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estimation methods from the treatment evaluation literature we separate the direct effect of textbooks from their peer effect …
Persistent link: https://www.econbiz.de/10013318702
In nonparametric instrumental variables estimation, the mapping that identifies the function of interest, g say, is discontinuous and must be regularised (that is, modified) to make consistent estimation possible. The amount of modification is contolled by a regularisation parameter. The optimal...
Persistent link: https://www.econbiz.de/10009760143
In a treatment effect model with unconfoundedness, treatment assignments are not only independent of potential outcomes given the covariates, but also given the propensity score alone. Despite this powerful dimension reduction property, adjusting for the propensity score is known to lead to an...
Persistent link: https://www.econbiz.de/10011486511
Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes...
Persistent link: https://www.econbiz.de/10013127382
Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well...
Persistent link: https://www.econbiz.de/10008906533
familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the …
Persistent link: https://www.econbiz.de/10011924562
Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing "big data" in regression settings, and it is often used for informal testing of substantive hypotheses such as linearity or monotonicity of the regression function....
Persistent link: https://www.econbiz.de/10011986776
This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the...
Persistent link: https://www.econbiz.de/10011760113
This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small) number of factors. I investigate the properties of the...
Persistent link: https://www.econbiz.de/10011568282
We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we use critical values that...
Persistent link: https://www.econbiz.de/10012202046