Pub Date : 2023-05-12DOI: 10.1080/07474938.2023.2205339
Offer Lieberman, Francesca Rossi
Abstract In this article, we develop asymptotic theory for a spatial autoregressive (SAR) model where the network structure is defined according to a similarity-based weight matrix, in line with the similarity theory, which in turn has an axiomatic justification. We prove consistency of the quasi-maximum-likelihood estimator and derive its limit distribution. The contribution of this article is two-fold: on one hand, we incorporate a regression component in the data generating process while allowing the similarity structure to accommodate non-ordered data and by estimating explicitly the weight of the similarity, allowing it to be equal to unity. On the other hand, this work complements the literature on SAR models by adopting a data-driven weight matrix which depends on a finite set of parameters that have to be estimated. The spatial parameter, which corresponds to the weight of the similarity structure, is in turn allowed to take values at the boundary of the standard SAR parameter space. In addition, our setup accommodates strong forms of cross-sectional correlation that are normally ruled out in the standard SAR literature. Our framework is general enough to include as special cases also the random walk with a drift model, the local to unit root model (LUR) with a drift and the model for moderate integration with a drift.
{"title":"Inference in a similarity-based spatial autoregressive model","authors":"Offer Lieberman, Francesca Rossi","doi":"10.1080/07474938.2023.2205339","DOIUrl":"https://doi.org/10.1080/07474938.2023.2205339","url":null,"abstract":"Abstract In this article, we develop asymptotic theory for a spatial autoregressive (SAR) model where the network structure is defined according to a similarity-based weight matrix, in line with the similarity theory, which in turn has an axiomatic justification. We prove consistency of the quasi-maximum-likelihood estimator and derive its limit distribution. The contribution of this article is two-fold: on one hand, we incorporate a regression component in the data generating process while allowing the similarity structure to accommodate non-ordered data and by estimating explicitly the weight of the similarity, allowing it to be equal to unity. On the other hand, this work complements the literature on SAR models by adopting a data-driven weight matrix which depends on a finite set of parameters that have to be estimated. The spatial parameter, which corresponds to the weight of the similarity structure, is in turn allowed to take values at the boundary of the standard SAR parameter space. In addition, our setup accommodates strong forms of cross-sectional correlation that are normally ruled out in the standard SAR literature. Our framework is general enough to include as special cases also the random walk with a drift model, the local to unit root model (LUR) with a drift and the model for moderate integration with a drift.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"471 - 486"},"PeriodicalIF":1.2,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46175633","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}
Pub Date : 2023-04-21DOI: 10.1080/07474938.2023.2178141
M. Wagner, Karsten Reichold
Abstract We develop group-mean fully modified OLS (FM-OLS) estimation and inference for panels of cointegrating polynomial regressions, i.e., regressions that include an integrated process and its powers as explanatory variables. The stationary errors are allowed to be serially correlated, the integrated regressors – allowed to contain drifts – to be endogenous and, as usual in the panel literature, we include individual-specific fixed effects and also allow for individual-specific time trends. We consider a fixed cross-section dimension and asymptotics in the time dimension only. Within this setting, we develop cross-section dependence robust inference for the group-mean estimator. In both the simulations and an illustrative application estimating environmental Kuznets curves (EKCs) for carbon dioxide emissions we compare our group-mean FM-OLS approach with a recently proposed pooled FM-OLS approach of de Jong and Wagner.
{"title":"Panel cointegrating polynomial regressions: group-mean fully modified OLS estimation and inference","authors":"M. Wagner, Karsten Reichold","doi":"10.1080/07474938.2023.2178141","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178141","url":null,"abstract":"Abstract We develop group-mean fully modified OLS (FM-OLS) estimation and inference for panels of cointegrating polynomial regressions, i.e., regressions that include an integrated process and its powers as explanatory variables. The stationary errors are allowed to be serially correlated, the integrated regressors – allowed to contain drifts – to be endogenous and, as usual in the panel literature, we include individual-specific fixed effects and also allow for individual-specific time trends. We consider a fixed cross-section dimension and asymptotics in the time dimension only. Within this setting, we develop cross-section dependence robust inference for the group-mean estimator. In both the simulations and an illustrative application estimating environmental Kuznets curves (EKCs) for carbon dioxide emissions we compare our group-mean FM-OLS approach with a recently proposed pooled FM-OLS approach of de Jong and Wagner.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"358 - 392"},"PeriodicalIF":1.2,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49442402","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}
Pub Date : 2023-04-21DOI: 10.1080/07474938.2023.2191105
Hao Dong, Taisuke Otsu, L. Taylor
Abstract We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.
{"title":"Bandwidth selection for nonparametric regression with errors-in-variables","authors":"Hao Dong, Taisuke Otsu, L. Taylor","doi":"10.1080/07474938.2023.2191105","DOIUrl":"https://doi.org/10.1080/07474938.2023.2191105","url":null,"abstract":"Abstract We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"393 - 419"},"PeriodicalIF":1.2,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43893953","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}
Pub Date : 2023-04-21DOI: 10.1080/07474938.2023.2178137
R. Startz, D. Steigerwald
Abstract Statistical inference in economics is commonly based on formulas assuming infinite populations. We present appropriate formulas for use when sampling from finite populations, with special attention given to issues of treatment effects and to issues of clustering. Issues of whether to apply finite population corrections are often subtle, and appropriate corrections may depend on difficult to observe parameters, leaving the investigator only with bounds on relevant estimator variances.
{"title":"Inference and extrapolation in finite populations with special attention to clustering","authors":"R. Startz, D. Steigerwald","doi":"10.1080/07474938.2023.2178137","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178137","url":null,"abstract":"Abstract Statistical inference in economics is commonly based on formulas assuming infinite populations. We present appropriate formulas for use when sampling from finite populations, with special attention given to issues of treatment effects and to issues of clustering. Issues of whether to apply finite population corrections are often subtle, and appropriate corrections may depend on difficult to observe parameters, leaving the investigator only with bounds on relevant estimator variances.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"343 - 357"},"PeriodicalIF":1.2,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44395802","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}
Pub Date : 2023-03-30DOI: 10.1080/07474938.2023.2222637
C. Grazian, Alex McInnes
Abstract This work examines how the dependence structures between energy futures asset prices differ in two periods identified before and after the 2008 global financial crisis. These two periods were characterized by a difference in the number of extraordinary meetings of OPEC countries organized to announce a change of oil production. In the period immediately following the global financial crisis, the decrease in oil prices and oil and gas demand forced OPEC countries to make frequent adjustments to the production of oil, while, since the first quarter of 2010, the recovery led to more regular meetings, with only three organized extraordinary meetings. We propose to use a copula model to study how the dependence structure among energy prices changed among the two periods. The use of copula models allows to introduce flexible and realistic models for the marginal time series; once marginal parameters are estimated, the estimates are used to fit several copula models for all asset combinations. Model selection techniques based on information criteria are implemented to choose the best models both for the univariate asset prices series and for the distribution of co-movements. The changes in the dependence structure of couple of assets are investigated through copula functionals and their uncertainty estimated through a bootstrapping method. We find the strength of dependence between asset combinations considerably differ between the two periods, showing a significant decrease for all the pairs of assets.
{"title":"An application of copulas to OPEC’s changing influence on fossil fuel prices","authors":"C. Grazian, Alex McInnes","doi":"10.1080/07474938.2023.2222637","DOIUrl":"https://doi.org/10.1080/07474938.2023.2222637","url":null,"abstract":"Abstract This work examines how the dependence structures between energy futures asset prices differ in two periods identified before and after the 2008 global financial crisis. These two periods were characterized by a difference in the number of extraordinary meetings of OPEC countries organized to announce a change of oil production. In the period immediately following the global financial crisis, the decrease in oil prices and oil and gas demand forced OPEC countries to make frequent adjustments to the production of oil, while, since the first quarter of 2010, the recovery led to more regular meetings, with only three organized extraordinary meetings. We propose to use a copula model to study how the dependence structure among energy prices changed among the two periods. The use of copula models allows to introduce flexible and realistic models for the marginal time series; once marginal parameters are estimated, the estimates are used to fit several copula models for all asset combinations. Model selection techniques based on information criteria are implemented to choose the best models both for the univariate asset prices series and for the distribution of co-movements. The changes in the dependence structure of couple of assets are investigated through copula functionals and their uncertainty estimated through a bootstrapping method. We find the strength of dependence between asset combinations considerably differ between the two periods, showing a significant decrease for all the pairs of assets.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"676 - 699"},"PeriodicalIF":1.2,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44258807","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}
Pub Date : 2023-02-07DOI: 10.1080/07474938.2023.2178136
Yong Bao
Abstract This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less.
{"title":"Indirect inference estimation of higher-order spatial autoregressive models","authors":"Yong Bao","doi":"10.1080/07474938.2023.2178136","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178136","url":null,"abstract":"Abstract This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"247 - 280"},"PeriodicalIF":1.2,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46767911","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}
Pub Date : 2023-02-07DOI: 10.1080/07474938.2023.2178140
Sungho Noh
Abstract In this article, I propose a nonparametric strategy to identify the distribution of heterogeneous causal effects. A set of identification restrictions proposed in this article differs from existing approaches in three ways. First, it extends the random coefficient model by allowing potentially nonlinear interactions between distributional parameters and the set of covariates. Second, the causal effect distributions identified in this article give an alternative to those under the rank invariance assumption. Third, identified distribution lies within the sharp bound of distributions of the treatment effect. I develop a consistent nonparametric estimator exploiting the identifying restriction by extending the conventional statistical deconvolution method to the Rubin causal framework. Results from a Monte Carlo experiment and an application to wage loss of displaced workers suggest that the method yields robust estimates under various scenarios.
{"title":"Nonparametric identification and estimation of heterogeneous causal effects under conditional independence","authors":"Sungho Noh","doi":"10.1080/07474938.2023.2178140","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178140","url":null,"abstract":"Abstract In this article, I propose a nonparametric strategy to identify the distribution of heterogeneous causal effects. A set of identification restrictions proposed in this article differs from existing approaches in three ways. First, it extends the random coefficient model by allowing potentially nonlinear interactions between distributional parameters and the set of covariates. Second, the causal effect distributions identified in this article give an alternative to those under the rank invariance assumption. Third, identified distribution lies within the sharp bound of distributions of the treatment effect. I develop a consistent nonparametric estimator exploiting the identifying restriction by extending the conventional statistical deconvolution method to the Rubin causal framework. Results from a Monte Carlo experiment and an application to wage loss of displaced workers suggest that the method yields robust estimates under various scenarios.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"307 - 341"},"PeriodicalIF":1.2,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42222895","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}
Pub Date : 2023-02-01DOI: 10.1080/07474938.2023.2178088
E. Beutner, Yicong Lin, Stephan Smeekes
Abstract We develop a Feasible Generalized Least Squares estimator of the date of a structural break in level and/or trend. The estimator is based on a consistent estimate of a T-dimensional inverse autocovariance matrix. A cubic polynomial transformation of break date estimates can be approximated by a nonstandard yet nuisance parameter free distribution asymptotically. The new limiting distribution captures the asymmetry and bimodality in finite samples and is applicable for inference with a single, known, set of critical values. We consider the confidence intervals/sets for break dates based on both Wald-type tests and by inverting multiple likelihood ratio (LR) tests. A simulation study shows that the proposed estimator increases the empirical concentration probability in a small neighborhood of the true break date and potentially reduces the mean squared errors. The LR-based confidence intervals/sets have good coverage while maintaining informative length even with highly persistent errors and small break sizes.
{"title":"GLS estimation and confidence sets for the date of a single break in models with trends","authors":"E. Beutner, Yicong Lin, Stephan Smeekes","doi":"10.1080/07474938.2023.2178088","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178088","url":null,"abstract":"Abstract We develop a Feasible Generalized Least Squares estimator of the date of a structural break in level and/or trend. The estimator is based on a consistent estimate of a T-dimensional inverse autocovariance matrix. A cubic polynomial transformation of break date estimates can be approximated by a nonstandard yet nuisance parameter free distribution asymptotically. The new limiting distribution captures the asymmetry and bimodality in finite samples and is applicable for inference with a single, known, set of critical values. We consider the confidence intervals/sets for break dates based on both Wald-type tests and by inverting multiple likelihood ratio (LR) tests. A simulation study shows that the proposed estimator increases the empirical concentration probability in a small neighborhood of the true break date and potentially reduces the mean squared errors. The LR-based confidence intervals/sets have good coverage while maintaining informative length even with highly persistent errors and small break sizes.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"195 - 219"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46298047","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}
Pub Date : 2023-02-01DOI: 10.1080/07474938.2023.2178139
B. Baltagi
Abstract Mundlak shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once the individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Wooldridge also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends Baltagi’s results from the one-way to the two-way error component model. The F test suggested by Mundlak to test for this correlation between the random effects and the regressors generate a Hausman type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang for the two-way error component model.
{"title":"The two-way Mundlak estimator","authors":"B. Baltagi","doi":"10.1080/07474938.2023.2178139","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178139","url":null,"abstract":"Abstract Mundlak shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once the individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Wooldridge also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends Baltagi’s results from the one-way to the two-way error component model. The F test suggested by Mundlak to test for this correlation between the random effects and the regressors generate a Hausman type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang for the two-way error component model.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"240 - 246"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46984968","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}
Pub Date : 2023-02-01DOI: 10.1080/07474938.2023.2178087
Fei Jin, Lung-fei Lee, Jihai Yu
Abstract This article investigates asymptotic properties of quasi-maximum likelihood (QML) estimates for flow data on the dual gravity model in international trade with spatial interactions (dependence). The dual gravity model has a well-established economic foundation, and it takes the form of a spatial autoregressive (SAR) model. The dual gravity model originates from Behrens et al., but the spatial weights matrix motivated by their economic theory has a feature that violates existing regularity conditions for asymptotic econometrics analysis. By overcoming the limitations of existing asymptotic theory, we show that QML estimates are consistent and asymptotically normal. The simulation results show the satisfactory finite sample performance of the estimates. We illustrate the usefulness of the model by investigating the McCallum “border puzzle” in the gravity literature.
{"title":"Estimating flow data models of international trade: dual gravity and spatial interactions","authors":"Fei Jin, Lung-fei Lee, Jihai Yu","doi":"10.1080/07474938.2023.2178087","DOIUrl":"https://doi.org/10.1080/07474938.2023.2178087","url":null,"abstract":"Abstract This article investigates asymptotic properties of quasi-maximum likelihood (QML) estimates for flow data on the dual gravity model in international trade with spatial interactions (dependence). The dual gravity model has a well-established economic foundation, and it takes the form of a spatial autoregressive (SAR) model. The dual gravity model originates from Behrens et al., but the spatial weights matrix motivated by their economic theory has a feature that violates existing regularity conditions for asymptotic econometrics analysis. By overcoming the limitations of existing asymptotic theory, we show that QML estimates are consistent and asymptotically normal. The simulation results show the satisfactory finite sample performance of the estimates. We illustrate the usefulness of the model by investigating the McCallum “border puzzle” in the gravity literature.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"157 - 194"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46608035","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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