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Econometrics Seminars

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Mon 26 Feb, '24
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Econometrics - Morten Orregaard Nielsen (Aarhus)
S2.79

Title: Inference on common trends in functional time series

The paper and abstract can be found here: https://arxiv.org/abs/2312.00590

Mon 4 Mar, '24
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Econometrics - Xun Tang (Rice)
S2.79

Title: Social Networks with Misclassified Links (joint w Arthur Lewbel and Xi Qu).

Abstract. We propose an adjusted 2SLS estimator for social network models when the links reported in samples are subject to two-sided misclassification errors (due, e.g., to recall errors by survey respondents, or lapses in data input). In a feasible structural form, misclassified links make all covariates endogenous and add a new source of correlation between the structural errors and endogenous peer outcomes (in addition to simultaneity), thus invalidating conventional estimators used in the literature. We resolve these issues by adjusting endogenous peer outcomes with estimates of the misclassification rates and constructing new instruments that exploit properties of the noisy network measures. We apply our method to study peer effects in household decisions to participate in a microfinance program in Indian villages. We find that ignoring the issue of link specification and applying conventional instruments would result in an upward bias in peer effect estimates.

Mon 11 Mar, '24
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Econometrics Seminar - Jordi Llorens Terrazas (Surrey)
S2.79

Title (provisional): An Oracle Inequality for Multivariate Dynamic Quantile Forecasting

Abstract: I derive an oracle inequality for a family of possibly misspecified multivariate conditional autoregressive quantile models. The family includes standard specifications for (nonlinear) quantile prediction proposed in the literature. This inequality is used to establish that the predictor that minimizes the in-sample average check loss achieves the best out-of-sample performance within its class at a near optimal rate, even when the model is fully misspecified. An empirical application to backtesting global Growth-at-Risk shows that a combination of the generalized autoregressive conditionally heteroscedastic model and the vector autoregression for Value-at-Risk performs best out-of-sample in terms of the check loss.

Link: An Oracle Inequality for Multivariate Dynamic Quantile Forecasting by Jordi Llorens-Terrazas :: SSRN

Mon 29 Apr, '24
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Econometrics Seminar - Tim Christensen (UCL)
S2.79
Mon 13 May, '24
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Econometrics Seminar - Cristina Gualdani (Queens Mary)
S2.79

Title to be advised.

Mon 20 May, '24
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Econometrics Seminar - Karim Chalak (Manchester)
S2.79

Title to be advised.

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