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STICERD Econometrics Seminar Series


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These seminars are held on Thursdays in term time at 14.00-15.30, in 32L 3.05 (3rd floor, 32 Lincolns Inn Fields, London), unless specified otherwise.

Entry is on a first-come first-served basis. No registration is required but places are limited. 

Seminar organisers:  Prof. Tai Otsu and Dr. Vassilis Hajivassiliou.

For more information please contact Jane Dickson.

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Thursday  02 May 2019  14:15 - 15:45
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Implied Stochastic Volatility Models

Yacine Ait-Sahalia (Princeton University) , joint with Chenxu Li - Peking University - Guanghua School of Management Chen Xu Li - Princeton University - Bendheim Center for Finance

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This paper proposes to build "implied stochastic volatility models" designed to fit option-implied volatility data, and implements a method to construct such models. The method is based on explicitly linking shape characteristics of the implied volatility surface to the specification of the stochastic volatility model. We propose and implement parametric and nonparametric versions of implied stochastic volatility models


32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Thursday  09 May 2019  14:15 - 15:45
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Identification strategies for nonseparable models

Rosa Matzkin (UCLA)

When confronting an economic model with data, one usually encounters a situation where some important variables, such as tastes and productivity that appear in the model in nonadditive, nonseparable ways, are unobserved. Rather than transforming the model into one where the unobservables enter in separable ways, the nonseparable approach considers identification and estimation of the original model. The original model satisfies the economic restrictions of the model, which aid in identification and estimation. These restrictions are often lost in separable transformations. This seminar will cover identification and estimation methods for nonseparable models, with emphasis on nonparametric methods. First, some key econometric techniques used in nonseparable models will be presented. Next, it will be shown how these techniques have been used and extended to study particular econometric models.


32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Monday  13 May 2019  14:00 - 15:30

Essential Concepts of Causal Inference: A remarkable history and an intriguing future

Donald Rubin (Harvard University)

Causal inference is a major topic in any field that tries to understand the kinds of treatments (i.e., interventions) we humans are considering in order to effect particular changes in the world around us, whether those treatments involve business decisions, pharmaceuticals to ingest, educational programs to offer, military actions to take ÿ effectively everything that involves choices in our lives. Despite the ubiquity of this topic to the lives of unconscious and later conscious humans for tens of thousands of years, it has a remarkable history, with solid mathematical foundations beginning only in the early 20th century, with the development of crucial ideas tied to related ideas in physics, namely those arising in quantum mechanics. This formulation of causal inference has an intriguing future because of the increasing application of causal inference to treatments with conscious units, humans, despite its mathematical origins with unconscious units: plants, animals, industrial objects. Conscious units do not necessarily comply with their assigned treatments and can suffer from complications such as placebo effects; moreover, humans may depart from study protocols by dropping out early, or may use the internet to interfere with each other in ways that were considered impossible in the middle of the twentieth century. The proper handling of such complexities comprises an intriguing collection of topics,which are currently virtually unstudied with any mathematical rigor.


32L 1.04, 1st Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Thursday  16 May 2019  14:15 - 15:45
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CANCELLED

Marc Henry (Penn State) , joint with ISMAšEL MOURIFIŽE, AND ROMUALD MŽEANGO

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We analyze the empirical content of the Roy model, stripped down to its essential features, namely sector specific unobserved heterogeneity and selfselection on the basis of potential outcomes. We characterize sharp bounds on the joint distribution of potential outcomes and testable implications of the Roy self-selection model under an instrumental constraint on the joint distribution of potential outcomes we call stochastically monotone instrumental variable (SMIV). We show that testing the Roy model selection is equivalent to testing stochastic monotonicity of observed outcomes relative to the instrument. We apply our sharp bounds to the derivation of a measure of departure from Roy self-selection to identify values of observable characteristics that induce the most costly misallocation of talent and sector and are therefore prime targets for intervention. Special emphasis is put on the case of binary outcomes, which has received little attention in the literature to date. For richer sets of outcomes, we emphasize the distinction between pointwise sharp bounds and functional sharp bounds, and its importance, when constructing sharp bounds on functional features, such as inequality measures. We analyze a Roy model of college major choice in Canada and Germany within this framework, and we take a new look at the under-representation of women in STEM.


32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Thursday  23 May 2019  14:15 - 15:45
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Kernel Estimation for Dyadic Data

James Powell (UC Berkeley) , joint with Bryan S. Graham and Fengshi Niu

In this forthcoming working paper we consider nonparametric estimation of density and conditional expectation functions for dyadic random variables, i.e., random variables defined for all pairs of individuals/nodes in a network of size N. These random variables are assumed to satisfy a “local dependence” property, specifically, that any random variables in the network that share one or two indices may be dependent (though random variables which do not have an index in common are assumed to be independent). Estimation of density functions for continuously-distributed random variables or regression functions for continuously-distributed regressors are proposed using straightforward application of the kernel estimation methods proposed by Rosenblatt and Parzen (for densities) or by Nadaraya and Watson (for regression functions). Estimation of their asymptotic variances is also straightforward using existing proposals for dyadic data. More unusual are the rates of convergence and asymptotic (normal) distributions for the estimators, which are shown to converge at the same rate as the (unconditional) sample mean, i.e., the square root of the number N of nodes, under standard assumptions on the kernel method. This differs from the results for nonparametric estimation of densities and regression functions for monadic data, which generally have a slower rate of convergence than the sample mean.


32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Thursday  30 May 2019  14:15 - 15:45
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A Pearson's Independence Test for Conditional Model Checking

Miguel Delgado (Carlos III, Madrid)

32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


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Thursday  06 June 2019  14:00 - 15:30

Towards a General Large Sample Theory for Regularized Estimators

Demian Pouzo (UCL)

32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH