Journal of Applied Econometrics最新文献

{"title":"Minimum Wages and Teenage Childbearing in the United States","authors":"Kyutaro Matsuzawa,&nbsp;Daniel I. Rees,&nbsp;Joseph J. Sabia,&nbsp;Rebecca Margolit","doi":"10.1002/jae.3112","DOIUrl":"https://doi.org/10.1002/jae.3112","url":null,"abstract":"<div>\u0000 \u0000 <p>The minimum wage is increasingly viewed as an important, but often neglected, tool for improving public health outcomes. Using data from the period 2003–2019 and a stacked difference-in-differences regression model that accounts for dynamic and heterogeneous treatment effects, we explore the relationship between minimum wages and teenage childbearing in the United States. We find no evidence of a systematic, negative relationship between minimum wages and childbearing among 15- through 19-year-olds. Likewise, our estimates are not consistent with the argument that minimum wages are an effective policy tool for discouraging female 15- through 19-year-olds from having unprotected sex.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"471-484"},"PeriodicalIF":2.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190799","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}

{"title":"Correction to “Heterogeneity and Dynamics in Network Models”","authors":"","doi":"10.1002/jae.3121","DOIUrl":"https://doi.org/10.1002/jae.3121","url":null,"abstract":"<p>D'Innocenzo, Enzo, Andre Lucas, Anne Opschoor, Xingmin Zhang (2024): “Heterogeneity and Dynamics in Network Models,” <i>Journal of Applied Econometrics</i>, <b>39</b>, 150-173. https://doi.org/10.1002/jae.3013</p><p>The figures and tables below give updated Tables 2–4 and Figures 3-9 from the original paper. The computer code distributed via https://www.gasmodel.com/code.htm has also been updated.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"349-356"},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749569","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}

{"title":"Dynamic Mixture Vector Autoregressions With Score-Driven Weights","authors":"Alexander Georges Gretener,&nbsp;Matthias Neuenkirch,&nbsp;Dennis Umlandt","doi":"10.1002/jae.3119","DOIUrl":"https://doi.org/10.1002/jae.3119","url":null,"abstract":"<p>We propose a novel dynamic mixture vector autoregressive (VAR) model where the time-varying mixture weights are driven by the predictive likelihood score. Intuitively, the weight of a component VAR model is increased in the subsequent period if the current observation is more likely to be drawn from this state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood-based estimation and inference. In a Monte Carlo study, we document the model's ability to filter and predict mixture dynamics across different data-generating processes. Moreover, we illustrate the model's empirical performance with the help of two applications.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"455-470"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190642","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}

{"title":"Modelling Volatility Cycles: The MF2-GARCH Model","authors":"Christian Conrad,&nbsp;Robert F. Engle","doi":"10.1002/jae.3118","DOIUrl":"https://doi.org/10.1002/jae.3118","url":null,"abstract":"<p>We propose a novel multiplicative factor multi-frequency GARCH (MF2-GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one-component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2-GARCH features stationary returns, and long-term volatility forecasts are mean-reverting. When applied to the S&amp;P 500, the new component model significantly outperforms the one-component GJR-GARCH, the GARCH-MIDAS-RV, and the log-HAR model in long-term out-of-sample forecasting. We illustrate the MF2-GARCH's scalability by applying the new model to more than 2100 individual stocks in the Volatility Lab at NYU Stern.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"438-454"},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191254","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}

{"title":"Interacting Treatments With Endogenous Takeup","authors":"Máté Kormos,&nbsp;Robert P. Lieli,&nbsp;Martin Huber","doi":"10.1002/jae.3120","DOIUrl":"https://doi.org/10.1002/jae.3120","url":null,"abstract":"<p>We study causal inference in randomized experiments (or quasi-experiments) following a \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>×</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ 2times 2 $$</annotation>\u0000 </semantics></math> factorial design. There are two treatments, denoted \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 </mrow>\u0000 <annotation>$$ A $$</annotation>\u0000 </semantics></math> and \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>B</mi>\u0000 </mrow>\u0000 <annotation>$$ B $$</annotation>\u0000 </semantics></math>, and units are randomly assigned to one of four categories: treatment \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 </mrow>\u0000 <annotation>$$ A $$</annotation>\u0000 </semantics></math> alone, treatment \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>B</mi>\u0000 </mrow>\u0000 <annotation>$$ B $$</annotation>\u0000 </semantics></math> alone, joint treatment, or none. Allowing for endogenous non-compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal interpretation of various instrumental variable estimands under more general compliance conditions than in the literature. In general, if treatment takeup is driven by both instruments for some units, it becomes difficult to separate treatment interaction from treatment effect heterogeneity. We provide auxiliary conditions and various bounding strategies that may help zero in on causally interesting parameters. We apply our results to a program randomly offering two different treatments to first-year college students, namely, tutoring and financial incentives, in order to assess the effect of the treatments on academic performance.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"424-437"},"PeriodicalIF":2.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190697","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}

{"title":"Macroeconomic Responses to Uncertainty Shocks: The Perils of Recursive Orderings","authors":"Lutz Kilian,&nbsp;Michael D. Plante,&nbsp;Alexander W. Richter","doi":"10.1002/jae.3113","DOIUrl":"https://doi.org/10.1002/jae.3113","url":null,"abstract":"<div>\u0000 \u0000 <p>A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on economic activity. We prove by counterexample and show by simulation that this practice is invalid, whether the data generating process is a structural VAR model or a dynamic stochastic general equilibrium model. Simulation evidence suggests that the underlying identification challenge can be addressed using an instrumental variables estimator.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"395-410"},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190688","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}

{"title":"Difference-in-Differences With a Misclassified Treatment","authors":"Akanksha Negi,&nbsp;Digvijay S. Negi","doi":"10.1002/jae.3116","DOIUrl":"https://doi.org/10.1002/jae.3116","url":null,"abstract":"<p>This paper studies identification and estimation of the average treatment effect of a latent treated subpopulation in difference-in-difference designs when the observed treatment is differentially (or endogenously) mismeasured for the truth. Common examples include misreporting and mistargeting. We propose a two-step estimator that corrects for the empirically common phenomenon of one-sided misclassification in the treatment status. The solution uses a single exclusion restriction embedded in a partial observability probit to point identify the latent parameter. We demonstrate the method by revisiting two large-scale national programs in India: one where pension benefits are underreported and second where the program is mistargeted.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"411-423"},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190689","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}

{"title":"Dynamic Effects of Persistent Shocks","authors":"Mario Alloza,&nbsp;Jesús Gonzalo,&nbsp;Carlos Sanz","doi":"10.1002/jae.3115","DOIUrl":"https://doi.org/10.1002/jae.3115","url":null,"abstract":"<div>\u0000 \u0000 <p>We provide evidence that many narrative shocks used by prominent literature display some persistence. We show that the two leading methods to estimate impulse responses to an independently identified shock (local projections and distributed lag models) treat persistence differently, hence identifying different objects. We propose corrections to re-establish the equivalence between local projections and distributed lag models, providing applied researchers with methods and guidance to estimate their desired object of interest. We apply these methods to well-known empirical work and find that how persistence is treated has a sizable impact on the estimates of dynamic effects.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"380-394"},"PeriodicalIF":2.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190969","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}

{"title":"Approximating Fixed-Horizon Forecasts Using Fixed-Event Forecasts","authors":"Malte Knüppel,&nbsp;Andreea L. Vladu","doi":"10.1002/jae.3114","DOIUrl":"https://doi.org/10.1002/jae.3114","url":null,"abstract":"<p>Many forecast surveys ask their participants for fixed-event forecasts. As fixed-event forecasts have seasonal properties, users often employ an ad hoc approach to approximate fixed-horizon forecasts based on these fixed-event forecasts. We derive an optimal approximation for fixed-horizon forecasts by minimizing the mean-squared approximation error. Like the ad hoc approach, our approximation employs a weighted average of the fixed-event forecasts. The optimal weights tend to differ substantially from those of the ad hoc approach. In empirical applications, the gains from using optimal instead of ad hoc weights turn out to be sizeable. The approximation approach proposed can also be useful in other applications.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 4","pages":"359-379"},"PeriodicalIF":2.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191124","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}

{"title":"Belief Shocks and Implications of Expectations About Growth-at-Risk","authors":"Maximilian Boeck,&nbsp;Michael Pfarrhofer","doi":"10.1002/jae.3117","DOIUrl":"https://doi.org/10.1002/jae.3117","url":null,"abstract":"<p>This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth-at-risk at various quantiles. This involves both recovering the quantiles of the nowcast distribution of output growth from the Survey of Professional Forecasters, and, since the true quantiles of output growth are unobserved, estimating them with quantile regressions. We document a lack of distinct patterns in response to shocks arising from nowcasts misjudging macroeconomic risk. Although the differences are statistically insignificant, belief shocks about downside risk seem to produce somewhat sharper business cycle fluctuations.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"341-348"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749310","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}