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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.

You can subscribe or unsubscribe to our mailing list (emetrics). 



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Thursday  13 February 2020  14:00 - 15:30

Identification and Estimation of Group-Level Partial Effects

Kenichi Nagasawa (Warwick)

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32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
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Thursday  13 February 2020  14:00 - 15:30

Identification and Estimation of Group-Level Partial Effects

Kenichi Nagasawa (Warwick)

[pdf] Download Paper


32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
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Thursday  20 February 2020  14:00 - 15:30

A Panel Correlated Random Coefficient Model with Time - Varying Endogeneity

Louise Laage (Toulouse School of Economics)

32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
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Thursday  27 February 2020  14:00 - 15:30

Estimation of Weak Factor Models

Takashi Yamagata (York University)

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In this paper, we propose a novel consistent estimation method for the approximate factor model of Chamberlain and Rothschild (1983), with large cross-sectional and time-series dimensions (N and T, respectively). Their model assumes that the r (&#8810;N) largest eigenvalues of data covariance matrix grow as N rises without specifying each diverging rate. This is weaker than the typical assumption on the recent factor models, in which all the r largest eigenvalues diverge proportionally to N, and is frequently referred to as the weak factor models. We extend the sparse orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) to consider consistent estimation of the weak factors structure, where the k-th largest eigenvalue grows proportionally to N&#945;k with some unknown exponents 0<&#945;k&#8804;1 for k=1,…,r. Importantly, our method enables us to consistently estimate &#945;k as well. In our finite sample experiment, the performance of the new estimator uniformly dominates that of the principal component (PC) estimators in terms of mean absolute loss, and its superiority gets larger as the common components become weaker. We apply our method to analyze S&P500 firm security monthly returns from January 1984 to April 2018, and the results show that the first factor is consistently near strong, whilst the second to the fourth exponents vary over months between 0.90 and 0.65 and they cross. In another application, we consider out-of-sample performance of forecasting regressions for bond yield using extracted factors by our method and by the PC, and the forecasting performance test concludes that our method outperforms the PC method


32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
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Thursday  05 March 2020  14:00 - 15:30

Optimal Linear Instrumental Variables Approximations

Juan Carlos Escanciano (Indiana University)

Ordinary least squares provides the optimal linear approximation to the true regression function under misspecification. This paper investigates the Instrumental Variables (IV) version of this problem. The resulting population parameter is called the Optimal Linear IV Approximation (OLIVA).This paper shows that a necessary condition for regular identification of the OLIVA is also sufficient for existence of an IV estimand in a linear IV model. The necessary condition holds for the important case of a binary endogenous treatment, leading also to a LATE interpretation with positive weights. The instrument in the IV estimand is unknown and is estimated in a first step. A Two-Step IV (TSIV) estimator is proposed. We establish the asymptotic normality of a debiased TSIV estimator based on locally robust moments. The TSIV estimator does not require neither completeness nor identification of the instrument. As a by-product of our analysis, we robustify the classical Hausman test for exogeneity against misspecification of the linear model. Monte Carlo simulations suggest excellent finite sample performance for the proposed inferences


32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
There are also future events listed for this series. Please see STICERD Econometrics Seminars listed for Next Term