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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, October 3, 2019 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Ruiqi Liu, IUPUI |
+^  **TITLE:**|Deep Instrument Variables Estimator  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+The endogeneity issue is fundamentally important in econometrics and
+statistics.  Many empirical applications may suffer from the omission of explanatory
+variables, measurement error and simultaneous causality.  We propose a two-stage
+estimator based on deep neural network (Deep Instrument Variables Estimator) to
+overcome endogeneity in the linear instrument variables model. A critical drawback
+of existing methods  is that when the number of instruments is large, one has to
+sacrifice the statistical efficiency for avoiding curse of dimensionality, or impose
+structural assumptions and explicitly rely on the specified structures to obtain an
+efficient estimator. We impose a latent structural assumption on the reduced form
+equation, which is more general and includes most of the popular statistical and
+econometric models. Based on deep neural network, we prove that our estimator can
+effectively capture the intrinsic structures of the reduced form equation without
+knowing the prior information of the structures. Moreover, we show that the proposed
+estimator is root-n consistent and semiparametric efficient. Simulation studies on
+synthetic data confirm the validity of our theoretical results.
+</WRAP>