nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒11‒13
twenty-two papers chosen by
Sune Karlsson, Örebro universitet

  1. Identification and Estimation of a Semiparametric Logit Model using Network Data By Brice Romuald Gueyap Kounga
  2. Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates By Qingliang Fan; Zijian Guo; Ziwei Mei; Cun-Hui Zhang
  3. Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects By Wenlong Ji; Lihua Lei; Asher Spector
  4. Robust Minimum Distance Inference in Structural Models By Joan Alegre; Juan Carlos Escanciano
  5. Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting By Nezakati, Ensiyeh; Pircalabelu, Eugen
  6. Subsampling inference for nonparametric extremal conditional quantiles By Kurisu, Daisuke; Otsu, Taisuke
  7. Identification and Estimation in a Class of Potential Outcomes Models By Manu Navjeevan; Rodrigo Pinto; Andres Santos
  8. On Optimal Set Estimation for Partially Identified Binary Choice Models By Shakeeb Khan; Tatiana Komarova; Denis Nekipelov
  9. Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples By Sascha A. Keweloh
  10. Directional false discovery rate control via debiased and distributed procedures in Gaussian graphical models By Nezakati, Ensiyeh; Pircalabelu, Eugen
  11. Moran's I Lasso for models with spatially correlated data By Sylvain Barde; Rowan Cherodian; Guy Tchuente
  12. On changepoint detection in functional data using empirical energy distance By B. Cooper Boniece; Lajos Horv\'ath; Lorenzo Trapani
  13. Estimation of market efficiency process within time-varying autoregressive models by extended Kalman filtering approach By Maria Kulikova; Gennady Kulikov
  14. Instability of Factor Strength in Asset Returns By Daniele Massacci
  15. Cointegration with time-varying parameters: literature review By Malikova, Ekaterina (Маликова, Екатерина)
  16. Realized Covariance Models with Time-varying Parameters and Spillover Effects By Bauwens, Luc; Otranto, Edoardo
  17. Inference in Dynamic, Nonparametric Models of Production for General Technologies By Simar, Léopold; Wilson, Paul
  18. An asymptotic expansion of the empirical angular measure for bivariate extremal dependence By Lhaut, Stéphane; Segers, Johan
  19. On Sinkhorn's Algorithm and Choice Modeling By Zhaonan Qu; Alfred Galichon; Johan Ugander
  20. Finite Sample Performance of a Conduct Parameter Test in Homogenous Goods Markets By Yuri Matsumura; Suguru Otani
  21. MIDAS regression: a new horse in the race of filtering macroeconomic time series By Michal BenÄ ík
  22. What Makes Econometric Ideas Popular: The Role of Connectivity By Candelon, Bertrand; Joëts, Marc; Mignon, Valérie

  1. By: Brice Romuald Gueyap Kounga
    Abstract: This paper studies the identification and estimation of a semiparametric binary network model in which the unobserved social characteristic is endogenous, that is, the unobserved individual characteristic influences both the binary outcome of interest and how links are formed within the network. The exact functional form of the latent social characteristic is not known. The proposed estimators are obtained based on matching pairs of agents whose network formation distributions are the same. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a Monte-Carlo simulation are assessed. We conclude this study with an empirical application.
    Date: 2023–10
  2. By: Qingliang Fan; Zijian Guo; Ziwei Mei; Cun-Hui Zhang
    Abstract: Nonlinearity and endogeneity are common in causal effect studies with observational data. In this paper, we propose new estimation and inference procedures for nonparametric treatment effect functions with endogeneity and potentially high-dimensional covariates. The main innovation of this paper is the double bias correction procedure for the nonparametric instrumental variable (NPIV) model under high dimensions. We provide a useful uniform confidence band of the marginal effect function, defined as the derivative of the nonparametric treatment function. The asymptotic honesty of the confidence band is verified in theory. Simulations and an empirical study of air pollution and migration demonstrate the validity of our procedures.
    Date: 2023–10
  3. By: Wenlong Ji; Lihua Lei; Asher Spector
    Abstract: Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper partial identification bounds; however, unless the covariates are discrete with relatively small support, this approach typically requires consistent estimation of the conditional distributions of the potential outcomes given the covariates. Thus, existing approaches may fail under model misspecification or if consistency assumptions are violated. In this study, we propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands, based on duality theory for optimal transport problems. In randomized experiments, our approach can wrap around any estimates of the conditional distributions and provide uniformly valid inference, even if the initial estimates are arbitrarily inaccurate. Also, our approach is doubly robust in observational studies. Notably, this property allows analysts to use the multiplier bootstrap to select covariates and models without sacrificing validity even if the true model is not included. Furthermore, if the conditional distributions are estimated at semiparametric rates, our approach matches the performance of an oracle with perfect knowledge of the outcome model. Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.
    Date: 2023–10
  4. By: Joan Alegre; Juan Carlos Escanciano
    Abstract: This paper proposes minimum distance inference for a structural parameter of interest, which is robust to the lack of identification of other structural nuisance parameters. Some choices of the weighting matrix lead to asymptotic chi-squared distributions with degrees of freedom that can be consistently estimated from the data, even under partial identification. In any case, knowledge of the level of under-identification is not required. We study the power of our robust test. Several examples show the wide applicability of the procedure and a Monte Carlo investigates its finite sample performance. Our identification-robust inference method can be applied to make inferences on both calibrated (fixed) parameters and any other structural parameter of interest. We illustrate the method's usefulness by applying it to a structural model on the non-neutrality of monetary policy, as in \cite{nakamura2018high}, where we empirically evaluate the validity of the calibrated parameters and we carry out robust inference on the slope of the Phillips curve and the information effect.
    Date: 2023–10
  5. By: Nezakati, Ensiyeh (Université catholique de Louvain, LIDAM/ISBA, Belgium); Pircalabelu, Eugen (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: This paper proposes a distributed estimation and inferential framework for sparse multivariate regression and conditional Gaussian graphical models under the unbalanced splitting setting. This type of data splitting arises when the datasets from different sources cannot be aggregated on one single machine or when the available machines are of different powers. In this paper, the number of covariates, responses and machines grow with the sample size, while sparsity is imposed. Debiased estimators of the coefficient matrix and of the precision matrix are proposed on every single machine and theoretical guarantees are provided. Moreover, new aggregated estimators that pool information across the machines using a pseudo log-likelihood function are proposed. It is shown that they enjoy efficiency and asymptotic normality as the number of machines grows with the sample size. The performance of these estimators is investigated via a simulation study and a real data example. It is shown empirically that the performances of these estimators are close to those of the non-distributed estimators which use the entire dataset.
    Keywords: Multivariate regression models ; Conditional Gaussian graphical models ; Debiased estimation ; Precision matrix ; Sparsity ; Unbalanced distributed setting
    Date: 2023–05–31
  6. By: Kurisu, Daisuke; Otsu, Taisuke
    Abstract: This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance parameters in the limiting distribution of the local estimator. A simulation study and empirical example illustrate usefulness of our subsampling inference to investigate extremal phenomena.
    Keywords: quantile regression; subsampling; extreme value theory
    JEL: J1
    Date: 2023–09–27
  7. By: Manu Navjeevan; Rodrigo Pinto; Andres Santos
    Abstract: This paper develops a class of potential outcomes models characterized by three main features: (i) Unobserved heterogeneity can be represented by a vector of potential outcomes and a type describing the manner in which an instrument determines the choice of treatment; (ii) The availability of an instrumental variable that is conditionally independent of unobserved heterogeneity; and (iii) The imposition of convex restrictions on the distribution of unobserved heterogeneity. The proposed class of models encompasses multiple classical and novel research designs, yet possesses a common structure that permits a unifying analysis of identification and estimation. In particular, we establish that these models share a common necessary and sufficient condition for identifying certain causal parameters. Our identification results are constructive in that they yield estimating moment conditions for the parameters of interest. Focusing on a leading special case of our framework, we further show how these estimating moment conditions may be modified to be doubly robust. The corresponding double robust estimators are shown to be asymptotically normally distributed, bootstrap based inference is shown to be asymptotically valid, and the semi-parametric efficiency bound is derived for those parameters that are root-n estimable. We illustrate the usefulness of our results for developing, identifying, and estimating causal models through an empirical evaluation of the role of mental health as a mediating variable in the Moving To Opportunity experiment.
    Date: 2023–10
  8. By: Shakeeb Khan; Tatiana Komarova; Denis Nekipelov
    Abstract: In this paper we reconsider the notion of optimality in estimation of partially identified models. We illustrate the general problem in the context of a semiparametric binary choice model with discrete covariates as an example of a model which is partially identified as shown in, e.g. Bierens and Hartog (1988). A set estimator for the regression coefficients in the model can be constructed by implementing the Maximum Score procedure proposed by Manski (1975). For many designs this procedure converges to the identified set for these parameters, and so in one sense is optimal. But as shown in Komarova (2013) for other cases the Maximum Score objective function gives an outer region of the identified set. This motivates alternative methods that are optimal in one sense that they converge to the identified region in all designs, and we propose and compare such procedures. One is a Hodges type estimator combining the Maximum Score estimator with existing procedures. A second is a two step estimator using a Maximum Score type objective function in the second step. Lastly we propose a new random set quantile estimator, motivated by definitions introduced in Molchanov (2006). Extensions of these ideas for the cross sectional model to static and dynamic discrete panel data models are also provided.
    Date: 2023–10
  9. By: Sascha A. Keweloh
    Abstract: Generalized method of moments estimators based on higher-order moment conditions derived from independent shocks can be used to identify and estimate the simultaneous interaction in structural vector autoregressions. This study highlights two problems that arise when using these estimators in small samples. First, imprecise estimates of the asymptotically efficient weighting matrix and the asymptotic variance lead to volatile estimates and inaccurate inference. Second, many moment conditions lead to a small sample scaling bias towards innovations with a variance smaller than the normalizing unit variance assumption. To address the first problem, I propose utilizing the assumption of independent structural shocks to estimate the efficient weighting matrix and the variance of the estimator. For the second issue, I propose incorporating a continuously updated scaling term into the weighting matrix, eliminating the scaling bias. To demonstrate the effectiveness of these measures, I conducted a Monte Carlo simulation which shows a significant improvement in the performance of the estimator.
    Date: 2023–10
  10. By: Nezakati, Ensiyeh (Université catholique de Louvain, LIDAM/ISBA, Belgium); Pircalabelu, Eugen (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: In this paper, a multiple testing procedure is established for the conditional dependence in Gaussian graphical models. In practice, it is important to determine whether the conditional dependence between variables is positive or negative. However, there are several challenges to building statistics for testing using sample data. For instance, due to privacy concerns, one is not able to aggregate different datasets from several locations in one single location. In this study, different test statistics are constructed using debiased and distributed estimators to address this problem in a multiple testing framework. It is shown that, under mild conditions, the proposed procedure can control asymptotically the directional false discovery rate, which focuses on the sign of the estimation, at a prespecified level. An asymptotic power equal to one is also attainable under mild conditions on the non-zero entries of the precision matrix. Different simulation scenarios and real data examples are used to investigate the performance of the proposed procedure and to confirm the theoretical results.
    Keywords: Multiple hypotheses testing ; directional false discovery rate ; Gaussian graphical models ; debiased estimator ; distributed estimator
    Date: 2023–06–29
  11. By: Sylvain Barde; Rowan Cherodian; Guy Tchuente
    Abstract: This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran's I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted cross-sectional correlation terms in a classical linear regression framework, thus does not require the researcher to explicitly specify the spatial part of the underlying structural model. We derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive, theoretically grounded, and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our main simulation results show the proposed selection procedure performs well in finite samples. Compared to existing selection procedures, we find Mi-Lasso has one of the smallest biases and mean squared errors across a range of sample sizes and levels of spatial correlation. An application on house prices further demonstrates Mi-Lasso performs well compared to existing procedures.
    Date: 2023–10
  12. By: B. Cooper Boniece; Lajos Horv\'ath; Lorenzo Trapani
    Abstract: We propose a novel family of test statistics to detect the presence of changepoints in a sequence of dependent, possibly multivariate, functional-valued observations. Our approach allows to test for a very general class of changepoints, including the "classical" case of changes in the mean, and even changes in the whole distribution. Our statistics are based on a generalisation of the empirical energy distance; we propose weighted functionals of the energy distance process, which are designed in order to enhance the ability to detect breaks occurring at sample endpoints. The limiting distribution of the maximally selected version of our statistics requires only the computation of the eigenvalues of the covariance function, thus being readily implementable in the most commonly employed packages, e.g. R. We show that, under the alternative, our statistics are able to detect changepoints occurring even very close to the beginning/end of the sample. In the presence of multiple changepoints, we propose a binary segmentation algorithm to estimate the number of breaks and the locations thereof. Simulations show that our procedures work very well in finite samples. We complement our theory with applications to financial and temperature data.
    Date: 2023–10
  13. By: Maria Kulikova; Gennady Kulikov
    Abstract: This paper explores a time-varying version of weak-form market efficiency that is a key component of the so-called Adaptive Market Hypothesis (AMH). One of the most common methodologies used for modeling and estimating a degree of market efficiency lies in an analysis of the serial autocorrelation in observed return series. Under the AMH, a time-varying market efficiency level is modeled by time-varying autoregressive (AR) process and traditionally estimated by the Kalman filter (KF). Being a linear estimator, the KF is hardly capable to track the hidden nonlinear dynamics that is an essential feature of the models under investigation. The contribution of this paper is threefold. We first provide a brief overview of time-varying AR models and estimation methods utilized for testing a weak-form market efficiency in econometrics literature. Secondly, we propose novel accurate estimation approach for recovering the hidden process of evolving market efficiency level by the extended Kalman filter (EKF). Thirdly, our empirical study concerns an examination of the Standard and Poor's 500 Composite stock index and the Dow Jones Industrial Average index. Monthly data covers the period from November 1927 to June 2020, which includes the U.S. Great Depression, the 2008-2009 global financial crisis and the first wave of recent COVID-19 recession. The results reveal that the U.S. market was affected during all these periods, but generally remained weak-form efficient since the mid of 1946 as detected by the estimator.
    Date: 2023–10
  14. By: Daniele Massacci (University of Naples Federico II, King’s Business School, and CSEF)
    Abstract: We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns with observable factors. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate determines the strength of the corresponding factor. We propose LM and Wald statistics for the null hypothesis of stability and derive their asymptotic distribution when the break fraction is known, as well as when it is unknown and has to be estimated. We corroborate our theoretical results through a comprehensive series of Monte Carlo experiments. An extensive empirical analysis uncovers the dynamics of instability of factor strength in financial returns from equity portfolios.
    Keywords: Factor strength, structural break, hypothesis testing, stock portfolios.
    JEL: C12 C33 C58 G10 G12
    Date: 2023–10–13
  15. By: Malikova, Ekaterina (Маликова, Екатерина) (The Russian Presidential Academy of National Economy and Public Administration)
    Abstract: This paper reviews the literature devoted to the analysis of cointegrated time series in the economy, where the parameters of cointegration vary over time. The main studies that develop methods for modeling the movement of parameters, different approaches to evaluating the model, as well as tests for cointegration are considered. In addition, the areas of application of cointegration models with time-varying parameters in macroeconomic studies are highlighted.
    Keywords: time series cointegration, TVC model
    JEL: C22
    Date: 2022–09–29
  16. By: Bauwens, Luc (Université catholique de Louvain, LIDAM/CORE, Belgium); Otranto, Edoardo (Universita di Messina)
    Abstract: A realized covariance model specifies a dynamic process for a conditional covariance matrix of daily asset returns as a function of past realized variances and covariances. We propose parsimonious parameterizations enabling a spillover effect in the conditional variance equations, and a specific nonlinear, time-varying, impact of the lagged realized covariance between each asset pair on the corresponding conditional covariance. We introduce these parameterizations in BEKK, DCC and HAR type scalar models. In an application relative to the components of the Dow Jones index, we find that the extended models improve the fit and the out-of-sample forecast performances of their less flexible scalar versions.
    Keywords: Realized volatility ; spillover effect ; attenuation effect ; time-varying parameters
    JEL: G11 G17 C32 C58
    Date: 2023–07–21
  17. By: Simar, Léopold (Université catholique de Louvain, LIDAM/ISBA, Belgium); Wilson, Paul (Clemson University)
    Abstract: Nonparametric envelopment estimators are often used to estimate the attainable set and its efficient boundary, and to assess efficiency and changes in productivity. Kneip et al (2021) provide asymptotic results that can be used to make inference about expected changes in productivity measured by Malmquist indices and about the sources of productivity changes. These results require convexity of the attainable set, but in a number of situations this assumption is questionable. Recently, Kneip et al (2022) extend these results to allow for possibly non-convex technologies where the DEA estimators are known to be inconsistent. This paper summarizes these results, and explains how researchers should choose the appropriate method in a particular application.
    Keywords: Nonparametric production frontiers ; DEA ; FDH ; Malmquist Indices
    Date: 2023–10–09
  18. By: Lhaut, Stéphane (Université catholique de Louvain, LIDAM/ISBA, Belgium); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learning problems involving observations far away from the center. In the common situation that the components of the vector have different distributions, the rank transformation offers a convenient and robust way of standardizing data in order to build an empirical version of the angular measure based on the most extreme ob- servations. We provide a functional asymptotic expansion for the empirical angular measure in the bivariate case based on the theory of weak convergence in the space of bounded functions. From the expansion, not only can the known asymptotic distribution of the empirical angular measure be recovered, it also enables to find expansions and weak limits for other statistics based on the associated empirical process or its quantile version.
    Date: 2023–05–29
  19. By: Zhaonan Qu; Alfred Galichon; Johan Ugander
    Abstract: For a broad class of choice and ranking models based on Luce's choice axiom, including the Bradley--Terry--Luce and Plackett--Luce models, we show that the associated maximum likelihood estimation problems are equivalent to a classic matrix balancing problem with target row and column sums. This perspective opens doors between two seemingly unrelated research areas, and allows us to unify existing algorithms in the choice modeling literature as special instances or analogs of Sinkhorn's celebrated algorithm for matrix balancing. We draw inspirations from these connections and resolve important open problems on the study of Sinkhorn's algorithm. We first prove the global linear convergence of Sinkhorn's algorithm for non-negative matrices whenever finite solutions to the matrix balancing problem exist. We characterize this global rate of convergence in terms of the algebraic connectivity of the bipartite graph constructed from data. Next, we also derive the sharp asymptotic rate of linear convergence, which generalizes a classic result of Knight (2008), but with a more explicit analysis that exploits an intrinsic orthogonality structure. To our knowledge, these are the first quantitative linear convergence results for Sinkhorn's algorithm for general non-negative matrices and positive marginals. The connections we establish in this paper between matrix balancing and choice modeling could help motivate further transmission of ideas and interesting results in both directions.
    Date: 2023–09
  20. By: Yuri Matsumura; Suguru Otani
    Abstract: We assess the finite sample performance of the conduct parameter test in homogeneous goods markets. Statistical power rises with an increase in the number of markets, a larger conduct parameter, and a stronger demand rotation instrument. However, even with a moderate number of markets and five firms, regardless of instrument strength and the utilization of optimal instruments, rejecting the null hypothesis of perfect competition remains challenging. Our findings indicate that empirical results that fail to reject perfect competition are a consequence of the limited number of markets rather than methodological deficiencies.
    Date: 2023–10
  21. By: Michal BenÄ ík (National Bank of Slovakia)
    Abstract: We propose a new method of dealing with the end point problem when filtering economic time series. The main idea is to replace filtered quarterly observations at the end of the sample with static forecasts from a MIDAS regression using higher frequency time series. This method is capable to improve stability of output gap estimates or other cyclical series, as we confirm by empirical analysis on selected CEE countries and the United States. We find that stability may still be violated due to structural breaks in business cycles, or by an excessive amount of short-term noise. While MIDAS regressions have the potential to improve output gap estimates compared to the HP filter approach, the country-specific circumstances play a considerable role and need to be considered.
    JEL: C22 E32
    Date: 2023–10
  22. By: Candelon, Bertrand (Université catholique de Louvain, LIDAM/LFIN, Belgium); Joëts, Marc; Mignon, Valérie
    Abstract: This paper aims to identify the factors contributing to the diffusion of ideas in econometrics by paying particular attention to connectivity in content and social networks. Considering a sample of 17, 260 research papers in econometrics over the 1980-2020 period, we rely on Structural Topic Models to extract and categorize topics relevant to key domains in the discipline. Using a hurdle count model, we show that both content and social connectivity among the authors (i.e., social connectivity) enhance the likelihood of non-zero citation counts and play a key role in shaping the diffusion of econometric ideas. We also find that high topic connectivity augmented by robust social connectivity among authors or authoring teams further enhances econometric ideas’ diffusion success.
    Keywords: Connectivity ; Idea diffusion ; Econometric publications ; Citations ; Structural Topic Model ; Hurdle count model
    JEL: C01
    Date: 2023–10–17

This nep-ecm issue is ©2023 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.