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Empirical evidence on the Euler equation for investment in the US 美国投资欧拉方程的经验证据
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-27 DOI: 10.1002/jae.3037
Guido Ascari, Qazi Haque, Leandro M. Magnusson, Sophocles Mavroeidis
{"title":"Empirical evidence on the Euler equation for investment in the US","authors":"Guido Ascari,&nbsp;Qazi Haque,&nbsp;Leandro M. Magnusson,&nbsp;Sophocles Mavroeidis","doi":"10.1002/jae.3037","DOIUrl":"10.1002/jae.3037","url":null,"abstract":"<p>Is the typical specification of the Euler equation for investment employed in dynamic stochastic general equilibrium (DSGE) models consistent with aggregate macro data? The answer is yes using state-of-the-art econometric methods that are robust to weak instruments and exploit information in possible structural changes. Unfortunately, however, there is very little information about the values of the parameters in aggregate data because investment is unresponsive to changes in capital utilization and the real interest rate. Bayesian estimation using fully specified DSGE models is more accurate due to both informative priors and cross-equation restrictions.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Peer desirability and academic achievement 同伴可取性与学习成绩
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-26 DOI: 10.1002/jae.3036
Adrian Mehic
{"title":"Peer desirability and academic achievement","authors":"Adrian Mehic","doi":"10.1002/jae.3036","DOIUrl":"10.1002/jae.3036","url":null,"abstract":"<p>Using the random assignment of university engineering students to peer groups during introductory freshmen weeks, this paper studies how a student's parental income and facial attractiveness affect the grade outcomes of peers. The results show that exposure to highly desirable peers with respect to socioeconomic background and beauty improves grades. The results operate chiefly through a direct spillover channel and also through an indirect marriage market channel, through which exposure to high-desirability peers improves well-being. A field experiment suggests that the marriage market mechanism is likely to be limited to students not currently in a romantic relationship.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140003996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How does the dramatic rise of nonresponse in the Current Population Survey impact labor market indicators? 当前人口调查中无回复率的急剧上升对劳动力市场指标有何影响？
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-26 DOI: 10.1002/jae.3035
Robert Bernhardt, David Munro, Erin L. Wolcott
{"title":"How does the dramatic rise of nonresponse in the Current Population Survey impact labor market indicators?","authors":"Robert Bernhardt,&nbsp;David Munro,&nbsp;Erin L. Wolcott","doi":"10.1002/jae.3035","DOIUrl":"10.1002/jae.3035","url":null,"abstract":"<div>\u0000 \u0000 <p>Within a decade, the share of households refusing to participate in the Current Population Survey (CPS) tripled. We show households that refuse 1 month but respond in an adjacent month account for an important part of the rise. Leveraging the labor force status of survey participants in the months surrounding their nonresponse, we find that rising refusals suppressed the measured labor force participation rate and employment–population ratio but had little effect on the unemployment rate. Notably, nonresponse bias accounts for at least 10% of the reported decline in the labor force participation rate from 2000 to 2020.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139968505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A high-dimensional multinomial logit model 高维多项式对数模型
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-18 DOI: 10.1002/jae.3034
Didier Nibbering
{"title":"A high-dimensional multinomial logit model","authors":"Didier Nibbering","doi":"10.1002/jae.3034","DOIUrl":"10.1002/jae.3034","url":null,"abstract":"<p>The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data-driven two-way parameter clustering over outcome categories and explanatory dummy categories in a multinomial logit model. A Bayesian Dirichlet process mixture model encourages parameters to cluster over the categories, which reduces the number of unique model parameters and provides interpretable clusters of categories. In an empirical application, we estimate the holiday preferences of 11 household types over 49 holiday destinations and identify a small number of household segments with different preferences across clusters of holiday destinations.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139919502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advance layoff notices and aggregate job loss 预发裁员通知和累计职位损失
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-15 DOI: 10.1002/jae.3032
Pawel M. Krolikowski, Kurt G. Lunsford
{"title":"Advance layoff notices and aggregate job loss","authors":"Pawel M. Krolikowski,&nbsp;Kurt G. Lunsford","doi":"10.1002/jae.3032","DOIUrl":"10.1002/jae.3032","url":null,"abstract":"<div>\u0000 \u0000 <p>We collect data from Worker Adjustment and Retraining Notification (WARN) Act notices and establish their usefulness as an indicator of aggregate job loss. The number of workers affected by WARN notices (“WARN layoffs”) leads state-level initial unemployment insurance claims and unemployment rate (UR) and private employment changes. WARN layoffs comove with aggregate layoffs from Mass Layoff Statistics and the Job Openings and Labor Turnover Survey but are timelier and cover a longer sample. In a vector autoregression, changes in WARN layoffs lead UR changes and job separations. Finally, they improve pseudo real-time forecasts of the UR and private employment changes.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139919559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identifying factors via automatic debiased machine learning 通过自动去偏差机器学习识别因素
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-13 DOI: 10.1002/jae.3031
Esfandiar Maasoumi, Jianqiu Wang, Zhuo Wang, Ke Wu
{"title":"Identifying factors via automatic debiased machine learning","authors":"Esfandiar Maasoumi,&nbsp;Jianqiu Wang,&nbsp;Zhuo Wang,&nbsp;Ke Wu","doi":"10.1002/jae.3031","DOIUrl":"10.1002/jae.3031","url":null,"abstract":"<div>\u0000 \u0000 <p>Identifying risk factors that have significant explanatory power for the cross-sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear stochastic discount factor (SDF) assumption. The ADML resolves biased estimation, non-robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama–French sparse factors and factors identified via the double-selection LASSO method under a linear factor model assumption. Out of a high-dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross-section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Statistically identified structural VAR model with potentially skewed and fat-tailed errors 经统计确定的结构 VAR 模型，误差可能偏斜和肥尾
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-02-13 DOI: 10.1002/jae.3019
Jetro Anttonen, Markku Lanne, Jani Luoto
{"title":"Statistically identified structural VAR model with potentially skewed and fat-tailed errors","authors":"Jetro Anttonen,&nbsp;Markku Lanne,&nbsp;Jani Luoto","doi":"10.1002/jae.3019","DOIUrl":"10.1002/jae.3019","url":null,"abstract":"<p>We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized <i>t</i>-distributions, whose flexibility compared with commonly considered Student's <i>t</i>-distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non-Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical application, the data support narrative sign restrictions in identifying the US monetary policy shock. In contrast to some of the previous literature, we find a strong negative response of real activity to contractionary monetary policy after a few months' delay.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A maximum likelihood bunching estimator of the elasticity of taxable income 应税收入弹性的最大似然串联估算器
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-01-18 DOI: 10.1002/jae.3015
Thomas Aronsson, Katharina Jenderny, Gauthier Lanot
{"title":"A maximum likelihood bunching estimator of the elasticity of taxable income","authors":"Thomas Aronsson,&nbsp;Katharina Jenderny,&nbsp;Gauthier Lanot","doi":"10.1002/jae.3015","DOIUrl":"10.1002/jae.3015","url":null,"abstract":"<p>This paper develops a maximum likelihood (ML) bunching estimator of the elasticity of taxable income (ETI). Our structural approach provides a natural framework to simultaneously account for unobserved preference heterogeneity and optimization errors and for measuring their relative importance. We characterize the conditions under which the parameters of the model are identified and show that the ML estimator performs well in terms of bias and precision. The paper also contains an empirical application using Swedish data, showing that both the ETI and the standard deviation of the optimization friction are precisely estimated, albeit relatively small.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The macroeconomy as a random forest 作为随机森林的宏观经济
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-01-17 DOI: 10.1002/jae.3030
Philippe Goulet Coulombe
{"title":"The macroeconomy as a random forest","authors":"Philippe Goulet Coulombe","doi":"10.1002/jae.3030","DOIUrl":"10.1002/jae.3030","url":null,"abstract":"<div>\u0000 \u0000 <p>I develop the <i>macroeconomic random forest</i> (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, <i>generalized</i> time-varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and structural breaks/change) and allowing for sophisticated new ones. The approach delivers clear forecasting gains over numerous alternatives, predicts the 2008 drastic rise in unemployment, and performs well for inflation. Unlike most ML-based methods, MRF is directly interpretable—via its GTVPs. For instance, the successful unemployment forecast is due to the influence of forward-looking variables (e.g., term spreads and housing starts) nearly doubling before every recession. Interestingly, the Phillips curve has indeed flattened, <i>and</i> its might is highly cyclical.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Addressing sample selection bias for machine learning methods 解决机器学习方法的样本选择偏差问题
IF 2.1 3区 经济学
Journal of Applied Econometrics Pub Date : 2024-01-17 DOI: 10.1002/jae.3029
Dylan Brewer, Alyssa Carlson
{"title":"Addressing sample selection bias for machine learning methods","authors":"Dylan Brewer,&nbsp;Alyssa Carlson","doi":"10.1002/jae.3029","DOIUrl":"10.1002/jae.3029","url":null,"abstract":"<div>\u0000 \u0000 <p>We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine-learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsen sample selection bias. We propose two control function approaches that remove the effects of selection bias before training and find that they reduce mean-squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re-election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139515469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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