Yuya Sasaki

WHAT DO QUANTILE REGRESSIONS IDENTIFY FOR GENERAL STRUCTURAL FUNCTIONS

This paper shows what quantile regressions identify for general structural functions. Under fairly mild conditions, the quantile partial derivative identifies a weighted average of heterogeneous structural partial effects among the subpopulation of individuals at the conditional quantile of interest. This result justifies the use of quantile regressions as means of measuring heterogeneous causal effects for a general class of structural functions with multiple unobservables.

IDENTIFICATION OF PAIRED NONSEPARABLE MEASUREMENT ERROR MODELS

This paper studies the paired nonseparable measurement error models, where two measurements, X and Y , are produced by mutually independent unobservables, U , V , and W , through the system, X = g ( U,V ) and Y = h ( U,W ). We propose restrictions to identify the marginal distribution of the common component U and the conditional distributions of X and Y given U . Applying this method to twin panel data, we find the following robust reporting patterns for years of education: (1) self reports are accurate only when the true years of education are 16 or 18, typically corresponding to advanced university degrees in the US education system; (2) sibling reports are accurate whenever the true years of education are 12, 14, 16, and 18, which are typical diploma years.

ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE

We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.

Closed-Form Identication of Dynamic Discrete Choice Models with Proxies for Unobserved State Variables

For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identication of the structure by explicitly solving integral equations. In therst step, we derive closed-form identication of Markov components. In the second step, we plug therst-step identifying formulas into linear restrictions to obtain closed-form identication of structural param- eters.