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Joint Statistics and Econometrics Seminar Series

Statistics takes the numbers that you have and summarises them into fewer numbers which are easily digestible by the human brain

This seminar series is a joint partnership with the STICERD Econometrics programme.

All joint Statistics and Econometrics seminars during Lent Term 2019 will take place from 12.00pm to 1.00pm and will be preceded by refreshments from 11.45am. Unless otherwise specified, the seminars will take place in COL 6.15 (Leverhulme Library), 6th Floor of Columbia House. 

Current seminars in Lent term 2020

 

Friday 6th March, 12pm in COL 6.15 - Dr Haolei Weng from Michigan State University 

More information will be confirmed nearer the time. 

Friday 7th February, 12pm in COL 6.15 - Dr Peter Orbanz from UCL

More information will be confirmed nearer the time. 

 

Past seminars in Lent term 2019

Friday 29th March 2019, 12pm in COL 6.15 - Prof. Duo Qin from SOAS, University of London

Title: Let’s take the bias out of Econometrics 

Abstract: This study exposes the cognitive flaws of ‘endogeneity bias’. It examines how conceptualisation of the bias has evolved to embrace all major econometric problems, despite extensive lack of hard evidence. It reveals the crux of the bias – a priori rejection of causal variables as conditionally valid ones, and of the bias correction by consistent estimators – modification of those variables by non-uniquely and non-causally generated regressors. It traces the flaws to misconceptions about error terms and estimation consistency. It highlights the need to shake off the bias to let statistical learning play an active and formal role in econometrics.  

It's a paper recently published

https://www.tandfonline.com/doi/full/10.1080/1350178X.2018.1547415

https://www.soas.ac.uk/staff/staff60855.php

Friday 22nd March 2019 12pm in - 32L.G.03  -  Victor Chernozhukov, MIT

Title: Double/debiased machine learning for treatment and structural parameters 

Abstract: We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) methods, which are particularly well suited to estimation in modern, very high‐dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η0 cause a heavy bias in estimators of θ0that are obtained by naively plugging ML estimators of η0 into estimating equations for θ0. This bias results in the naive estimator failing to be urn:x-wiley:13684221:media:ectj12097:ectj12097-math-0001 consistent, where N is the sample size. We show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ0 can be removed by using two simple, yet critical, ingredients: (1) using Neyman‐orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters to estimate θ0; (2) making use of cross‐fitting, which provides an efficient form of data‐splitting. We call the resulting set of methods double or debiased ML (DML). We verify that DML delivers point estimators that concentrate in an urn:x-wiley:13684221:media:ectj12097:ectj12097-math-0002‐neighbourhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements. The generic statistical theory of DML is elementary and simultaneously relies on only weak theoretical requirements, which will admit the use of a broad array of modern ML methods for estimating the nuisance parameters, such as random forests, lasso, ridge, deep neural nets, boosted trees, and various hybrids and ensembles of these methods. We illustrate the general theory by applying it to provide theoretical properties of the following: DML applied to learn the main regression parameter in a partially linear regression model; DML applied to learn the coefficient on an endogenous variable in a partially linear instrumental variables model; DML applied to learn the average treatment effect and the average treatment effect on the treated under unconfoundedness; DML applied to learn the local average treatment effect in an instrumental variables setting. In addition to these theoretical applications, we also illustrate the use of DML in three empirical examples.

Friday 15th March 2019 12pm in COL 6.15 - Degui Li, University of York

Title: Nonparametric Homogeneity Pursuit in Functional-Coefficient Models

Abstract: This paper explores the homogeneity of coefficient functions in nonlinear models with functional coefficients and identifies the underlying semiparametric modelling structure. With initial kernel estimates of coefficient functions, we combine the classic hierarchical clustering method with a generalised version of the information criterion to estimate the number of clusters, each of which has a common functional coefficient, and determine the membership of each cluster. To identify a possible semi-varying coefficient modelling framework, we further introduce a penalised local least squares method to determine zero coefficients, non-zero constant coefficients and functional coefficients which vary with an index variable. Through the nonparametric kernel-based cluster analysis and the penalised approach, we can substantially reduce the number of unknown parametric and nonparametric components in the models, thereby achieving the aim of dimension reduction. Under some regularity conditions, we establish the asymptotic properties for the proposed methods including the consistency of the homogeneity pursuit. Numerical studies, including Monte-Carlo experiments and an empirical application, are given to demonstrate the finite-sample performance of our methods.

Friday 1st March 2019 12pm in COL 6.15 - Filipa Sa, Kings College London

Title: The Effect of University Fees on Applications, Attendance and Course Choice: Evidence from a Natural Experiment in the UK 

Abstract: Over the past two decades, large changes have been introduced to the level of university fees in the UK, with significant variation across countries. This paper exploits this variation to examine the effect of fees on university applications, attendance and course choice. It finds that applications decrease in response to higher fees with an elasticity of demand of about -0.4. Attendance also decreases. The reduction in applications and attendance is larger for courses with lower salaries and employment rates after graduation, for non-STEM subjects, and for less selective universities.

Friday 1st February 2019, 12pm in NAB 1.07 - Roger Koenker, UCL 

Title: Nonparametric maximum likelihood methods for binary response models with random coefficients

Abstract: Single index linear models for binary response with random coefficients have been extensively employed in many settings under various parametric specifications of the distribution of the random coefficients. Nonparametric maximum likelihood estimation (NPMLE) as proposed by Kiefer and Wolfowitz (1956) in contrast, has received less attention in applied work due primarily to computational difficulties. We propose a new approach to computation of NPMLEs for binary response models that significantly increase their computational tractability thereby facilitating greater flexibility in applications. Our approach, which relies on recent developments involving the geometry of hyperplane arrangements by Rada and Cerny (2018), is contrasted with the deconvolution method of Gautier and Kitamura (2013).

**Please note that this talk will be held in the New Academic Building room 1.07**

 

Past seminars 

Please have a look at the STICERD website for details on the past seminars.