Posts by Collection


Correcting for Sample Selection Bias in Dyadic Regressions. With an Application to Gravity Models.

Based on MPhil thesis. We present methods to correct for sample selection bias in the estimation of dyadic regressions. Dyadic datasets can be seen as a pseudo panel data, where both dimensions tend to infinity as the number of individuals grows. We show that including fixed effects for both individuals forming a dyad leads to asymptotically biased estimates of the structural parameters in the first stage of the Heckman (1979) two step method. This is a consequence of the incidental parameter problem. We reconcile and modify existing approaches to similar problems in standard panel data models to this framework. Our Monte Carlo simulation exercise corroborates to the theoretical predictions that the standard Heckman approach yields biased estimates, while the proposed methods reduce such biases. We apply the proposed estimators to the gravity model for international trade flows. The suggested methods deliver different estimates for the coefficients of trade barriers.

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On the use of U-statistics for linear dyadic interaction models

Even though dyadic regressions are widely used in empirical applications, the (asymptotic) properties of estimation methods only began to be studied recently in the literature. This paper aims to provide in a step-by-step manner how U-statistics tools can be applied to obtain the asymptotic properties of pairwise differences estimators for a two-way fixed effects model of dyadic interactions. More specifically, we first propose an estimator for the model that relies on pairwise differencing such that the fixed effects are differenced out. As a result, the summands of the influence function will not be independent anymore, showing dependence on the individual level and translating to the fact that the usual law of large numbers and central limit theorems do not straightforwardly apply. To overcome such obstacles, we show how to generalize tools of U-statistics for single-index variables to the double-indices context of dyadic datasets. A key result is that there can be different ways of defining the Hajek projection for a directed dyadic structure, which will lead to distinct, but equivalent, consistent estimators for the asymptotic variances. The results presented in this paper are easily extended to non-linear models.

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On the Use of the Synthetic Difference-in-Differences Approach with(-out) Covariates: The Case Study of the Brexit Referendum

Joint with Esther de Brabander and Artūras Juodis. Submitted. The Synthetic Control (SC) method has been a popular and dominant method to evaluate treatment and intervention effects in the last two decades. The method is powerful yet very intuitive to use both for empirical researchers and policy experts, but is not without shortcomings. As a response to this, the new Demeaned SC (DSC) and Synthetic Differencein-differences (SDID) approaches were introduced in the literature. In this paper, we evaluate the relative benefits of using DSC and SDID using in-sample placebo analysis on the real data on the Brexit referendum, as well as an extensive Monte Carlo study. Overall, using the SDID methodology, we find that the estimated effect of the Brexit referendum on UK GDP at the end of 2018 and 2019 is higher than previously documented in the literature.


FEB PhD Lunch Seminar


Presenting “Asymptotic Properties of a Two-Way Fixed Effects Model of Dyadic Interactions Using U-statistics”.

Baltic Economic Conference


Presenting “Is the Effect of Brexit Larger Than Previously Thought? A Synthetic Difference-in-differences Approach with (-out) Covariates”.


Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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