nep-ecm New Economics Papers
on Econometrics
Issue of 2022‒05‒23
eleven papers chosen by
Sune Karlsson
Örebro universitet

  1. Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs By Haitian Xie
  2. Adversarial Estimators By Jonas Metzger
  3. A Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure By Liyang Sun; Jesse M. Shapiro
  4. Boundary Adaptive Local Polynomial Conditional Density Estimators By Matias D. Cattaneo; Rajita Chandak; Michael Jansson; Xinwei Ma
  5. Nonparametric Multiple-Output Center-Outward Quantile Regression By Eustasio del Barrio; Alberto González-Sanz; Marc Hallin
  6. Two-step estimation in linear regressions with adaptive learning By Alexander Mayer
  7. Controlling for Latent Confounding with Triple Proxies By Ben Deaner
  8. Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity By Victor Aguirregabiria
  9. Generic Identifiability for REMIS: The Cointegrated Unit Root VAR By Philipp Gersing; Leopold Soegner; Manfred Deistler
  10. Approximations and Inference for Nonparametric Production Frontiers By Cinzia Daraio; Leopold Simar
  11. MTE with Misspecification By Juli\'an Mart\'inez-Iriarte; Pietro Emilio Spini

  1. By: Haitian Xie
    Abstract: This paper examines the identification and estimation of the structural function in fuzzy regression discontinuity (RD) designs with a continuous treatment variable. Under a dual monotonicity condition, we show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff. The dual monotonicity condition requires that the structural function and the treatment choice be strictly increasing in the unobserved causal factor. This condition is satisfied by standard parametric models used in practice. The identification result contrasts with the local average treatment effect literature, where only a certain weighted average of the structural function is identified. We propose a three-step semiparametric estimation procedure and derive the asymptotic distribution of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by the discontinuity in natural light timing at time-zone boundaries.
    Date: 2022–04
  2. By: Jonas Metzger
    Abstract: We develop an asymptotic theory of adversarial estimators (`A-estimators'). Like maximum-likelihood-type estimators (`M-estimators'), both the estimator and estimand are defined as the critical points of a sample and population average respectively. A-estimators generalize M-estimators, as their objective is maximized by one set of parameters and minimized by another. The continuous-updating Generalized Method of Moments estimator, popular in econometrics and causal inference, is among the earliest members of this class which distinctly falls outside the M-estimation framework. Since the recent success of Generative Adversarial Networks, A-estimators received considerable attention in both machine learning and causal inference contexts, where a flexible adversary can remove the need for researchers to manually specify which features of a problem are important. We present general results characterizing the convergence rates of A-estimators under both point-wise and partial identification, and derive the asymptotic root-n normality for plug-in estimates of smooth functionals of their parameters. All unknown parameters may contain functions which are approximated via sieves. While the results apply generally, we provide easily verifiable, low-level conditions for the case where the sieves correspond to (deep) neural networks. Our theory also yields the asymptotic normality of general functionals of neural network M-estimators (as a special case), overcoming technical issues previously identified by the literature. We examine a variety of A-estimators proposed across econometrics and machine learning and use our theory to derive novel statistical results for each of them. Embedding distinct A-estimators into the same framework, we notice interesting connections among them, providing intuition and formal justification for their recent success in practical applications.
    Date: 2022–04
  3. By: Liyang Sun; Jesse M. Shapiro
    Abstract: Linear panel models featuring unit and time fixed effects appear in many areas of empirical economics. An active literature studies the interpretation of the ordinary least squares estimator of the model, commonly called the two-way fixed effects (TWFE) estimator, in the presence of unmodeled coefficient heterogeneity. We illustrate some implications for the case where the research design takes advantage of variation across units (say, US states) in exposure to some treatment (say, a policy change). In this case, the TWFE can fail to estimate the average (or even a weighted average) of the units' coefficients. Under some conditions, there exists no estimator that is guaranteed to estimate even a weighted average. Building on the literature, we note that when there is a unit totally unaffected by treatment, it is possible to estimate an average effect by replacing the TWFE with an average of difference-in-differences estimators.
    JEL: C23 C87
    Date: 2022–04
  4. By: Matias D. Cattaneo; Rajita Chandak; Michael Jansson; Xinwei Ma
    Abstract: We begin by introducing a class of conditional density estimators based on local polynomial techniques. The estimators are automatically boundary adaptive and easy to implement. We then study the (pointwise and) uniform statistical properties of the estimators, offering nonasymptotic characterizations of both probability concentration and distributional approximation. In particular, we establish optimal uniform convergence rate in probability and valid Gaussian distributional approximations for the t-statistic process indexed over the data support. We also discuss implementation issues such as consistent estimation of the covariance function of the Gaussian approximation, optimal integrated mean squared error bandwidth selection, and valid robust bias-corrected inference. We illustrate the applicability of our results by constructing valid confidence bands and hypothesis tests for both parametric specification and shape constraints, explicitly characterizing their nonasymptotic approximation probability errors. A companion R software package implementing our main results is provided.
    Date: 2022–04
  5. By: Eustasio del Barrio; Alberto González-Sanz; Marc Hallin
    Abstract: Based on the novel concept of multivariate center-outward quantiles introduced recently in Chernozhukov et al. (2017) and Hallin et al. (2021), we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content irrespective of the underlying distribution; their graphs constitute nested center-outward quantile regression tubes. Empirical counterparts of these concepts are constructed, yielding interpretable empirical regions andcontours which are shown to consistently reconstruct their population versions in the Pompeiu-Hausdorff topology. Our method is entirely non-parametric and performs well in simulations including heteroskedasticity and nonlinear trends; its power as a data-analytic tool is illustrated on some real datasets.
    Keywords: Multiple-output regression, Center-outward quantiles, Optimal transport
    Date: 2022–05
  6. By: Alexander Mayer
    Abstract: Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regression with adaptive learning is derived when the crucial, so-called, `gain' parameter is estimated in a first step by nonlinear least squares from an auxiliary model. The singular limiting distribution of the two-step estimator is normal and in general affected by the sampling uncertainty from the first step. However, this `generated-regressor' issue disappears for certain parameter combinations.
    Date: 2022–04
  7. By: Ben Deaner
    Abstract: We apply results in Hu and Schennach (2008) to achieve nonparametric identification of causal effects using noisy proxies for unobserved confounders. We call this the `triple proxy' approach because it requires three proxies that are jointly independent conditional on unobservables. We consider three different choices for the third proxy: it may be an outcome, a vector of treatments, or a collection of auxiliary variables. We compare to an alternative identification strategy introduced by Miao et. al. (2018) in which causal effects are identified using two conditionally independent proxies. We refer to this as the `double proxy' approach. We show that the conditional independence assumptions in the double and triple proxy approaches are non-nested, which suggests that either of the two identification strategies may be appropriate depending on the particular setting.
    Date: 2022–04
  8. By: Victor Aguirregabiria
    Abstract: This paper studies identification and estimation of a dynamic discrete choice model of demand for differentiated product using consumer-level panel data with few purchase events per consumer (i.e., short panel). Consumers are forward-looking and their preferences incorporate two sources of dynamics: last choice dependence due to habits and switching costs, and duration dependence due to inventory, depreciation, or learning. A key distinguishing feature of the model is that consumer unobserved heterogeneity has a Fixed Effects (FE) structure -- that is, its probability distribution conditional on the initial values of endogenous state variables is unrestricted. I apply and extend recent results to establish the identification of all the structural parameters as long as the dataset includes four or more purchase events per household. The parameters can be estimated using a sufficient statistic - conditional maximum likelihood (CML) method. An attractive feature of CML in this model is that the sufficient statistic controls for the forward-looking value of the consumer's decision problem such that the method does not require solving dynamic programming problems or calculating expected present values.
    Keywords: Structural dynamic discrete choice models; Dynamic demand of differentiated products; Panel data; Fixed effects; Habits; Switching costs; Storable products; Durable products
    JEL: C23 C25 C51 D12
    Date: 2022–05–08
  9. By: Philipp Gersing; Leopold Soegner; Manfred Deistler
    Abstract: The "REtrieval from MIxed Sampling" (REMIS) approach based on blocking developed in Anderson et al. (2016a) is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper we investigate parameter-identifiability in the Johansen (1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking differences at lag N (N is the slow sampling rate), the parameters of the high frequency system are generically identified. We treat the stock and the flow case as well as deterministic terms.
    Date: 2022–04
  10. By: Cinzia Daraio; Leopold Simar
    Abstract: Nonparametric methods have been widely used for assessing the performance of organizations in the private and public sector. The most popular ones are based on envelopment estimators, like the FDH or DEA estimators, that estimate the attainable sets and its efficient boundary by enveloping the cloud of observed units in the appropriate input-output space. The statistical properties of these flexible estimators have been established. However these nonparametric techniques do not allow to make sensitivity analysis of the production outputs to some particular inputs, or to infer about marginal products and other coefficients of economic interest. On the contrary, parametric models for production frontiers allow richer and easier economic interpretation but at a cost of restrictive assumptions on the data generating process. In addition, the latter rely mostly on regression methods fitting the center of the cloud of observed points. In this paper we offer a way to avoid these drawbacks and provide approximations of these coefficients of economic interest by “smoothing†the popular nonparametric estimators of the frontiers. Our approach allows to handle fully multivariate cases. We describe the statistical properties for both the full and the partial (robust) frontiers. We consider parametric but also flexible approximations based on local linear tools providing local estimates of all the desired partial derivatives and we show how to deal with environmental factors. An illustration on real data from European Higher Education Institutions (HEI) shows the usefulness of the proposed approach.
    Keywords: Nonparametric production frontiers; DEA; FDH; partial frontiers; directional distances; linear approximations; local linear approximations.
    Date: 2022–05–10
  11. By: Juli\'an Mart\'inez-Iriarte; Pietro Emilio Spini
    Abstract: This paper studies the implication of a fraction of the population not responding to the instrument when selecting into treatment. We show that, in general, the presence of non-responders biases the Marginal Treatment Effect (MTE) curve and many of its functionals. Yet, we show that, when the propensity score is fully supported on the unit interval, it is still possible to restore identification of the MTE curve and its functionals with an appropriate re-weighting.
    Date: 2022–04

This nep-ecm issue is ©2022 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.