Pub Date : 2023-07-12DOI: 10.1080/07474938.2023.2225947
Fang Lu, Sisheng Liu, Jing Yang, Xuewen Lu
Abstract This article studies the generalized method of moment estimation of semiparametric varying coefficient partially linear spatial autoregressive model. The technique of profile least squares is employed and all estimators have explicit formulas which are computationally convenient. We derive the limiting distributions of the proposed estimators for both parametric and non parametric components. Variable selection procedures based on smooth-threshold estimating equations are proposed to automatically eliminate irrelevant parameters and zero varying coefficient functions. Compared to the alternative approaches based on shrinkage penalty, the new method is easily implemented. Oracle properties of the resulting estimators are established. Large amounts of Monte Carlo simulations confirm our theories and demonstrate that the estimators perform reasonably well in finite samples. We also apply the novel methods to an empirical data analysis.
{"title":"Automatic variable selection for semiparametric spatial autoregressive model","authors":"Fang Lu, Sisheng Liu, Jing Yang, Xuewen Lu","doi":"10.1080/07474938.2023.2225947","DOIUrl":"https://doi.org/10.1080/07474938.2023.2225947","url":null,"abstract":"Abstract This article studies the generalized method of moment estimation of semiparametric varying coefficient partially linear spatial autoregressive model. The technique of profile least squares is employed and all estimators have explicit formulas which are computationally convenient. We derive the limiting distributions of the proposed estimators for both parametric and non parametric components. Variable selection procedures based on smooth-threshold estimating equations are proposed to automatically eliminate irrelevant parameters and zero varying coefficient functions. Compared to the alternative approaches based on shrinkage penalty, the new method is easily implemented. Oracle properties of the resulting estimators are established. Large amounts of Monte Carlo simulations confirm our theories and demonstrate that the estimators perform reasonably well in finite samples. We also apply the novel methods to an empirical data analysis.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"655 - 675"},"PeriodicalIF":1.2,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49062643","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 : 2023-07-12DOI: 10.1080/07474938.2023.2224175
Kees Jan van Garderen
Abstract This article considers unbiased prediction of levels when data series are modeled as a random walk with drift and other exogenous factors after taking natural logs. We derive the unique unbiased predictors for growth and its variance. Derivation of level forecasts is more involved because the last observation enters the conditional expectation and is highly correlated with the parameter estimates, even asymptotically. This leads to conceptual questions regarding conditioning on endogenous variables. We prove that no conditionally unbiased forecast exists. We derive forecasts that are unconditionally unbiased and take into account estimation uncertainty, non linearity of the transformations, and the correlation between the last observation and estimate, which is quantitatively more important than estimation uncertainty and future disturbances together. The exact unbiased forecasts are shown to have lower Mean Squared Forecast Error (MSFE) than usual forecasts. The results are applied to Bitcoin price levels and a disaggregated eight sector model of UK industrial production.
{"title":"Forecasting Levels in Loglinear Unit Root Models","authors":"Kees Jan van Garderen","doi":"10.1080/07474938.2023.2224175","DOIUrl":"https://doi.org/10.1080/07474938.2023.2224175","url":null,"abstract":"Abstract This article considers unbiased prediction of levels when data series are modeled as a random walk with drift and other exogenous factors after taking natural logs. We derive the unique unbiased predictors for growth and its variance. Derivation of level forecasts is more involved because the last observation enters the conditional expectation and is highly correlated with the parameter estimates, even asymptotically. This leads to conceptual questions regarding conditioning on endogenous variables. We prove that no conditionally unbiased forecast exists. We derive forecasts that are unconditionally unbiased and take into account estimation uncertainty, non linearity of the transformations, and the correlation between the last observation and estimate, which is quantitatively more important than estimation uncertainty and future disturbances together. The exact unbiased forecasts are shown to have lower Mean Squared Forecast Error (MSFE) than usual forecasts. The results are applied to Bitcoin price levels and a disaggregated eight sector model of UK industrial production.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"780 - 805"},"PeriodicalIF":1.2,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45835672","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 : 2023-07-12DOI: 10.1080/07474938.2023.2224658
Helmut Farbmacher, H. Tauchmann
Abstract We demonstrate that popular linear fixed-effects panel-data estimators are biased and inconsistent when applied in a discrete-time hazard setting, even if the data-generating process is consistent with the linear model. The bias is not just survival bias, but originates from the impossibility to transform the model such that the remaining disturbance term becomes conditional mean independent of the explanatory variables. The bias is hence present even in the absence of unobserved heterogeneity. We discuss instrumental variables estimation, using first-differences of the explanatory variables as instruments, as alternative estimation strategy. Monte Carlo simulations and an empirical application substantiate our theoretical results.
{"title":"Linear fixed-effects estimation with nonrepeated outcomes","authors":"Helmut Farbmacher, H. Tauchmann","doi":"10.1080/07474938.2023.2224658","DOIUrl":"https://doi.org/10.1080/07474938.2023.2224658","url":null,"abstract":"Abstract We demonstrate that popular linear fixed-effects panel-data estimators are biased and inconsistent when applied in a discrete-time hazard setting, even if the data-generating process is consistent with the linear model. The bias is not just survival bias, but originates from the impossibility to transform the model such that the remaining disturbance term becomes conditional mean independent of the explanatory variables. The bias is hence present even in the absence of unobserved heterogeneity. We discuss instrumental variables estimation, using first-differences of the explanatory variables as instruments, as alternative estimation strategy. Monte Carlo simulations and an empirical application substantiate our theoretical results.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"635 - 654"},"PeriodicalIF":1.2,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47946875","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 : 2023-07-03DOI: 10.1080/07474938.2023.2219183
Yining Chen, H. Torrent, F. Ziegelmann
Abstract We propose a robust methodology for estimating production frontiers with multi-dimensional input via a two-step nonparametric regression, in which we estimate the level and shape of the frontier before shifting it to an appropriate position. Our main contribution is to derive a novel frontier estimation method under a variety of flexible models which is robust to the presence of outliers and possesses some inherent advantages over traditional frontier estimators. Our approach may be viewed as a simplification, yet a generalization, of those proposed by Martins-Filho and coauthors, who estimate frontier surfaces in three steps. In particular, outliers, as well as commonly seen shape constraints of the frontier surfaces, such as concavity and monotonicity, can be straightforwardly handled by our estimation procedure. We show consistency and asymptotic distributional theory of our resulting estimators under standard assumptions in the multi-dimensional input setting. The competitive finite-sample performances of our estimators are highlighted in both simulation studies and empirical data analysis.
{"title":"Robust nonparametric frontier estimation in two steps","authors":"Yining Chen, H. Torrent, F. Ziegelmann","doi":"10.1080/07474938.2023.2219183","DOIUrl":"https://doi.org/10.1080/07474938.2023.2219183","url":null,"abstract":"Abstract We propose a robust methodology for estimating production frontiers with multi-dimensional input via a two-step nonparametric regression, in which we estimate the level and shape of the frontier before shifting it to an appropriate position. Our main contribution is to derive a novel frontier estimation method under a variety of flexible models which is robust to the presence of outliers and possesses some inherent advantages over traditional frontier estimators. Our approach may be viewed as a simplification, yet a generalization, of those proposed by Martins-Filho and coauthors, who estimate frontier surfaces in three steps. In particular, outliers, as well as commonly seen shape constraints of the frontier surfaces, such as concavity and monotonicity, can be straightforwardly handled by our estimation procedure. We show consistency and asymptotic distributional theory of our resulting estimators under standard assumptions in the multi-dimensional input setting. The competitive finite-sample performances of our estimators are highlighted in both simulation studies and empirical data analysis.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"612 - 634"},"PeriodicalIF":1.2,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43770302","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 : 2023-07-03DOI: 10.1080/07474938.2023.2213605
Y. Tu, Xinling Xie
Abstract This article evaluates the forecast performance of model averaging forecasts in a nonstationary vector autoregression with mixed roots in the vicinity of unity. The deviation from unit root allows for local to unity, moderate deviation from unity and strong unit root, and the direction of such deviation could be from either the stationary or the explosive side. We provide a theoretical foundation for comparison among various forecasts, including the least squares estimator, the constrained estimator imposing the unit root constraint, and the selection or average over these two basic estimators. Furthermore, three new types of estimators are constructed, i.e., the bagging versions of the pretest estimator, the Mallows-pretest estimator that marries the Mallows averaging criterion and the Wald test, and the Mallows-bagging estimator that combines the Mallows averaging criterion and bagging technique. The asymptotic risks are shown to depend on the local parameters, which are not consistently estimable. Via Monte Carlo simulations, graphic comparisons indicate that the Mallows averaging estimator has both robust and outstanding forecasting performance. Model averaging over the vector autoregressive lag order is further considered to address the issue of model uncertainty in the lag specification. Finite sample simulations show that the Mallows averaging estimator performs superior to other frequently used selection and averaging methods. The application to forecasting the financial indices popularly used in the predictive regression further illustrates the practical merit of the proposed estimator.
{"title":"Forecasting vector autoregressions with mixed roots in the vicinity of unity","authors":"Y. Tu, Xinling Xie","doi":"10.1080/07474938.2023.2213605","DOIUrl":"https://doi.org/10.1080/07474938.2023.2213605","url":null,"abstract":"Abstract This article evaluates the forecast performance of model averaging forecasts in a nonstationary vector autoregression with mixed roots in the vicinity of unity. The deviation from unit root allows for local to unity, moderate deviation from unity and strong unit root, and the direction of such deviation could be from either the stationary or the explosive side. We provide a theoretical foundation for comparison among various forecasts, including the least squares estimator, the constrained estimator imposing the unit root constraint, and the selection or average over these two basic estimators. Furthermore, three new types of estimators are constructed, i.e., the bagging versions of the pretest estimator, the Mallows-pretest estimator that marries the Mallows averaging criterion and the Wald test, and the Mallows-bagging estimator that combines the Mallows averaging criterion and bagging technique. The asymptotic risks are shown to depend on the local parameters, which are not consistently estimable. Via Monte Carlo simulations, graphic comparisons indicate that the Mallows averaging estimator has both robust and outstanding forecasting performance. Model averaging over the vector autoregressive lag order is further considered to address the issue of model uncertainty in the lag specification. Finite sample simulations show that the Mallows averaging estimator performs superior to other frequently used selection and averaging methods. The application to forecasting the financial indices popularly used in the predictive regression further illustrates the practical merit of the proposed estimator.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"556 - 585"},"PeriodicalIF":1.2,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43079830","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 : 2023-06-29DOI: 10.1080/07474938.2023.2215034
Xiaohu Wang, Jun Yu
Abstract The article studies a class of state-space models where the state equation is a local-to-unity process. The parameter of interest is the persistence parameter of the latent process. The large sample theory for the least squares (LS) estimator and an instrumental variable (IV) estimator of the persistent parameter in the autoregressive (AR) representation of the model is developed under two sets of conditions. In the first set of conditions, the measurement error is independent and identically distributed, and the error term in the state equation is stationary and fractionally integrated with memory parameter . For both estimators, the convergence rate and the asymptotic distribution crucially depend on d. The LS estimator has a severe downward bias, which is aggravated even more by the measurement error when . The IV estimator eliminates the effects of the measurement error and reduces the bias. In the second set of conditions, the measurement error is independent but not necessarily identically distributed, and the error term in the state equation is strongly mixing. In this case, the IV estimator still leads to a smaller bias than the LS estimator. Special cases of our models and results in relation to those in the literature are discussed.
{"title":"Latent local-to-unity models","authors":"Xiaohu Wang, Jun Yu","doi":"10.1080/07474938.2023.2215034","DOIUrl":"https://doi.org/10.1080/07474938.2023.2215034","url":null,"abstract":"Abstract The article studies a class of state-space models where the state equation is a local-to-unity process. The parameter of interest is the persistence parameter of the latent process. The large sample theory for the least squares (LS) estimator and an instrumental variable (IV) estimator of the persistent parameter in the autoregressive (AR) representation of the model is developed under two sets of conditions. In the first set of conditions, the measurement error is independent and identically distributed, and the error term in the state equation is stationary and fractionally integrated with memory parameter . For both estimators, the convergence rate and the asymptotic distribution crucially depend on d. The LS estimator has a severe downward bias, which is aggravated even more by the measurement error when . The IV estimator eliminates the effects of the measurement error and reduces the bias. In the second set of conditions, the measurement error is independent but not necessarily identically distributed, and the error term in the state equation is strongly mixing. In this case, the IV estimator still leads to a smaller bias than the LS estimator. Special cases of our models and results in relation to those in the literature are discussed.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"586 - 611"},"PeriodicalIF":1.2,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43728364","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 : 2023-06-22DOI: 10.1080/07474938.2023.2221558
Chaoyi Chen, Thanasis Stengos, Yiguo Sun
This article considers a semiparametric threshold regression model with two threshold variables. The proposed model allows endogenous threshold variables and endogenous slope regressors. Under the diminishing threshold effects framework, we derive consistency and asymptotic results of our proposed estimator for weakly dependent data. We study the finite sample performance of our proposed estimator via small Monte Carlo simulations and apply our model to classify economic growth regimes based on both national public debt and national external debt.
{"title":"Endogeneity in semiparametric threshold regression models with two threshold variables","authors":"Chaoyi Chen, Thanasis Stengos, Yiguo Sun","doi":"10.1080/07474938.2023.2221558","DOIUrl":"https://doi.org/10.1080/07474938.2023.2221558","url":null,"abstract":"This article considers a semiparametric threshold regression model with two threshold variables. The proposed model allows endogenous threshold variables and endogenous slope regressors. Under the diminishing threshold effects framework, we derive consistency and asymptotic results of our proposed estimator for weakly dependent data. We study the finite sample performance of our proposed estimator via small Monte Carlo simulations and apply our model to classify economic growth regimes based on both national public debt and national external debt.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136286760","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 : 2023-06-12DOI: 10.1080/07474938.2023.2217077
Bingduo Yang, Xiaohui Liu, Wei Long, Liang Peng
Abstract Using the augmented Dickey-Fuller test to verify the existence of a unit root in an autoregressive process often requires the correctly specified intercept, since the test statistics can be distinctive under different model specifications and lead to contradictory results at times. In this article, we develop a unified inference that not only unifies the specifications of the intercept but also accommodates different degrees of persistence of the underlying process and heteroscedastic errors. A simulation study shows that the resulting unified unit root test exhibits excellent size control and reasonably good power. In an empirical application, we implement the proposed test to re-examine the presence of unit roots within eleven widely used variables in stock return predictability.
{"title":"A unified unit root test regardless of intercept","authors":"Bingduo Yang, Xiaohui Liu, Wei Long, Liang Peng","doi":"10.1080/07474938.2023.2217077","DOIUrl":"https://doi.org/10.1080/07474938.2023.2217077","url":null,"abstract":"Abstract Using the augmented Dickey-Fuller test to verify the existence of a unit root in an autoregressive process often requires the correctly specified intercept, since the test statistics can be distinctive under different model specifications and lead to contradictory results at times. In this article, we develop a unified inference that not only unifies the specifications of the intercept but also accommodates different degrees of persistence of the underlying process and heteroscedastic errors. A simulation study shows that the resulting unified unit root test exhibits excellent size control and reasonably good power. In an empirical application, we implement the proposed test to re-examine the presence of unit roots within eleven widely used variables in stock return predictability.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"540 - 555"},"PeriodicalIF":1.2,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42227929","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 : 2023-06-07DOI: 10.1080/07474938.2023.2198929
K. Hitomi, Masamune Iwasawa, Y. Nishiyama
Abstract This study investigates optimal minimax rates of specification testing for linear and non-linear instrumental variable regression models. The test constructed by non-parametric kernel techniques can be rate optimal when bandwidths are selected appropriately. Since bandwidths are often selected in a data-dependent way in empirical studies, the rate-optimality of the test with data-driven bandwidths is investigated. While least squares cross-validation selects bandwidths that are optimal for estimation, it is shown not to be optimal for testing. Thus, we propose a novel bandwidth selection method for testing, the performance of which is investigated in a simulation study.
{"title":"Optimal minimax rates of specification testing with data-driven bandwidth","authors":"K. Hitomi, Masamune Iwasawa, Y. Nishiyama","doi":"10.1080/07474938.2023.2198929","DOIUrl":"https://doi.org/10.1080/07474938.2023.2198929","url":null,"abstract":"Abstract This study investigates optimal minimax rates of specification testing for linear and non-linear instrumental variable regression models. The test constructed by non-parametric kernel techniques can be rate optimal when bandwidths are selected appropriately. Since bandwidths are often selected in a data-dependent way in empirical studies, the rate-optimality of the test with data-driven bandwidths is investigated. While least squares cross-validation selects bandwidths that are optimal for estimation, it is shown not to be optimal for testing. Thus, we propose a novel bandwidth selection method for testing, the performance of which is investigated in a simulation study.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"487 - 512"},"PeriodicalIF":1.2,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44979079","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 : 2023-05-28DOI: 10.1080/07474938.2023.2209008
E. Dagum, S. Bianconcini
Abstract Socioeconomic indicators have long been used by official statistical agencies to analyze and assess the current stage at which the economy stands via the application of linear filters used in conjunction with seasonal adjustment procedures. In this study, we propose a new set of symmetric and asymmetric weights that offer substantial gains in real-time by providing timely and more accurate information for detecting short-term trends with respect to filters commonly applied by statistical agencies. We compare the new filters to the classical ones through application to indicators of the US economy, which remains the linchpin of the global economic system. To assess the superiority of the proposed filters, we develop and evaluate explicit tests of the null hypothesis of no difference in revision accuracy of two competing filters. Furthermore, asymptotic and exact finite-sample tests are proposed and illustrated to assess if two compared filters have equal probabilities of failing to detect turning points at different time horizons after their occurrence.
{"title":"Monitoring the direction of the short-term trend of economic indicators","authors":"E. Dagum, S. Bianconcini","doi":"10.1080/07474938.2023.2209008","DOIUrl":"https://doi.org/10.1080/07474938.2023.2209008","url":null,"abstract":"Abstract Socioeconomic indicators have long been used by official statistical agencies to analyze and assess the current stage at which the economy stands via the application of linear filters used in conjunction with seasonal adjustment procedures. In this study, we propose a new set of symmetric and asymmetric weights that offer substantial gains in real-time by providing timely and more accurate information for detecting short-term trends with respect to filters commonly applied by statistical agencies. We compare the new filters to the classical ones through application to indicators of the US economy, which remains the linchpin of the global economic system. To assess the superiority of the proposed filters, we develop and evaluate explicit tests of the null hypothesis of no difference in revision accuracy of two competing filters. Furthermore, asymptotic and exact finite-sample tests are proposed and illustrated to assess if two compared filters have equal probabilities of failing to detect turning points at different time horizons after their occurrence.","PeriodicalId":11438,"journal":{"name":"Econometric Reviews","volume":"42 1","pages":"421 - 440"},"PeriodicalIF":1.2,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48883567","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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