{"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":"Belief Shocks and Implications of Expectations About Growth-at-Risk","authors":"Maximilian Boeck, 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}
{"title":"Spread Regression, Skewness Regression, and Kurtosis Regression With an Application to the US Wage Structure","authors":"Qiang Chen, Zhijie Xiao","doi":"10.1002/jae.3105","DOIUrl":"https://doi.org/10.1002/jae.3105","url":null,"abstract":"<p>Quantile regression provides a powerful tool for investigating the effects of covariates on key quantiles of a conditional distribution. However, we often lack a general picture of how covariates affect the overall shape of the conditional distribution. Using quantile regression estimation and quantile-based measures of spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the effects of covariates on the spread, skewness, and kurtosis of the conditional distribution. This methodology is applied to US wage data during 1980–2019 with substantive findings, and a comparison is made with a moment-based robust approach. In addition, we decompose changes in the spread into composition effects and structural effects to clarify rising inequality. We also provide the Stata commands spreadreg, skewreg, and kurtosisreg, which are available from the Statistical Software Components (SSC) archive, for easy implementation of spread, skewness, and kurtotis regressions.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"325-340"},"PeriodicalIF":2.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749980","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":"Exchange Rates, Uncovered Interest Parity, and Time-Varying Fama Regressions","authors":"Bowen Fu, Mengheng Li, Qazi Haque","doi":"10.1002/jae.3111","DOIUrl":"https://doi.org/10.1002/jae.3111","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper studies the forward premium puzzle, which signals a violation of the uncovered interest parity (UIP) hypothesis. We test this hypothesis with Fama-style regressions with time-varying parameters (TVPs) and stochastic volatility (SV) on six major currencies relative to the US dollar on monthly samples from 1993 to 2018. TVP-SV regressions are also employed to examine the opposing predictions of the forward premium and excess volatility puzzles often found in exchange rate risk premiums and interest rate differentials. Using Bayesian methods, we document that the riskiness of exchange rates explains the forward premium puzzle, while a liquidity premium reconciles the contrasting predictions of the forward premium and excess volatility puzzles.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"310-324"},"PeriodicalIF":2.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143750003","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":"Standard Errors for Difference-in-Difference Regression","authors":"Bruce E. Hansen","doi":"10.1002/jae.3110","DOIUrl":"https://doi.org/10.1002/jae.3110","url":null,"abstract":"<p>This paper makes a case for the use of jackknife methods for standard error, \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow>\u0000 <annotation>$$ p $$</annotation>\u0000 </semantics></math> value, and confidence interval construction for difference-in-difference (DiD) regression. We review cluster-robust, bootstrap, and jackknife standard error methods and show that standard methods can substantially underperform in conventional settings. In contrast, our proposed jackknife inference methods work well in broad contexts. We illustrate the relevance by replicating several influential DiD applications and showing how inferential results can change if jackknife standard error and inference methods are used.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"291-309"},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749493","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}
Florian Eckert, Philipp Kronenberg, Heiner Mikosch, Stefan Neuwirth
{"title":"Tracking Economic Activity With Alternative High-Frequency Data","authors":"Florian Eckert, Philipp Kronenberg, Heiner Mikosch, Stefan Neuwirth","doi":"10.1002/jae.3104","DOIUrl":"https://doi.org/10.1002/jae.3104","url":null,"abstract":"<p>Monthly macroeconomic series captured the sharp fluctuations during the COVID-19 pandemic only with a lag. The use of alternative high-frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed-frequency dynamic factor model with stochastic volatility for measuring GDP growth at high-frequency intervals. Its novelty is an additional state-space block, in which the sparse observations in the mixed-frequency data are augmented to a balanced panel with observed and estimated latent information. The dynamic factors are then estimated conditional on the augmented data. Our model exploits the information in rich datasets of weekly, monthly, and quarterly series, including alternative high-frequency data. GDP is nowcasted timely and accurately during volatile periods.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"270-290"},"PeriodicalIF":2.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749954","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}
Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann
{"title":"Model Averaging and Double Machine Learning","authors":"Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann","doi":"10.1002/jae.3103","DOIUrl":"https://doi.org/10.1002/jae.3103","url":null,"abstract":"<p>This paper discusses pairing double/debiased machine learning (DDML) with <i>stacking</i>, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: <i>Short-stacking</i> exploits the cross-fitting step of DDML to substantially reduce the computational burden, and <i>pooled stacking</i> enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and <span>R</span> software implementing our proposals.</p>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 3","pages":"249-269"},"PeriodicalIF":2.3,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jae.3103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749897","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":"Binary Response Model With Many Weak Instruments","authors":"Dakyung Seong","doi":"10.1002/jae.3101","DOIUrl":"https://doi.org/10.1002/jae.3101","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least squares estimator (RNLSE). Monte Carlo simulations show that the proposed estimators outperform the existing ones when there are many weak instruments. We use the proposed estimation method to examine the effect of family income on college completion.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 2","pages":"214-230"},"PeriodicalIF":2.3,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555051","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":"US Monetary Policy and Indeterminacy","authors":"Giovanni Nicolò","doi":"10.1002/jae.3107","DOIUrl":"https://doi.org/10.1002/jae.3107","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium-scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive monetary policy in the pre-1979 period is pervasive and robust to the use of alternative model specifications and data. By contrast, the evidence of an active stance after 1979 is overturned if the period of the Volcker disinflation is excluded or if the model is estimated including a time-varying inflation target and data on inflation expectations.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 2","pages":"195-213"},"PeriodicalIF":2.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554932","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":"Identifying the Sources of the Slowdown in Growth: Demand Versus Supply","authors":"Nicolò Maffei-Faccioli","doi":"10.1002/jae.3109","DOIUrl":"https://doi.org/10.1002/jae.3109","url":null,"abstract":"<div>\u0000 \u0000 <p>Long-run GDP growth has declined in the United States over the past two decades. Two competing views take centre stage in accounting for this slowdown: the demand-side view and the supply-side view. I empirically quantify their relative importance in a Bayesian SVAR with common trends identified using sign restrictions. While supply-side factors were the main driver of long-run GDP growth prior to 2000, demand-side factors explain half of its slowdown afterwards. The findings highlight the importance of demand-side factors as drivers of long-run growth.</p>\u0000 </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":"40 2","pages":"181-194"},"PeriodicalIF":2.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554863","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}
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