jäntti, Jukka Pirttilä, Risto Rönkkö

引用次数: 16

Reasonable Doubt: Experimental Detection of Job-Level Employment Discrimination 合理怀疑:工作层面就业歧视的实验检测

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-03-01 DOI: 10.3386/w26861

Patrick M. Kline, Christopher R. Walters

{"title":"Reasonable Doubt: Experimental Detection of Job-Level Employment Discrimination","authors":"Patrick M. Kline, Christopher R. Walters","doi":"10.3386/w26861","DOIUrl":"https://doi.org/10.3386/w26861","url":null,"abstract":"This paper develops methods for detecting discrimination by individual employers using correspondence experiments that send fictitious resumes to real job openings. We establish identification of higher moments of the distribution of job‐level callback rates as a function of the number of resumes sent to each job and propose shape‐constrained estimators of these moments. Applying our methods to three experimental data sets, we find striking job‐level heterogeneity in the extent to which callback probabilities differ by race or sex. Estimates of higher moments reveal that while most jobs barely discriminate, a few discriminate heavily. These moment estimates are then used to bound the share of jobs that discriminate and the posterior probability that each individual job is engaged in discrimination. In a recent experiment manipulating racially distinctive names, we find that at least 85% of jobs that contact both of two white applications and neither of two black applications are engaged in discrimination. To assess the potential value of our methods for regulators, we consider the accuracy of decision rules for investigating suspicious callback behavior in various experimental designs under a simple two‐type model that rationalizes the experimental data. Though we estimate that only 17% of employers discriminate on the basis of race, we find that an experiment sending 10 applications to each job would enable detection of 7–10% of discriminatory jobs while yielding Type I error rates below 0.2%. A minimax decision rule acknowledging partial identification of the distribution of callback rates yields only slightly fewer investigations than a Bayes decision rule based on the two‐type model. These findings suggest illegal labor market discrimination can be reliably monitored with relatively small modifications to existing correspondence designs.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85367863","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: 23

(Structural) VAR Models with Ignored Changes in Mean and Volatility 忽略均值和波动率变化的(结构性)VAR模型

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-02-26 DOI: 10.2139/ssrn.3544676

M. Demetrescu, Nazarii Salish

{"title":"(Structural) VAR Models with Ignored Changes in Mean and Volatility","authors":"M. Demetrescu, Nazarii Salish","doi":"10.2139/ssrn.3544676","DOIUrl":"https://doi.org/10.2139/ssrn.3544676","url":null,"abstract":"We discuss estimation of so-called long vector autoregressions for multivariate series exhibiting possibly time-varying mean and (co)variances. In applied work, such changes often escape undetected, and we ask how standard tools (least squares estimation, point forecasts, and estimated impulse responses) are affected when ignoring the changes altogether. Keeping the order of the autoregression fixed is known to lead to asymptotic bias in autoregressive parameter estimators in the presence of ignored changes in the mean. Yet we show that allowing the complexity of the model to increase with the sample size leads to consistent estimators of the AR coefficient matrices individually. The fitted long VAR models appear to have unit root behavior, in spite of the absence of any stochastic trend in the model, and may even mimic cointegration; but, in spite of the structural change in the data generating process, out-of-sample forecasts based on long VARs are consistent. These findings hold under constant as well as under time-varying covariances. In what concerns estimated impulse responses, their sampling behavior depends primarily on whether the residual covariance matrix is employed for identification of the structural shocks or not. While MA coefficient matrices obtained by inversion of the fitted long VAR model are consistent for the true coefficients under mild additional restrictions even under time-varying error (co)variances, the residual covariance matrix estimator converges to an \"average\" covariance matrix, such that localized estimators may be more suitable for a precise identification. Monte Carlo simulations and empirical illustration support our theoretical findings. Empirical relevance of the theory is illustrated through two illustrations: (i) international dynamics of inflation and (ii) uncertainty and economics activity.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73503353","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

Does Governance Travel Across Industries? A Mutual Fund Episode 治理是否跨行业传播?共同基金的插曲

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-02-19 DOI: 10.2139/ssrn.3540962

Yen-Cheng Chang, Kevin Tseng, Chia-Yi Yen

引用次数: 0

Testing Stochastic Dominance with Many Conditioning Variables 多条件变量随机优势检验

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-02-10 DOI: 10.2139/ssrn.3535723

O. Linton, M. Seo, Yoon-Jae Whang

{"title":"Testing Stochastic Dominance with Many Conditioning Variables","authors":"O. Linton, M. Seo, Yoon-Jae Whang","doi":"10.2139/ssrn.3535723","DOIUrl":"https://doi.org/10.2139/ssrn.3535723","url":null,"abstract":"We propose a test of the hypothesis of conditional stochastic dominance in the presence of many conditioning variables (whose dimension may grow to infinity as the sample size diverges). Our approach builds on a semiparametric location scale model in the sense that the conditional distribution of the outcome given the covariates is characterized by a nonparametric mean function and a nonparametric skedastic function with an independent innovation whose distribution is unknown. We propose to estimate the nonparametric mean and skedastic regression functions by the `1-penalized nonparametric series estimation with thresholding. Under the sparsity assumption, where the number of truly relevant series terms are relatively small (but their identities are unknown), we develop the estimation error bounds for the regression functions and series coefficients estimates allowing for the time series dependence. We derive the asymptotic distribution of the test statistic, which is not pivotal asymptotically, and introduce the smooth stationary bootstrap to approximate its sample distribution. We investigate the finite sample performance of the bootstrap critical values by a set of Monte Carlo simulations. Finally, our method is illustrated by an application to stochastic dominance among portfolio returns given all the past information.","PeriodicalId":11465,"journal":{"name":"Econometrics: Econometric & Statistical Methods - General eJournal","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81794442","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

Minimum Relative Entropy Inference for Normal and Monte Carlo Distributions 正态分布和蒙特卡罗分布的最小相对熵推断

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-02-07 DOI: 10.2139/ssrn.3479693

Marcello Colasante, A. Meucci

引用次数: 0

Shrinkage Estimation of Large Covariance Matrices: Keep it Simple, Statistician? 大协方差矩阵的收缩估计:保持简单，统计学家?

Econometrics: Econometric & Statistical Methods - General eJournal Pub Date : 2020-02-01 DOI: 10.2139/ssrn.3421503

Olivier Ledoit, Michael Wolf

引用次数: 12