Economics

We provide an adversarial approach to estimating Riesz representers of linear functionals within arbitrary function spaces. We prove oracle inequalities based on the localized Rademacher complexity of the function space used to approximate the Riesz representer and the approximation error. These inequalities imply fast finite sample mean-squared-error rates for many function spaces of interest, such as high-dimensional sparse linear functions, neural networks and reproducing kernel Hilbert spaces. Our approach offers a new way of estimating Riesz representers with a plethora of recently introduced machine learning techniques. We show how our estimator can be used in the context of de-biasing structural/causal parameters in semi-parametric models, for automated orthogonalization of moment equations and for estimating the stochastic discount factor in the context of asset pricing.

Testing for mediation effects is empirically important and theoretically interesting. It is important in psychology, medicine, economics, accountancy, and marketing for instance, generating over 90,000 citations to a single key paper in the field. It also leads to a statistically interesting and long-standing problem that this paper solves. The no-mediation hypothesis, expressed as H 0 : θ 1 θ 2 =0 , defines a manifold that is non-regular in the origin where rejection probabilities of standard tests are extremely low. We propose a general method for obtaining near similar tests using a flexible g -function to bound the critical region. We prove that no similar test exists for mediation, but using our new varying g -method obtain a test that is all but similar and easy to use in practice. We derive tight upper bounds to similar and nonsimilar power envelopes and derive an optimal test. We extend the test to higher dimensions and illustrate the results in a trade union sentiment application.

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.

A quest to understand the decision of the UK to leave the European Union, Brexit, in the referendum of June 2016 has occupied academics, the media and politicians alike. As the debate about what the future relationship will look like rages, the referendum is given renewed importance as an indicator of the likely success, or otherwise, of any forward plans. Topological data analysis offers an ability to faithfully extract maximal information from complex multi-dimensional datasets of the type that have been gathered on Brexit voting. Within the complexity it is shown that support for Leave drew from a far more similar demographic than Remain. Obtaining votes from this concise set was more straightforward for Leave campaigners than was Remain's task of mobilising a diverse group to oppose Brexit. Broad patterns are consistent with extant empirical work, but the strength of TDA Ball Mapper means that evidence is offered to enrich the narrative on immobility, and being ``left-behind'' by EU membership, that could not be found before. A detailed understanding emerges which comments robustly on why Britain voted as it did. A start point for the policy development that must follow is given.

We provide an upper bound as a random variable for the functions of estimators in high dimensions. This upper bound may help establish the rate of convergence of functions in high dimensions. The upper bound random variable may converge faster, slower, or at the same rate as estimators depending on the behavior of the partial derivative of the function. We illustrate this via three examples. The first two examples use the upper bound for testing in high dimensions, and third example derives the estimated out-of-sample variance of large portfolios. All our results allow for a larger number of parameters, p, than the sample size, n.

The synthetic control method is a an econometric tool to evaluate causal effects when only one unit is treated. While initially aimed at evaluating the effect of large-scale macroeconomic changes with very few available control units, it has increasingly been used in place of more well-known microeconometric tools in a broad range of applications, but its properties in this context are unknown. This paper introduces an alternative to the synthetic control method, which is developed both in the usual asymptotic framework and in the high-dimensional scenario. We propose an estimator of average treatment effect that is doubly robust, consistent and asymptotically normal. It is also immunized against first-step selection mistakes. We illustrate these properties using Monte Carlo simulations and applications to both standard and potentially high-dimensional settings, and offer a comparison with the synthetic control method.

Price indexes in time and space is a most relevant topic in statistical analysis from both the methodological and the application side. In this paper a price index providing a novel and effective solution to price indexes over several periods and among several countries, that is in both a multi-period and a multilateral framework, is devised. The reference basket of the devised index is the union of the intersections of the baskets of all periods/countries in pairs. As such, it provides a broader coverage than usual indexes. Index closed-form expressions and updating formulas are provided and properties investigated. Last, applications with real and simulated data provide evidence of the performance of the index at stake.

We introduce a test for whether agents' preferences over network structure are interdependent. Interdependent preferences induce strategic behavior since the optimal set of links directed by agent i will vary with the configuration of links directed by other agents. Our model also incorporates agent-specific in- and out-degree heterogeneity and homophily on observable agent attributes. This introduces 2N+ K 2 nuisance parameters ( N is number of agents in the network and K the number of possible agent attribute configurations). Under the null equilibrium is unique, but our hypothesis is nevertheless a composite one as the degree heterogeneity and homophily nuisance parameters may range freely across their parameter space. Under the alternative our model is incomplete; there may be multiple equilibrium network configurations and our test is agnostic about which one is selected. Motivated by size control, and exploiting the exponential family structure of our model \emph{under the null}, we restrict ourselves to conditional tests. We characterize the exact null distribution of a family of conditional tests and introduce a novel Markov Chain Monte Carlo (MCMC) algorithm for simulating this distribution. We also characterize the locally best test. The form of this test depends upon the gradient of the likelihood with respect to the strategic interaction parameter in the neighborhood of the null. Remarkably, this gradient, and consequently the form of the locally best test statistic, does not depend on how an equilibrium is selected. Exploiting this lack of dependence, we outline a feasible version of the locally best test.

We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6 percentage points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.

Randomized experiments have become a standard tool in economics. In analyzing randomized experiments, the traditional approach has been based on the Stable Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there is no interference between individuals. However, the SUTVA assumption fails to hold in many applications due to social interaction, general equilibrium, and/or externality effects. While much progress has been made in relaxing the SUTVA assumption, most of this literature has only considered a setting with perfect compliance to treatment assignment. In practice, however, noncompliance occurs frequently where the actual treatment receipt is different from the assignment to the treatment. In this paper, we study causal effects in randomized experiments with network interference and noncompliance. Spillovers are allowed to occur at both treatment choice stage and outcome realization stage. In particular, we explicitly model treatment choices of agents as a binary game of incomplete information where resulting equilibrium treatment choice probabilities affect outcomes of interest. Outcomes are further characterized by a random coefficient model to allow for general unobserved heterogeneity in the causal effects. After defining our causal parameters of interest, we propose a simple control function estimator and derive its asymptotic properties under large-network asymptotics. We apply our methods to the randomized subsidy program of \cite{dupas} where we find evidence of spillover effects on both short-run and long-run adoption of insecticide-treated bed nets. Finally, we illustrate the usefulness of our methods by analyzing the impact of counterfactual subsidy policies.