Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2216740
Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu
Abstract–Barseghyan and Molinari give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent.KEYWORDS: Discrete choiceRandom limited considerationRandom utilitySemi-nonparametric identification AcknowledgmentsWe thank Francesca Molinari and the participants at the 2023 ASSA meetings (JBES Session: Risk Preference Types, Limited Consideration, and Welfare) for comments.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingCattaneo gratefully acknowledges financial support from the National Science Foundation through grants SES-1947805 and SES-2241575.
{"title":"Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)","authors":"Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu","doi":"10.1080/07350015.2023.2216740","DOIUrl":"https://doi.org/10.1080/07350015.2023.2216740","url":null,"abstract":"Abstract–Barseghyan and Molinari give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent.KEYWORDS: Discrete choiceRandom limited considerationRandom utilitySemi-nonparametric identification AcknowledgmentsWe thank Francesca Molinari and the participants at the 2023 ASSA meetings (JBES Session: Risk Preference Types, Limited Consideration, and Welfare) for comments.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingCattaneo gratefully acknowledges financial support from the National Science Foundation through grants SES-1947805 and SES-2241575.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135900503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2216255
Cristina Gualdani
This article is part of an impressive research agenda by the authors which develops tools to identify models of risk preferences (Barseghyan, Prince, and Teitelbaum 2011; Barseghyan et al. 2013, 2018, 2021; Barseghyan, Molinari, and Teitelbaum 2016; Barseghyan, Teitelbaum, and Xu 2018; Barseghyan, Molinari, and Thirkettle 2021). Such work is prominent in industrial organization, development, health, labor, finance, and public economics because it is pivotal to studying incentives and assessing the welfare impact of policy interventions in insurance markets. In this article, the authors provide a novel method to identify a static model of decision-making under risk, where agents choose insurance bundles over multiple lines of property coverage, belong to different preference types, display unobserved heterogeneity in attitudes toward risk, and may consider a limited amount of bundles when making their choices. This rich framework is critical for rationalizing data patterns but introduces substantial econometric challenges. The crucial insight consists of exploiting the single crossing property (SCP) that the model features within each coverage context and an exclusion restriction to characterize the response to changes in the covariates of the choice probability of the cheapest bundle. From these elasticities, we can identify the type shares and the distribution of unobserved heterogeneity and consideration sets for each type. I devote the first part of the discussion to summarizing the identification strategy and giving context to the novelty of the arguments. In doing so, I applaud the authors for expertly and smoothly guiding us throughout their overarching research agenda to learn econometric tools that prove extremely useful for the specific setting at hand and, more generally, for employment by structural economists and other applied researchers. In the second part of the discussion, I suggest additional aspects that could play an important empirical role in the functioning of property insurance markets, namely private information about risk and supply-side issues, and pave the way for possible approaches to introduce them into the authors’ framework.
{"title":"Discussion of “Risk Preference Types, Limited Consideration, and Welfare” by Levon Barseghyan and Francesca Molinari","authors":"Cristina Gualdani","doi":"10.1080/07350015.2023.2216255","DOIUrl":"https://doi.org/10.1080/07350015.2023.2216255","url":null,"abstract":"This article is part of an impressive research agenda by the authors which develops tools to identify models of risk preferences (Barseghyan, Prince, and Teitelbaum 2011; Barseghyan et al. 2013, 2018, 2021; Barseghyan, Molinari, and Teitelbaum 2016; Barseghyan, Teitelbaum, and Xu 2018; Barseghyan, Molinari, and Thirkettle 2021). Such work is prominent in industrial organization, development, health, labor, finance, and public economics because it is pivotal to studying incentives and assessing the welfare impact of policy interventions in insurance markets. In this article, the authors provide a novel method to identify a static model of decision-making under risk, where agents choose insurance bundles over multiple lines of property coverage, belong to different preference types, display unobserved heterogeneity in attitudes toward risk, and may consider a limited amount of bundles when making their choices. This rich framework is critical for rationalizing data patterns but introduces substantial econometric challenges. The crucial insight consists of exploiting the single crossing property (SCP) that the model features within each coverage context and an exclusion restriction to characterize the response to changes in the covariates of the choice probability of the cheapest bundle. From these elasticities, we can identify the type shares and the distribution of unobserved heterogeneity and consideration sets for each type. I devote the first part of the discussion to summarizing the identification strategy and giving context to the novelty of the arguments. In doing so, I applaud the authors for expertly and smoothly guiding us throughout their overarching research agenda to learn econometric tools that prove extremely useful for the specific setting at hand and, more generally, for employment by structural economists and other applied researchers. In the second part of the discussion, I suggest additional aspects that could play an important empirical role in the functioning of property insurance markets, namely private information about risk and supply-side issues, and pave the way for possible approaches to introduce them into the authors’ framework.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2239870
Levon Barseghyan, Francesca Molinari
Click to increase image sizeClick to decrease image size Notes1 Our analysis, available upon request, allows for endogenous loss probabilities via a linear function of effort, (1−e)μ. The effort level, e, is in turn associated with a (potentially heterogeneous across agents) quadratic cost function. The analysis shows that for deductible levels as in our data, the choice of $200 in collision is not rationalizable, even in the presence of endogenous loss probabilities.
{"title":"Rejoinder","authors":"Levon Barseghyan, Francesca Molinari","doi":"10.1080/07350015.2023.2239870","DOIUrl":"https://doi.org/10.1080/07350015.2023.2239870","url":null,"abstract":"Click to increase image sizeClick to decrease image size Notes1 Our analysis, available upon request, allows for endogenous loss probabilities via a linear function of effort, (1−e)μ. The effort level, e, is in turn associated with a (potentially heterogeneous across agents) quadratic cost function. The analysis shows that for deductible levels as in our data, the choice of $200 in collision is not rationalizable, even in the presence of endogenous loss probabilities.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/07350015.2023.2217870
Elisabeth Honka
Click to increase image sizeClick to decrease image size Disclosure StatementI state that there are no competing interests to declare.Notes1 Throughout the economics and marketing literature, consideration sets have also been called “search sets,” “evoked sets,” or “(endogenous) choice sets.”2 For example, see Hauser and Wernerfelt (Citation1990) for a variety of grocery store products, Roberts and Lattin (Citation1991) for cereal, De los Santos, Hortçsu, and Wildenbeest (Citation2012) for books, Honka (Citation2014) for auto insurance, Koulayev (Citation2014) and Ursu (Citation2018) for hotels, Bronnenberg, Kim, and Mela (Citation2016) for digital cameras, Honka, Hortçsu, and Vitorino (Citation2017) for savings accounts, Ursu, Wang, and Chintagunta (Citation2020) for restaurants, Kapor (Citation2020) for colleges, Yavorsky, Honka, and Chen (Citation2021), Gardete and Hunter (Citation2020), and Moraga-González et al. (Citation2022) for cars, Morozov et al. (Citation2021) for cosmetics, and Zhang et al. (Citation2023) for shoes.3 For example, consumers’ average consideration set sizes are 2.4 for auto insurance (Honka Citation2014), 2.8–6.4 for digital cameras (Bronnenberg, Kim, and Mela Citation2016), 2.5 for savings accounts (Honka, Hortçsu, and Vitorino Citation2017), 2.3 for online used cars (Gardete and Hunter Citation2020), 1.4 for cosmetics (Morozov et al. Citation2021), 1.1 for new car purchases (Yavorsky, Honka, and Chen Citation2021), 1.7 for home improvement products (Amano, Rhodes, and Seiler Citation2022), and 1.9 for shoes (Zhang et al. Citation2023).
单击以增大图像大小，单击以减小图像大小披露声明我声明没有相互竞争的利益需要声明。注1在经济学和市场营销文献中，考虑集也被称为“搜索集”、“诱发集”或“(内生)选择集”。2例如，豪瑟和沃纳菲尔特(Citation1990)为各种食品商店的产品，罗伯茨和拉丁(Citation1991)为谷物，德洛斯桑托斯，hortsu和Wildenbeest (Citation2012)为书籍，Honka (Citation2014)为汽车保险，Koulayev (Citation2014)和Ursu (Citation2018)为酒店，Bronnenberg, Kim和Mela (Citation2016)为数码相机，Honka, hortsu和Vitorino (Citation2017)为储蓄账户，Ursu, Wang和Chintagunta (Citation2020)为餐馆，2 . Kapor (Citation2020)用于大学，Yavorsky, Honka, and Chen (Citation2021)， gardet and Hunter (Citation2020)， Moraga-González等人(Citation2022)用于汽车，Morozov等人(Citation2021)用于化妆品，Zhang等人(Citation2023)用于鞋子例如，消费者对汽车保险的平均考虑集大小为2.4 (Honka Citation2014)，数码相机的2.8-6.4 (Bronnenberg, Kim和Mela Citation2016)，储蓄账户的2.5 (Honka, hortsu和Vitorino Citation2017)，在线二手车的2.3 (Gardete和Hunter Citation2020)，化妆品的1.4 (Morozov等)。新车购买为1.1 (Yavorsky, Honka, and Chen Citation2021)，家装产品为1.7 (Amano, Rhodes, and Seiler Citation2022)，鞋子为1.9 (Zhang et al.)。Citation2023)。
{"title":"Discussion of “Risk Preference Types, Limited Consideration, and Welfare” by Levon Barseghyan and Francesca Molinari","authors":"Elisabeth Honka","doi":"10.1080/07350015.2023.2217870","DOIUrl":"https://doi.org/10.1080/07350015.2023.2217870","url":null,"abstract":"Click to increase image sizeClick to decrease image size Disclosure StatementI state that there are no competing interests to declare.Notes1 Throughout the economics and marketing literature, consideration sets have also been called “search sets,” “evoked sets,” or “(endogenous) choice sets.”2 For example, see Hauser and Wernerfelt (Citation1990) for a variety of grocery store products, Roberts and Lattin (Citation1991) for cereal, De los Santos, Hortçsu, and Wildenbeest (Citation2012) for books, Honka (Citation2014) for auto insurance, Koulayev (Citation2014) and Ursu (Citation2018) for hotels, Bronnenberg, Kim, and Mela (Citation2016) for digital cameras, Honka, Hortçsu, and Vitorino (Citation2017) for savings accounts, Ursu, Wang, and Chintagunta (Citation2020) for restaurants, Kapor (Citation2020) for colleges, Yavorsky, Honka, and Chen (Citation2021), Gardete and Hunter (Citation2020), and Moraga-González et al. (Citation2022) for cars, Morozov et al. (Citation2021) for cosmetics, and Zhang et al. (Citation2023) for shoes.3 For example, consumers’ average consideration set sizes are 2.4 for auto insurance (Honka Citation2014), 2.8–6.4 for digital cameras (Bronnenberg, Kim, and Mela Citation2016), 2.5 for savings accounts (Honka, Hortçsu, and Vitorino Citation2017), 2.3 for online used cars (Gardete and Hunter Citation2020), 1.4 for cosmetics (Morozov et al. Citation2021), 1.1 for new car purchases (Yavorsky, Honka, and Chen Citation2021), 1.7 for home improvement products (Amano, Rhodes, and Seiler Citation2022), and 1.9 for shoes (Zhang et al. Citation2023).","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-26DOI: 10.1080/07350015.2023.2263537
Alexander Kreiss, Enno Mammen, Wolfgang Polonik
AbstractIn statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e. covariates taking the same value for all individuals, such as seasonality) and local, dyadic covariates modeling interactions between two individuals in the network. Existing continuous time network models are extended to allow for comparing a completely parametric model and a model that is parametric only in the local covariates but has a global non-parametric time component. This allows, for instance, to test whether global time dynamics can be explained by simple global covariates like weather, seasonality etc. The procedure is applied to a bike-sharing network by using weather and weekdays as global covariates and distances between the bike stations as local covariates.Keywords: Dynamic NetworksCounting ProcessesDependenceDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"Testing For Global Covariate Effects in Dynamic Interaction Event Networks","authors":"Alexander Kreiss, Enno Mammen, Wolfgang Polonik","doi":"10.1080/07350015.2023.2263537","DOIUrl":"https://doi.org/10.1080/07350015.2023.2263537","url":null,"abstract":"AbstractIn statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e. covariates taking the same value for all individuals, such as seasonality) and local, dyadic covariates modeling interactions between two individuals in the network. Existing continuous time network models are extended to allow for comparing a completely parametric model and a model that is parametric only in the local covariates but has a global non-parametric time component. This allows, for instance, to test whether global time dynamics can be explained by simple global covariates like weather, seasonality etc. The procedure is applied to a bike-sharing network by using weather and weekdays as global covariates and distances between the bike stations as local covariates.Keywords: Dynamic NetworksCounting ProcessesDependenceDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/07350015.2023.2260862
S. Yaser Samadi, H. M. Wiranthe B. Herath
Abstract–The standard vector autoregressive (VAR) models suffer from overparameterization which is a serious issue for high-dimensional time series data as it restricts the number of variables and lags that can be incorporated into the model. Several statistical methods, such as the reduced-rank model for multivariate (multiple) time series (Velu et al., 1986; Reinsel and Velu, 1998; Reinsel et al., 2022) and the Envelope VAR model (Wang and Ding, 2018), provide solutions for achieving dimension reduction of the parameter space of the VAR model. However, these methods can be inefficient in extracting relevant information from complex data, as they fail to distinguish between relevant and irrelevant information, or they are inefficient in addressing the rank deficiency problem. We put together the idea of envelope models into the reduced-rank VAR model to simultaneously tackle these challenges, and propose a new parsimonious version of the classical VAR model called the reduced-rank envelope VAR (REVAR) model. Our proposed REVAR model incorporates the strengths of both reduced-rank VAR and envelope VAR models and leads to significant gains in efficiency and accuracy. The asymptotic properties of the proposed estimators are established under different error assumptions. Simulation studies and real data analysis are conducted to evaluate and illustrate the proposed method.Keywords: Reduced-rank autoregressionEnvelope modelVector autoregressive model.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
标准向量自回归(VAR)模型存在过度参数化问题，这对高维时间序列数据来说是一个严重的问题，因为它限制了可以纳入模型的变量和滞后的数量。几种统计方法，如多元(多)时间序列的降秩模型(Velu et al.， 1986;Reinsel and Velu, 1998;Reinsel et al.， 2022)和Envelope VAR模型(Wang and Ding, 2018)为实现VAR模型参数空间降维提供了解决方案。然而，这些方法在从复杂数据中提取相关信息时效率低下，因为它们不能区分相关和不相关信息，或者它们在解决秩不足问题时效率低下。为了同时解决这些问题，我们将包络模型的思想整合到降阶VAR模型中，并提出了一个新的简化版本的经典VAR模型，称为降阶包络VAR (REVAR)模型。我们提出的REVAR模型结合了降阶VAR和包络VAR模型的优势，并在效率和准确性方面取得了显著的进步。在不同的误差假设下，建立了所提估计量的渐近性质。通过仿真研究和实际数据分析对所提出的方法进行了评价和说明。关键词:降秩自回归;包络模型;矢量自回归模型;免责声明作为对作者和研究人员的服务，我们提供了这个版本的已接受的手稿(AM)。在最终出版版本记录(VoR)之前，将对该手稿进行编辑、排版和审查。在制作和印前，可能会发现可能影响内容的错误，所有适用于期刊的法律免责声明也与这些版本有关。
{"title":"Reduced-rank envelope vector autoregressive model","authors":"S. Yaser Samadi, H. M. Wiranthe B. Herath","doi":"10.1080/07350015.2023.2260862","DOIUrl":"https://doi.org/10.1080/07350015.2023.2260862","url":null,"abstract":"Abstract–The standard vector autoregressive (VAR) models suffer from overparameterization which is a serious issue for high-dimensional time series data as it restricts the number of variables and lags that can be incorporated into the model. Several statistical methods, such as the reduced-rank model for multivariate (multiple) time series (Velu et al., 1986; Reinsel and Velu, 1998; Reinsel et al., 2022) and the Envelope VAR model (Wang and Ding, 2018), provide solutions for achieving dimension reduction of the parameter space of the VAR model. However, these methods can be inefficient in extracting relevant information from complex data, as they fail to distinguish between relevant and irrelevant information, or they are inefficient in addressing the rank deficiency problem. We put together the idea of envelope models into the reduced-rank VAR model to simultaneously tackle these challenges, and propose a new parsimonious version of the classical VAR model called the reduced-rank envelope VAR (REVAR) model. Our proposed REVAR model incorporates the strengths of both reduced-rank VAR and envelope VAR models and leads to significant gains in efficiency and accuracy. The asymptotic properties of the proposed estimators are established under different error assumptions. Simulation studies and real data analysis are conducted to evaluate and illustrate the proposed method.Keywords: Reduced-rank autoregressionEnvelope modelVector autoregressive model.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136153379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/07350015.2023.2260439
Bernd Schwaab, Xin Zhang, Andre Lucas
We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail parameters. We establish parameter regions for stationarity and ergodicity and for the existence of (unconditional) moments and consider conditions for consistency and asymptotic normality of the maximum likelihood estimator for the deterministic parameters in the model. Two empirical datasets illustrate the usefulness of the approach: daily U.S. equity returns, and 15-minute euro area sovereign bond yield changes.
{"title":"Modeling extreme events: time-varying extreme tail shape*","authors":"Bernd Schwaab, Xin Zhang, Andre Lucas","doi":"10.1080/07350015.2023.2260439","DOIUrl":"https://doi.org/10.1080/07350015.2023.2260439","url":null,"abstract":"We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail parameters. We establish parameter regions for stationarity and ergodicity and for the existence of (unconditional) moments and consider conditions for consistency and asymptotic normality of the maximum likelihood estimator for the deterministic parameters in the model. Two empirical datasets illustrate the usefulness of the approach: daily U.S. equity returns, and 15-minute euro area sovereign bond yield changes.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/07350015.2023.2261567
Yong Cai
AbstractSuppose a researcher observes individuals within a county within a state. Given concerns about correlation across individuals, it is common to group observations into clusters and conduct inference treating observations across clusters as independent. However, a researcher that has chosen to cluster at the county level may be unsure of their decision, given knowledge that observations are independent across states. This paper proposes a modified randomization test as a robustness check for the chosen level of clustering in a linear regression setting. Existing tests require either the number of states or number of counties to be large. Our method is designed for settings with few states and few counties. While the method is conservative, it has competitive power in settings that may be relevant to empirical work.Keywords: Linear RegressionClustered Standard ErrorsSmall-Cluster AsymptoticsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
{"title":"A Modified Randomization Test for the Level of Clustering","authors":"Yong Cai","doi":"10.1080/07350015.2023.2261567","DOIUrl":"https://doi.org/10.1080/07350015.2023.2261567","url":null,"abstract":"AbstractSuppose a researcher observes individuals within a county within a state. Given concerns about correlation across individuals, it is common to group observations into clusters and conduct inference treating observations across clusters as independent. However, a researcher that has chosen to cluster at the county level may be unsure of their decision, given knowledge that observations are independent across states. This paper proposes a modified randomization test as a robustness check for the chosen level of clustering in a linear regression setting. Existing tests require either the number of states or number of counties to be large. Our method is designed for settings with few states and few counties. While the method is conservative, it has competitive power in settings that may be relevant to empirical work.Keywords: Linear RegressionClustered Standard ErrorsSmall-Cluster AsymptoticsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1080/07350015.2023.2239949
Levon Barseghyan, Francesca Molinari
AbstractWe provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle. As a first departure from the related literature, the model allows for two preference types. In the first one, agents behave according to standard expected utility theory with CARA Bernoulli utility function, with an agent-specific coefficient of absolute risk aversion whose distribution is left completely unspecified. In the other, agents behave according to the dual theory of choice under risk combined with a one-parameter family distortion function, where the parameter is agent-specific and is drawn from a distribution that is left completely unspecified. Within each preference type, the model allows for unobserved heterogeneity in consideration sets, where the latter form at the bundle level—a second departure from the related literature. Our point identification result rests on observing sufficient variation in covariates across contexts, without requiring any independent variation across alternatives within a single context. We estimate the model on data on households’ deductible choices in two lines of property insurance, and use the results to assess the welfare implications of a hypothetical market intervention where the two lines of insurance are combined into a single one. We study the role of limited consideration in mediating the welfare effects of such intervention.KEYWORDS: (Non-)expected utilityRisk preferencesSemi-nonparametric identificationUnobserved consideration sets AcknowledgmentsWe thank the editor, Ivan Canay, two anonymous reviewers, Matias Cattaneo, Cristina Gualdani, Elisabeth Honka, Xinwei Ma, Yusufcan Masatlioglu, Julie Mortimer, Deborah Doukas, Roberta Olivieri, and conference participants at FUR22 and at the JBES session at the ESWM23 for helpful comments.Disclosure StatementThe authors report there are no competing interests to declare.Notes1 This assumption is sometimes viewed as an aspect of rationality (e.g., Kahneman Citation2003), and is credible in our empirical study of demand in very similar contexts (collision and comprehensive deductible insurance).2 Within a single insurance company, typically in a given context if an agent faces a larger price than another agent for one alternative, the first agent faces a (proportionally) larger price for all other alternatives.3 See Barseghyan, Molinari, and Thirkettle (Citation2021b) for a formal discussion and Section 4.3 for further details.4 See Section 4.2 for additional information on the data.5 The multiplicative factors {glj:l∈Dj} are known as the deductible factors and δj is a small markup known as the expense fee.6 Multiple preference types are a focus of the literature that estimates risk preferences using experimental data (e.g., Bruhin, Fehr-Duda, and Epper Citation2010; Harrison, Humphrey, and Verschoor Citation2010; Conte
摘要我们提供了风险下决策混合模型的半非参数点识别的充分条件，当代理人通过购买捆绑包在多个保险范围(上下文)中进行选择时。作为与相关文献的第一个区别，该模型允许两种偏好类型。在第一种模型中，agent的行为遵循标准期望效用理论，具有CARA Bernoulli效用函数，具有特定于agent的绝对风险厌恶系数，其分布完全不确定。在另一种情况下，智能体根据风险下的二元选择理论和单参数族扭曲函数进行行为，其中参数是特定于智能体的，并且是从完全未指定的分布中提取的。在每种偏好类型中，该模型允许考虑集中存在未观察到的异质性，后者在bundle级别形成——这是与相关文献的第二个不同之处。我们的点识别结果依赖于观察跨上下文的协变量的足够变化，而不需要在单个上下文中跨备选项的任何独立变化。我们根据家庭在两种财产保险中免赔额选择的数据估计模型，并使用结果来评估假设的市场干预对福利的影响，其中两种保险合并为一种。我们研究了有限考虑在调解这种干预的福利效应中的作用。我们感谢编辑Ivan Canay、两位匿名审稿人Matias Cattaneo、Cristina Gualdani、Elisabeth Honka、Xinwei Ma、Yusufcan Masatlioglu、Julie Mortimer、Deborah Doukas、Roberta Olivieri以及FUR22和ESWM23 JBES会议的与会者提供的宝贵意见。声明作者报告无竞争利益需要申报。注1:这一假设有时被视为理性的一个方面(例如，Kahneman Citation2003)，并且在我们对非常相似背景(碰撞和全面免赔保险)的需求的实证研究中是可信的在单个保险公司中，通常在给定的环境中，如果一个代理人在一个选择上面临比另一个代理人更高的价格，那么第一个代理人在所有其他选择上面临(按比例)更高的价格参见Barseghyan, Molinari和Thirkettle (Citation2021b)的正式讨论和4.3节的进一步细节有关数据的更多信息，请参见4.2节乘法因子{glj:l∈Dj}被称为免赔因子，δj是一个小的加价，被称为费用费用多重偏好类型是使用实验数据估计风险偏好的文献的焦点(例如，Bruhin, Fehr-Duda和Epper Citation2010;Harrison, Humphrey, and Verschoor citation; 2010;Conte, Hey, and Moffatt Citation2011)，尽管每种类型的偏好都是同质的，但最多是受一些观察到的人口特征的影响其他以标量参数为特征的偏好包括表现出恒定相对风险厌恶(CRRA)或可忽略的三阶导数(NTD;参见，例如，Cohen和Einav Citation2007;Barseghyan等人。Citation2013)。根据CRRA，代理人的初始财富必须为研究人员所知回想一下，我们的分析条件是μij，因此，偏好的分布可能取决于它Barseghyan等人(Citation2018)审查的所有估计该领域风险偏好的论文都强加了它SCP在许多情况下都是可以满足的，从具有商品的单代理模型(可以根据质量明确订购)到多代理模型(例如，Athey Citation2001)我们假设，当ν和ω具有有界支持时，U1和U0中的效用函数分别对任何实值ν和ω都是定义好的关于SCP可能失败的讨论，请参见Apesteguia和Ballester (Citation2018)回想一下，我们的分析条件是μij，因此，考虑集的分布可能依赖于它结果很容易扩展到两种以上的上下文中，但代价是更沉重的符号参见图3.1及其下面的讨论在我们第4节描述的经验模型中，这个交点对应于ν = 0和ω = 1，即分别没有风险规避和概率扭曲回想一下，这些假设合在一起，意味着任何抽到ν1的代理。相比之下，在这里，通过适当地使用xII中的变化，我们能够创建这样的映射，即使可以有多个首选项类型。 26概率扭曲也有特点，例如，前景理论(卡尼曼和特沃斯基引文，1979;27 . Tversky and Kahneman Citation1992)， Quiggin Citation1982)， Gul (Citation1991)失望厌恶理论，Kőszegi and Rabin (Citation2006, Citation2007)参考依赖效用理论比较各种模型的Vuong试验证实了我们的首选规格的良好拟合除非两者都退化为νi=0和ωi=1.29的净现值计算，否则独立性源于索赔遵循泊松分布的假设，这是在估计索赔概率时强加的(见Barseghyan等人)。Citation2013;Barseghyan, Teitelbaum, and Xu Citation2018).30对附录(A.2) - (A.4)的检验表明，在假设4.4下，假设3.4中的区间[ν*，ν**]和[ω*，ω**]不是单例，则f(·)和g(·)是可识别的或者，我们可以假设，如果实现的考虑集是空的，代理均匀随机地选择D中的一个选项。我们的估计结果对这个建模假设是稳健的正如Barseghyan、Molinari和Thirkettle (Citation2021b)所解释的那样，该数据集是Barseghyan等人(Citation2013)使用的数据集的更新版本。它包含了额外一年的数据信息，并对不同行业的购买时间进行了更严格的限制。这些限制是为了尽量减少由非主动选择引起的潜在偏差，如政策更新和社会经济条件的时间变化类似的事实可以建立，即使在覆盖水平(4.3)的效用函数中添加一个特定类型的噪声项，或者更广泛地说，对于任何遵循Barseghyan, Molinari和Thirkettle (Citation2021b)中正式定义的广义优势概念的模型我们使用子采样是因为参数向量在参数空间的边界上考虑到第4.3节中讨论的数据中的选择模式，这并不奇怪，因为MLE将从未选择的束的考虑概率设置为零回想一下，我们假设($1000，$1000)被考虑的概率是1在充分考虑的情况下，观察到非最佳选择的非零股票的可能性为零。因此，在估计中，我们将每个捆绑包的考虑概率设置为0.99而不是1.00.38。这些结果对选择简单的摇号来衡量支付意愿很敏感。改变赌注将引起欧盟类型的非线性响应，而DT类型的线性响应。39 .改变损失概率会引起DT型的非线性响应，而EU型的线性响应狭义考虑模型的秩相关系数为0.42，而在数据和广义考虑模型下，该系数分别为0.61和0.62。相比之下，在充分考虑的混合Logit模型中，这一相关性为0.45，而在低三角考虑下，这一相关性为0.65.40，参见(Gualdani and Sinha Citation2023, example 2) 41中具有不完全信息的模型由于我们允许多种偏好类型，我们的分析扩展了Barseghyan, Molinari和Thirkettle (Citation2021b)的分析，甚至在考虑跨上下文独立的简化框架中也是如此理论文献中的许多重要论文——包括关于有限注意、有限考虑、理性不注意和其他形式的有限理性下的揭示偏好分析的论文，这些论文在考虑集
{"title":"Risk Preference Types, Limited Consideration, and Welfare","authors":"Levon Barseghyan, Francesca Molinari","doi":"10.1080/07350015.2023.2239949","DOIUrl":"https://doi.org/10.1080/07350015.2023.2239949","url":null,"abstract":"AbstractWe provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle. As a first departure from the related literature, the model allows for two preference types. In the first one, agents behave according to standard expected utility theory with CARA Bernoulli utility function, with an agent-specific coefficient of absolute risk aversion whose distribution is left completely unspecified. In the other, agents behave according to the dual theory of choice under risk combined with a one-parameter family distortion function, where the parameter is agent-specific and is drawn from a distribution that is left completely unspecified. Within each preference type, the model allows for unobserved heterogeneity in consideration sets, where the latter form at the bundle level—a second departure from the related literature. Our point identification result rests on observing sufficient variation in covariates across contexts, without requiring any independent variation across alternatives within a single context. We estimate the model on data on households’ deductible choices in two lines of property insurance, and use the results to assess the welfare implications of a hypothetical market intervention where the two lines of insurance are combined into a single one. We study the role of limited consideration in mediating the welfare effects of such intervention.KEYWORDS: (Non-)expected utilityRisk preferencesSemi-nonparametric identificationUnobserved consideration sets AcknowledgmentsWe thank the editor, Ivan Canay, two anonymous reviewers, Matias Cattaneo, Cristina Gualdani, Elisabeth Honka, Xinwei Ma, Yusufcan Masatlioglu, Julie Mortimer, Deborah Doukas, Roberta Olivieri, and conference participants at FUR22 and at the JBES session at the ESWM23 for helpful comments.Disclosure StatementThe authors report there are no competing interests to declare.Notes1 This assumption is sometimes viewed as an aspect of rationality (e.g., Kahneman Citation2003), and is credible in our empirical study of demand in very similar contexts (collision and comprehensive deductible insurance).2 Within a single insurance company, typically in a given context if an agent faces a larger price than another agent for one alternative, the first agent faces a (proportionally) larger price for all other alternatives.3 See Barseghyan, Molinari, and Thirkettle (Citation2021b) for a formal discussion and Section 4.3 for further details.4 See Section 4.2 for additional information on the data.5 The multiplicative factors {glj:l∈Dj} are known as the deductible factors and δj is a small markup known as the expense fee.6 Multiple preference types are a focus of the literature that estimates risk preferences using experimental data (e.g., Bruhin, Fehr-Duda, and Epper Citation2010; Harrison, Humphrey, and Verschoor Citation2010; Conte","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135110688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1080/07350015.2023.2257270
Matteo Barigozzi, Haeran Cho, Dom Owens
–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i) factor-adjustment via dynamic principal component analysis, (ii) estimation of the latent VAR process via-regularised Yule-Walker estimator, and (iii) estimation of partial correlation and long-run partial correlation matrices. In doing so, we learn three networks underpinning the VAR process, namely a directed network representing the Granger causal linkages between the variables, an undirected one embedding their contemporaneous relationships and finally, an undirected network that summarises both lead-lag and contemporaneous linkages. In addition, FNETS provides a suite of methods for forecasting the factor-driven and the idiosyncratic VAR processes. Under general conditions permitting tails heavier than the Gaussian one, we derive uniform consistency rates for the estimators in both network estimation and forecasting, which hold as the dimension of the panel and the sample size diverge. Simulation studies and real data application confirm the good performance of FNETS.
{"title":"FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series","authors":"Matteo Barigozzi, Haeran Cho, Dom Owens","doi":"10.1080/07350015.2023.2257270","DOIUrl":"https://doi.org/10.1080/07350015.2023.2257270","url":null,"abstract":"–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i) factor-adjustment via dynamic principal component analysis, (ii) estimation of the latent VAR process via-regularised Yule-Walker estimator, and (iii) estimation of partial correlation and long-run partial correlation matrices. In doing so, we learn three networks underpinning the VAR process, namely a directed network representing the Granger causal linkages between the variables, an undirected one embedding their contemporaneous relationships and finally, an undirected network that summarises both lead-lag and contemporaneous linkages. In addition, FNETS provides a suite of methods for forecasting the factor-driven and the idiosyncratic VAR processes. Under general conditions permitting tails heavier than the Gaussian one, we derive uniform consistency rates for the estimators in both network estimation and forecasting, which hold as the dimension of the panel and the sample size diverge. Simulation studies and real data application confirm the good performance of FNETS.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135487683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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