SummaryResearch underscores the role of naturalization in enhancing immigrants' socio‐economic integration, yet application rates remain low. We estimate a policy rule for a letter‐based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one‐half of 1717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared with assigning the same letter to everyone.
{"title":"Optimal multi‐action treatment allocation: A two‐phase field experiment to boost immigrant naturalization","authors":"Achim Ahrens, Alessandra Stampi‐Bombelli, Selina Kurer, Dominik Hangartner","doi":"10.1002/jae.3092","DOIUrl":"https://doi.org/10.1002/jae.3092","url":null,"abstract":"SummaryResearch underscores the role of naturalization in enhancing immigrants' socio‐economic integration, yet application rates remain low. We estimate a policy rule for a letter‐based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one‐half of 1717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared with assigning the same letter to everyone.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211624","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}
Akram Shavkatovich Hasanov, Robert Brooks, Sirojiddin Abrorov, Aktam Usmanovich Burkhanov
SummaryWe examine the empirical significance of structural changes concerning generalized autoregressive conditional heteroskedasticity (GARCH) models of exchange rate volatility using out‐of‐sample tests by replicating and carrying out robustness checks on the volatility forecasting study by Rapach and Strauss (Journal of Applied Econometrics, 2008; 23, 65–90). We employ the same econometric models but incorporate recent US dollar daily exchange rates data while also using different software, a relatively recent forecast accuracy test and loss metrics. Our objective is to attain scientific replication in a broad sense. Our analysis verifies and broadly aligns with the results obtained in the original study. In particular, we find strong evidence that the models incorporating structural breaks demonstrate superior performance across all loss functions and forecast horizons compared with those models that ignore instabilities.
{"title":"Structural breaks and GARCH models of exchange rate volatility: Re‐examination and extension","authors":"Akram Shavkatovich Hasanov, Robert Brooks, Sirojiddin Abrorov, Aktam Usmanovich Burkhanov","doi":"10.1002/jae.3091","DOIUrl":"https://doi.org/10.1002/jae.3091","url":null,"abstract":"SummaryWe examine the empirical significance of structural changes concerning generalized autoregressive conditional heteroskedasticity (GARCH) models of exchange rate volatility using out‐of‐sample tests by replicating and carrying out robustness checks on the volatility forecasting study by Rapach and Strauss (Journal of Applied Econometrics, 2008; 23, 65–90). We employ the same econometric models but incorporate recent US dollar daily exchange rates data while also using different software, a relatively recent forecast accuracy test and loss metrics. Our objective is to attain scientific replication in a broad sense. Our analysis verifies and broadly aligns with the results obtained in the original study. In particular, we find strong evidence that the models incorporating structural breaks demonstrate superior performance across all loss functions and forecast horizons compared with those models that ignore instabilities.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944541","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}
SummaryThe shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead, we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model that allows for computationally fast and order‐invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions show that nonparametric treatment of the VAR errors often improves forecast accuracy and can be used to analyze the changing transmission of US monetary policy.
摘要冲击宏观经济模型（如向量自回归（VAR））的冲击有可能是非高斯的，表现出不对称和肥尾。基于这一考虑，本文开发了使用德里克利特过程混合物（DPM）对还原形式冲击进行建模的 VAR。然而，我们并没有采用简单地用 DPM 对 VAR 误差建模的明显策略，因为这将导致在较大的 VAR 中贝叶斯推理计算上的不可行性，并可能对 VAR 中变量排序方式产生敏感性。相反，我们受面板数据模型中随机效应的贝叶斯非参数处理方法的启发，开发了一种特殊的加法误差结构。我们的研究表明，这种模型可以在具有非参数冲击的大型 VAR 中实现快速计算和阶次不变的推断。我们对不同维度的非参数 VAR 的实证结果表明，对 VAR 误差的非参数处理往往能提高预测准确性，并可用于分析美国货币政策不断变化的传导。
{"title":"Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks","authors":"Florian Huber, Gary Koop","doi":"10.1002/jae.3087","DOIUrl":"https://doi.org/10.1002/jae.3087","url":null,"abstract":"SummaryThe shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead, we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model that allows for computationally fast and order‐invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions show that nonparametric treatment of the VAR errors often improves forecast accuracy and can be used to analyze the changing transmission of US monetary policy.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944526","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}
SummaryWe use a structural vector autoregressive (SVAR) model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared with what we observe in the data.
{"title":"Sudden stop: Supply and demand shocks in the German natural gas market","authors":"Jochen Güntner, Magnus Reif, Maik Wolters","doi":"10.1002/jae.3089","DOIUrl":"https://doi.org/10.1002/jae.3089","url":null,"abstract":"SummaryWe use a structural vector autoregressive (SVAR) model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared with what we observe in the data.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866628","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}
SummaryThe global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.
摘要 全球金融危机和科维德-19 经济衰退再次引发了有关宏观经济数据趋势周期发现的讨论，而最近的助推技术将流行的霍德里克-普雷斯科特滤波器升级为适合数据丰富和快速计算环境的现代机器学习设备。本文将 boosting 的趋势判断能力扩展到高阶积分过程和具有局部统一根的时间序列。该理论是通过理解提升对简单指数函数的渐近效果而建立的。鉴于 FRED 数据库中的时间序列展现出各种动态模式，助推法能及时捕捉危机时的衰退和随后的复苏。
{"title":"The boosted Hodrick‐Prescott filter is more general than you might think","authors":"Ziwei Mei, Peter C. B. Phillips, Zhentao Shi","doi":"10.1002/jae.3086","DOIUrl":"https://doi.org/10.1002/jae.3086","url":null,"abstract":"SummaryThe global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866630","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}
SummaryThis paper undertakes a replication in a wide sense of a recent study that examines the relationship between historical plough agriculture and current gender roles. We revisit the main research question with recently developed causal machine learning methods, which allow researchers to model the relationship of covariates with the treatment and the outcomes in a more flexible way, while also including interactions and nonlinearities that were not considered in the original analysis. Our results suggest an even larger negative effect of the historical plough adoption on female labor force participation than what the original analysis found. The paper highlights the benefits of using causal machine learning methods in applied empirical economics.
{"title":"The effect of plough agriculture on gender roles: A machine learning approach","authors":"Anna Baiardi, Andrea A. Naghi","doi":"10.1002/jae.3083","DOIUrl":"https://doi.org/10.1002/jae.3083","url":null,"abstract":"SummaryThis paper undertakes a replication in a wide sense of a recent study that examines the relationship between historical plough agriculture and current gender roles. We revisit the main research question with recently developed causal machine learning methods, which allow researchers to model the relationship of covariates with the treatment and the outcomes in a more flexible way, while also including interactions and nonlinearities that were not considered in the original analysis. Our results suggest an even larger negative effect of the historical plough adoption on female labor force participation than what the original analysis found. The paper highlights the benefits of using causal machine learning methods in applied empirical economics.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584917","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}
Knut Are Aastveit, Tuva Marie Fastbø, Eleonora Granziera, Kenneth Sæterhagen Paulsen, Kjersti Næss Torstensen
SummaryWe use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high‐frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID‐19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.
{"title":"Nowcasting Norwegian household consumption with debit card transaction data","authors":"Knut Are Aastveit, Tuva Marie Fastbø, Eleonora Granziera, Kenneth Sæterhagen Paulsen, Kjersti Næss Torstensen","doi":"10.1002/jae.3076","DOIUrl":"https://doi.org/10.1002/jae.3076","url":null,"abstract":"SummaryWe use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high‐frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID‐19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576252","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}
SummaryWe propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.
摘要 我们提出了一种将分层聚类与过度识别限制检验相结合的程序，用于从大量的工具变量（IV）中选择有效的工具变量（IV）。其中一些 IV 可能是无效的，因为它们没有通过排除限制。我们的研究表明，如果最大的一组 IV 是有效的，那么我们的方法就能实现神谕特性。与现有技术不同，我们的工作涉及多个内生回归因子。仿真结果表明，该方法在各种情况下都具有优势。该方法被应用于估计移民对工资的影响。
{"title":"Agglomerative hierarchical clustering for selecting valid instrumental variables","authors":"Nicolas Apfel, Xiaoran Liang","doi":"10.1002/jae.3078","DOIUrl":"https://doi.org/10.1002/jae.3078","url":null,"abstract":"SummaryWe propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576253","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}
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