What can contracts—traded and priced in a competitive market and featuring a pre-specified system of future payments—teach us about time preferences and present bias? We first show that identification of present bias requires assumptions on the felicity function and that agents must have credit constraints on consumption expenditure. Moreover, when there is heterogeneity in present bias, identification requires that agents with the same present bias parameter buy houses with different contracts. We illustrate our findings with observational land-lease-contract data from Amsterdam.
{"title":"Identifying present bias and time preferences with an application to land-lease-contract data1","authors":"P. Gautier, A. Vuuren","doi":"10.1093/ectj/utaa018","DOIUrl":"https://doi.org/10.1093/ectj/utaa018","url":null,"abstract":"\u0000 What can contracts—traded and priced in a competitive market and featuring a pre-specified system of future payments—teach us about time preferences and present bias? We first show that identification of present bias requires assumptions on the felicity function and that agents must have credit constraints on consumption expenditure. Moreover, when there is heterogeneity in present bias, identification requires that agents with the same present bias parameter buy houses with different contracts. We illustrate our findings with observational land-lease-contract data from Amsterdam.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"23 1","pages":"363-385"},"PeriodicalIF":1.9,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46645867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When a sample is drawn from or coincides with a finite population, the uncertainty of the coefficient estimators is often reported assuming the population is effectively infinite. The recent literature on finite-population inference instead derives an alternative asymptotic variance of the ordinary least squares estimator. Here, I extend the results to the more general setting of M-estimators and also find that the usual robust ‘sandwich’ estimator is conservative. The proposed asymptotic variance of M-estimators accounts for two sources of variation. In addition to the usual sampling-based uncertainty arising from (possibly) not observing the entire population, there is also design-based uncertainty, which is usually ignored in the common inference method, resulting from lack of knowledge of the counterfactuals. Under this alternative framework, we can obtain smaller standard errors of M-estimators when the population is treated as finite.
{"title":"Potential outcomes and finite-population inference for M-estimators","authors":"Ruonan Xu","doi":"10.1093/ectj/utaa022","DOIUrl":"https://doi.org/10.1093/ectj/utaa022","url":null,"abstract":"\u0000 When a sample is drawn from or coincides with a finite population, the uncertainty of the coefficient estimators is often reported assuming the population is effectively infinite. The recent literature on finite-population inference instead derives an alternative asymptotic variance of the ordinary least squares estimator. Here, I extend the results to the more general setting of M-estimators and also find that the usual robust ‘sandwich’ estimator is conservative. The proposed asymptotic variance of M-estimators accounts for two sources of variation. In addition to the usual sampling-based uncertainty arising from (possibly) not observing the entire population, there is also design-based uncertainty, which is usually ignored in the common inference method, resulting from lack of knowledge of the counterfactuals. Under this alternative framework, we can obtain smaller standard errors of M-estimators when the population is treated as finite.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45188016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the literature, a common feature of panel models with interactive fixed effects is that they model a univariate variable. In this regard, they are incapable of addressing dynamic and simultaneous interactions among a set of macroeconomic variables, a problem that falls within the realm of structural analysis. This paper aims to contribute to the existing literature by studying a homogeneous panel vector autoregression (VAR) model with interactive fixed effects. The panel VAR model in question is flexible in that it can accommodate an arbitrary lag length and observable regressors that can be individual-specific or common. For factor VAR models with both a large cross-section (C) and a large time (T) dimension, we derive the limiting distribution of the interactive fixed estimator, allowing structural analysis to be extended to panel VAR models with interactive fixed effects.
{"title":"Panel VAR models with interactive fixed effects","authors":"M. Tuğan","doi":"10.1093/ectj/utaa021","DOIUrl":"https://doi.org/10.1093/ectj/utaa021","url":null,"abstract":"\u0000 In the literature, a common feature of panel models with interactive fixed effects is that they model a univariate variable. In this regard, they are incapable of addressing dynamic and simultaneous interactions among a set of macroeconomic variables, a problem that falls within the realm of structural analysis. This paper aims to contribute to the existing literature by studying a homogeneous panel vector autoregression (VAR) model with interactive fixed effects. The panel VAR model in question is flexible in that it can accommodate an arbitrary lag length and observable regressors that can be individual-specific or common. For factor VAR models with both a large cross-section (C) and a large time (T) dimension, we derive the limiting distribution of the interactive fixed estimator, allowing structural analysis to be extended to panel VAR models with interactive fixed effects.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41429432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider the problem of binary classification with covariate selection. We construct a classification procedure by minimising the empirical misclassification risk with a penalty on the number of selected covariates. This optimisation problem is equivalent to obtaining an ℓ0-penalised maximum score estimator. We derive probability bounds on the estimated sparsity as well as on the excess misclassification risk. These theoretical results are nonasymptotic and established in a high-dimensional setting. In particular, we show that our method yields a sparse solution whose ℓ0-norm can be arbitrarily close to true sparsity with high probability and obtain the rates of convergence for the excess misclassification risk. We implement the proposed procedure via the method of mixed-integer linear programming. Its numerical performance is illustrated in Monte Carlo experiments and a real data application of the work-trip transportation mode choice.
{"title":"Binary classification with covariate selection through ℓ0-penalised empirical risk minimisation","authors":"Le‐Yu Chen, S. Lee","doi":"10.1093/ectj/utaa017","DOIUrl":"https://doi.org/10.1093/ectj/utaa017","url":null,"abstract":"\u0000 We consider the problem of binary classification with covariate selection. We construct a classification procedure by minimising the empirical misclassification risk with a penalty on the number of selected covariates. This optimisation problem is equivalent to obtaining an ℓ0-penalised maximum score estimator. We derive probability bounds on the estimated sparsity as well as on the excess misclassification risk. These theoretical results are nonasymptotic and established in a high-dimensional setting. In particular, we show that our method yields a sparse solution whose ℓ0-norm can be arbitrarily close to true sparsity with high probability and obtain the rates of convergence for the excess misclassification risk. We implement the proposed procedure via the method of mixed-integer linear programming. Its numerical performance is illustrated in Monte Carlo experiments and a real data application of the work-trip transportation mode choice.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48273157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is well known that the sample correlation coefficient between many financial return indices exhibit substantial variation on any reasonable sampling window. This stylized fact contradicts a unit root model for the underlying processes in levels, as the statistic converges in probability to a constant under this modeling scheme. In this paper we establish asymptotic theory for regression in local stochastic unit root (LSTUR) variables. An empirical application reveals that the new theory explains very well the instability, in both sign and scale, of the sample correlation coefficient, between gold, oil and stock return price indices. In addition, we establish spurious regression theory for LSTUR variables, which generalizes the results known hitherto, as well as theory for balanced regression in this setting.
{"title":"LSTUR regression theory and the instability of the sample correlation coefficient between financial return indices","authors":"Timofey Ginker, Offer Lieberman","doi":"10.1093/ectj/utaa011","DOIUrl":"https://doi.org/10.1093/ectj/utaa011","url":null,"abstract":"It is well known that the sample correlation coefficient between many financial return indices exhibit substantial variation on any reasonable sampling window. This stylized fact contradicts a unit root model for the underlying processes in levels, as the statistic converges in probability to a constant under this modeling scheme. In this paper we establish asymptotic theory for regression in local stochastic unit root (LSTUR) variables. An empirical application reveals that the new theory explains very well the instability, in both sign and scale, of the sample correlation coefficient, between gold, oil and stock return price indices. In addition, we establish spurious regression theory for LSTUR variables, which generalizes the results known hitherto, as well as theory for balanced regression in this setting.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41790293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable and the unobservable. We propose an estimation method, and establish the consistency and asymptotic normality for the proposed estimator. Simulation results illustrate the good performance of our estimator in a finite sample. We apply the proposed method to bilateral trade issues of the U.S.A. and foreign countries.
{"title":"Semiparametric estimation of generalized transformation panel data models with nonstationary error","authors":"Xi Wang, Songnian Chen","doi":"10.1093/ectj/utaa009","DOIUrl":"https://doi.org/10.1093/ectj/utaa009","url":null,"abstract":"\u0000 Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable and the unobservable. We propose an estimation method, and establish the consistency and asymptotic normality for the proposed estimator. Simulation results illustrate the good performance of our estimator in a finite sample. We apply the proposed method to bilateral trade issues of the U.S.A. and foreign countries.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"23 1","pages":"386-402"},"PeriodicalIF":1.9,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44336092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anil K. Bera, Gabriel Montes-Rojas, W. Sosa-Escudero, Javier Alejo
This paper develops generalized method of moments-based (GMM-based) Lagrange multiplier tests for nonlinear hypotheses that are robust to locally misspecified possibly nonlinear alternatives. The procedure is based on an initial consistent GMM estimator of the parameters under a given set of nonlinear restrictions. The new test for one particular set of nonlinear hypotheses is consistent and has correct asymptotic size independently of whether the other, also nonlinear hypotheses, are correct or locally misspecified. To illustrate the usefulness of our proposed tests we consider testing rational expectations hypotheses using U.S. data.
{"title":"Tests for nonlinear restrictions under misspecified alternatives with an application to testing rational expectation hypotheses","authors":"Anil K. Bera, Gabriel Montes-Rojas, W. Sosa-Escudero, Javier Alejo","doi":"10.1093/ectj/utaa010","DOIUrl":"https://doi.org/10.1093/ectj/utaa010","url":null,"abstract":"\u0000 This paper develops generalized method of moments-based (GMM-based) Lagrange multiplier tests for nonlinear hypotheses that are robust to locally misspecified possibly nonlinear alternatives. The procedure is based on an initial consistent GMM estimator of the parameters under a given set of nonlinear restrictions. The new test for one particular set of nonlinear hypotheses is consistent and has correct asymptotic size independently of whether the other, also nonlinear hypotheses, are correct or locally misspecified. To illustrate the usefulness of our proposed tests we consider testing rational expectations hypotheses using U.S. data.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utaa010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42775938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.
{"title":"Partial identification in nonseparable count data instrumental variable models","authors":"Dongwoo Kim","doi":"10.1093/ectj/utz025","DOIUrl":"https://doi.org/10.1093/ectj/utz025","url":null,"abstract":"\u0000 This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"23 1","pages":"232-250"},"PeriodicalIF":1.9,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utz025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45051295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study accelerated failure time (AFT) models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly-used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semi-parametric maximum likelihood estimates of the finite dimensional parameter and establish the large sample properties. The shape restriction is incorporated via a nonparametric maximum likelihood estimator (NPMLE) of the hazard function. Our approach guarantees the uniqueness of a global solution for the estimating equations and delivers semiparametric efficient estimates. Simulation studies and empirical applications demonstrate the usefulness of our method.
{"title":"Accelerated failure time models with log-concave errors","authors":"Ruixuan Liu, Zhengfei Yu","doi":"10.1093/ectj/utz024","DOIUrl":"https://doi.org/10.1093/ectj/utz024","url":null,"abstract":"We study accelerated failure time (AFT) models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly-used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semi-parametric maximum likelihood estimates of the finite dimensional parameter and establish the large sample properties. The shape restriction is incorporated via a nonparametric maximum likelihood estimator (NPMLE) of the hazard function. Our approach guarantees the uniqueness of a global solution for the estimating equations and delivers semiparametric efficient estimates. Simulation studies and empirical applications demonstrate the usefulness of our method.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":"23 1","pages":"251-268"},"PeriodicalIF":1.9,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ectj/utz024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41559542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging.
{"title":"Generalized Forecast Averaging in Autoregressions with a Near Unit Root","authors":"Mohitosh Kejriwal, Xuewen Yu","doi":"10.1093/ECTJ/UTAA006","DOIUrl":"https://doi.org/10.1093/ECTJ/UTAA006","url":null,"abstract":"This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging.","PeriodicalId":50555,"journal":{"name":"Econometrics Journal","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/ECTJ/UTAA006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42068710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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