Pub Date : 2024-05-21DOI: 10.1017/s0266466624000173
Jungyoon Lee, Peter C. B. Phillips, Francesca Rossi
Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops a new specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, the functional form and the model itself. The test is robust to the presence of heteroskedasticity of unknown form in the disturbances and the approach relates to the conditional moment test framework of Bierens ([1982, Journal of Econometrics 20, 105–134], [1990, Econometrica 58, 1443–1458]). The Bierens test is shown to be inconsistent in general against spatial alternatives and the new test introduces modifications to achieve test consistency in the spatial setting. A central element is the infinite-dimensional endogeneity induced by spatial linkages. This complexity is addressed by introducing a new component to the omnibus test that captures the effects of potential spatial matrix misspecification. With this modification, the approach leads to a simple pivotal test procedure with standard critical values that is the first test in the literature to have power against misspecifications in the spatial linkages. We derive the asymptotic distribution of the test under the null hypothesis of correct SAR specification and prove consistency. A Monte Carlo study is conducted to study its finite sample performance. An empirical illustration on the performance of the test in modeling tax competition in Finland is included.
空间自回归（SAR）及相关模型为空间和网络互动建模提供了灵活而简洁的方法。SAR 规范通常依赖于特定的参数函数形式和所谓空间权重矩阵的外生选择，在制定这些规范时只有有限的理论指导。此外，在空间杜宾模型（SD）或空间滞后 X 模型（SLX）等其他替代模型中选择 SAR 模型往往是任意的，从而引发了潜在的规范误差问题。为了解决这些问题，本文在 SAR 框架内开发了一种新的规范检验方法，可以检测出一般形式的规范错误，包括空间权重矩阵、函数形式和模型本身的规范错误。该检验对扰动中存在的未知形式的异方差具有鲁棒性，其方法与 Bierens 的条件矩检验框架有关（[1982 年，《计量经济学杂志》20，105-134]，[1990 年，《计量经济学》58，1443-1458]）。比伦斯检验在一般情况下与空间检验不一致，新检验引入了一些修改，以实现空间检验的一致性。一个核心要素是空间联系引起的无限维内生性。为了解决这一复杂性，我们在综合测试中引入了一个新的组成部分，以捕捉潜在的空间矩阵规格错误的影响。通过这一修改，该方法产生了一个具有标准临界值的简单枢轴检验程序，这是文献中第一个对空间关联中的误规范具有威力的检验。我们推导了该检验在 SAR 规格正确的零假设下的渐近分布，并证明了其一致性。我们进行了蒙特卡罗研究，以研究其有限样本性能。此外，我们还对该检验在芬兰税收竞争模型中的表现进行了实证说明。
{"title":"HETEROSKEDASTICITY ROBUST SPECIFICATION TESTING IN SPATIAL AUTOREGRESSION","authors":"Jungyoon Lee, Peter C. B. Phillips, Francesca Rossi","doi":"10.1017/s0266466624000173","DOIUrl":"https://doi.org/10.1017/s0266466624000173","url":null,"abstract":"Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops a new specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, the functional form and the model itself. The test is robust to the presence of heteroskedasticity of unknown form in the disturbances and the approach relates to the conditional moment test framework of Bierens ([1982, Journal of Econometrics 20, 105–134], [1990, Econometrica 58, 1443–1458]). The Bierens test is shown to be inconsistent in general against spatial alternatives and the new test introduces modifications to achieve test consistency in the spatial setting. A central element is the infinite-dimensional endogeneity induced by spatial linkages. This complexity is addressed by introducing a new component to the omnibus test that captures the effects of potential spatial matrix misspecification. With this modification, the approach leads to a simple pivotal test procedure with standard critical values that is the first test in the literature to have power against misspecifications in the spatial linkages. We derive the asymptotic distribution of the test under the null hypothesis of correct SAR specification and prove consistency. A Monte Carlo study is conducted to study its finite sample performance. An empirical illustration on the performance of the test in modeling tax competition in Finland is included.","PeriodicalId":502648,"journal":{"name":"Econometric Theory","volume":"126 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115219","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}
Pub Date : 2024-05-20DOI: 10.1017/s0266466624000148
Alessandro Iaria, Ao Wang
We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates nonparametric and semi-nonparametric identification, extrapolation to hypothetical counterfactuals, numerical implementation of demand inverses, and numerical implementation of the maximum likelihood estimator.
{"title":"REAL ANALYTIC DISCRETE CHOICE MODELS OF DEMAND: THEORY AND IMPLICATIONS","authors":"Alessandro Iaria, Ao Wang","doi":"10.1017/s0266466624000148","DOIUrl":"https://doi.org/10.1017/s0266466624000148","url":null,"abstract":"We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates nonparametric and semi-nonparametric identification, extrapolation to hypothetical counterfactuals, numerical implementation of demand inverses, and numerical implementation of the maximum likelihood estimator.","PeriodicalId":502648,"journal":{"name":"Econometric Theory","volume":"38 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120242","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}
Pub Date : 2024-05-03DOI: 10.1017/s0266466624000136
Peter C. B. Phillips
{"title":"THE ECONOMETRIC THEORY AWARDS 2024","authors":"Peter C. B. Phillips","doi":"10.1017/s0266466624000136","DOIUrl":"https://doi.org/10.1017/s0266466624000136","url":null,"abstract":"","PeriodicalId":502648,"journal":{"name":"Econometric Theory","volume":"27 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016974","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}
Pub Date : 2024-04-15DOI: 10.1017/s0266466624000124
M. Bun, F. Kleibergen
{"title":"IDENTIFICATION ROBUST INFERENCE FOR MOMENTS-BASED ANALYSIS OF LINEAR DYNAMIC PANEL DATA MODELS – ADDENDUM","authors":"M. Bun, F. Kleibergen","doi":"10.1017/s0266466624000124","DOIUrl":"https://doi.org/10.1017/s0266466624000124","url":null,"abstract":"","PeriodicalId":502648,"journal":{"name":"Econometric Theory","volume":"48 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701810","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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