In this paper,we highlight some recent developments of a new route to evaluate macroeconomic policy effects,which are investigated under the framework with potential outcomes.First,this paper begins with a brief intro...In this paper,we highlight some recent developments of a new route to evaluate macroeconomic policy effects,which are investigated under the framework with potential outcomes.First,this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation.Secondly,primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data.Furthermore,we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation.Finally,we conclude by addressing some possible future research directions in statistics and econometrics.展开更多
Background： Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure＇s causal effect on outcomes. The instrumental variable （IV）...Background： Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure＇s causal effect on outcomes. The instrumental variable （IV） method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendefian randomization （MR）, which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator （TSLS）. The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage. Results： In this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine （LSKM）. Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting. Conclusions： Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.展开更多
基金the National Natural Science Foundation of China(71631004,Key Project)the National Science Fund for Distinguished Young Scholars(71625001)+2 种基金the Basic Scientific Center Project of National Science Foundation of China:Econometrics and Quantitative Policy Evaluation(71988101)the Science Foundation of Ministry of Education of China(19YJA910003)China Scholarship Council Funded Project(201806315045).
文摘In this paper,we highlight some recent developments of a new route to evaluate macroeconomic policy effects,which are investigated under the framework with potential outcomes.First,this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation.Secondly,primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data.Furthermore,we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation.Finally,we conclude by addressing some possible future research directions in statistics and econometrics.
基金This research was supported by the National Science Foundation under Grant （No. NSF ABI 1457935） and the National Institutes of Health under Grant （No. R01 GM117946）.
文摘Background： Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure＇s causal effect on outcomes. The instrumental variable （IV） method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendefian randomization （MR）, which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator （TSLS）. The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage. Results： In this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine （LSKM）. Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting. Conclusions： Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.
