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on Econometrics |
| By: | Luther Yap |
| Abstract: | This paper considers inference in a linear instrumental variable regression model with many potentially weak instruments and treatment effect heterogeneity. I show that existing tests can be arbitrarily oversized in this setup. Then, I develop a valid procedure that is robust to weak instrument asymptotics and heterogeneous treatment effects. The procedure targets a JIVE estimand, calculates an LM statistic, and compares it with critical values from a normal distribution. To establish this procedure's validity, this paper shows that the LM statistic is asymptotically normal and a leave-three-out variance estimator is unbiased and consistent. The power of the LM test is also close to a power envelope in an empirical application. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11193 |
| By: | Alyssa Carlson (Department of Economics, University of Missouri); Anastasia Semykina (Royal Melbourne Institute of Technology) |
| Abstract: | We present a general framework for nonlinear dynamic panel data models subject to missing outcomes due to endogenous attrition. We consider two cases of attrition. First, ignorable attrition where the distribution of the outcome does not depend on missingness conditional on the unobserved heterogeneity. Second, non-ignorable attrition where the conditional distribution of the outcome does depend on attrition. In either case, a major challenge posed by the dynamic specification is the inherent correlation between the lagged dependent variable and unobserved individual heterogeneity. Our key assumption is that the distribution of the unobserved heterogeneity does not depend on attrition conditional on observed covariates and initial condition. The resulting estimator is a joint MLE that accommodates a dynamic specification, correlated unobserved heterogeneity, and endogenous attrition. We discuss the derivation and estimation of the average partial effects within this framework and provide examples for the binary response, ordinal response, and corner solution cases. Finite sample properties are studied using Monte Carlo simulations. As an empirical application, the proposed method is applied to estimating a dynamic health model for older women. |
| Keywords: | attrition, dynamic, nonlinear, panel data, correlated random effects |
| JEL: | C23 C24 C25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:umc:wpaper:2408 |
| By: | Geonwoo Kim; Suyong Song |
| Abstract: | We develop an estimator for treatment effects in high-dimensional settings with additive measurement error, a prevalent challenge in modern econometrics. We introduce the Double/Debiased Convex Conditioned LASSO (Double/Debiased CoCoLASSO), which extends the double/debiased machine learning framework to accommodate mismeasured covariates. Our principal contributions are threefold. (1) We construct a Neyman-orthogonal score function that remains valid under measurement error, incorporating a bias correction term to account for error-induced correlations. (2) We propose a method of moments estimator for the measurement error variance, enabling implementation without prior knowledge of the error covariance structure. (3) We establish the $\sqrt{N}$-consistency and asymptotic normality of our estimator under general conditions, allowing for both the number of covariates and the magnitude of measurement error to increase with the sample size. Our theoretical results demonstrate the estimator's efficiency within the class of regularized high-dimensional estimators accounting for measurement error. Monte Carlo simulations corroborate our asymptotic theory and illustrate the estimator's robust performance across various levels of measurement error. Notably, our covariance-oblivious approach nearly matches the efficiency of methods that assume known error variance. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.14671 |
| By: | Weiss, Amanda |
| Abstract: | When do modern difference-in-differences (DID)-style methods work for empirical political science? Scholars exploit the staggered roll-out of policies like election regulation, civil service reform, and healthcare across places to estimate causal effects - often using the two-way fixed effects (TWFE) estimator. However, recent literature has highlighted the TWFE estimator's bias in the presence of heterogeneous treatment effects and tendency to make ``forbidden comparisons" between treated units. In response, scholars have increasingly turned to modern DID estimators that promise greater robustness to real-world data problems. This paper asks how well these modern methods work for for the empirical settings and sample sizes commonly used in political science, with the U.S. states as the running example. In particular, it provides a simulation study of the performance of seven DID methods under either constant or heterogeneous effects, in an N=50 setting that mimics the American federalism natural experiment. I find that many modern methods (1) produce confidence intervals that do not include the true average effect at the specified rate and (2) are underpowered. I show that many cases of coverage problems with modern DID estimators can be addressed using the block bootstrap to estimate standard errors. However, I also show that even where identification and estimation are straightforward, the fifty-state sample poses a power problem without large average effect sizes - at least 0.5 standard deviations. I illustrate the challenges of DID research with the fifty-state panel in the case of estimating the effects of strict voter identification laws on voter turnout. |
| Date: | 2024–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:bqmws |
| By: | Carol Liu |
| Abstract: | Randomized controlled trials (RCTs) have long been the gold standard for causal inference across various fields, including business analysis, economic studies, sociology, clinical research, and network learning. The primary advantage of RCTs over observational studies lies in their ability to significantly reduce noise from individual variance. However, RCTs depend on strong assumptions, such as group independence, time independence, and group randomness, which are not always feasible in real-world applications. Traditional inferential methods, including analysis of covariance (ANCOVA), often fail when these assumptions do not hold. In this paper, we propose a novel approach named \textbf{Sp}ill\textbf{o}ve\textbf{r} \textbf{T}ime \textbf{S}eries \textbf{Causal} (\verb+SPORTSCausal+), which enables the estimation of treatment effects without relying on these stringent assumptions. We demonstrate the practical applicability of \verb+SPORTSCausal+ through a real-world budget-control experiment. In this experiment, data was collected from both a 5\% live experiment and a 50\% live experiment using the same treatment. Due to the spillover effect, the vanilla estimation of the treatment effect was not robust across different treatment sizes, whereas \verb+SPORTSCausal+ provided a robust estimation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11951 |
| By: | Jonathan Fuhr; Dominik Papies |
| Abstract: | Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. However, most of these frameworks assume settings with cross-sectional data, whereas researchers often have access to panel data, which in traditional methods helps to deal with unobserved heterogeneity between units. In this paper, we explore how we can adapt double/debiased machine learning (DML) (Chernozhukov et al., 2018) for panel data in the presence of unobserved heterogeneity. This adaptation is challenging because DML's cross-fitting procedure assumes independent data and the unobserved heterogeneity is not necessarily additively separable in settings with nonlinear observed confounding. We assess the performance of several intuitively appealing estimators in a variety of simulations. While we find violations of the cross-fitting assumptions to be largely inconsequential for the accuracy of the effect estimates, many of the considered methods fail to adequately account for the presence of unobserved heterogeneity. However, we find that using predictive models based on the correlated random effects approach (Mundlak, 1978) within DML leads to accurate coefficient estimates across settings, given a sample size that is large relative to the number of observed confounders. We also show that the influence of the unobserved heterogeneity on the observed confounders plays a significant role for the performance of most alternative methods. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.01266 |
| By: | Federico Bugni; Jackson Bunting; Muyang Ren |
| Abstract: | A panel dataset satisfies marginal homogeneity if the time-specific marginal distributions are homogeneous or time-invariant. Marginal homogeneity is relevant in economic settings such as dynamic discrete games. In this paper, we propose several tests for the hypothesis of marginal homogeneity and investigate their properties. We consider an asymptotic framework in which the number of individuals n in the panel diverges, and the number of periods T is fixed. We implement our tests by comparing a studentized or non-studentized T-sample version of the Cramer-von Mises statistic with a suitable critical value. We propose three methods to construct the critical value: asymptotic approximations, the bootstrap, and time permutations. We show that the first two methods result in asymptotically exact hypothesis tests. The permutation test based on a non-studentized statistic is asymptotically exact when T=2, but is asymptotically invalid when T>2. In contrast, the permutation test based on a studentized statistic is always asymptotically exact. Finally, under a time-exchangeability assumption, the permutation test is exact in finite samples, both with and without studentization. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.15862 |
| By: | Anne Opschoor (Vrije Universiteit Amsterdam); Dewi Peerlings (Vrije Universiteit Amsterdam); Luca Rossini (University of Milan); Andre Lucas (Vrije Universiteit Amsterdam) |
| Abstract: | We introduce the vector-valued t-Riesz distribution for time series models of electricity prices. The t-Riesz distribution extends the well-known Multivariate Student’s t distribution by allowing for tail heterogeneity via a vector of degrees of freedom (DoF) parameters. The closed-form density expression allows for straightforward maximum likelihood estimation. A clustering approach for the DoF parameters is provided to reduce the number of parameters in higher dimensions. We apply the t- Riesz distribution to a 24-dimensional panel of Danish daily electricity prices over the period 2017-2024, considering each hour of the day as a separate coordinate. Results show that multivariate t-Riesz-based density forecasts improve significantly upon the standard Student’s t distribution and the t-copula. Further, the t-Riesz distribution produces superior implied univariate density forecasts during the afternoon for the distribution as a whole and during 8 a.m.- 8 p.m. in its left tail. Moreover, during crisis periods, this effect is even stronger and holds for almost every hour of the day. Finally, portfolio Value-at-Risk forecasts during the central hours of the day improve during crisis periods compared to the classical Student’s t distribution and the t- copula. |
| Keywords: | multivariate distributions, (fat)-tail heterogeneity, (inverse) Riesz distribution, electricity prices |
| JEL: | C1 C22 C53 |
| Date: | 2024–07–19 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240049 |
| By: | Daouia, Abdelaati; Stupfler, Gilles; Usseglio-Carleve, Antoine |
| Abstract: | The use of the Expected Shortfall as a solution for various deficiencies of quan-tiles has gained substantial traction over the last 20 years. Its inference at extreme levels is a difficult problem in statistics, with existing approaches typically being limited to heavy-tailed distributions having a finite second tail moment. This constitutes a substantial restriction in areas like finance and environmental science, where the random variable of interest may have a much heavier tail or, at the opposite, may be light-tailed or short-tailed. Under a wider semiparametric extreme value framework, we develop comprehensive asymptotic theory for extreme Expected Shortfall estimation in the general class of distributions with finite first tail moment. By relying on the moment estimators of the scale and shape extreme value pa-rameters, we construct refined asymptotic confidence intervals whose finite-sample coverage is found to be close to the nominal level on simulated data. We illustrate the usefulness of our construction on two sets of financial loss returns and flood insurance claims data. |
| Keywords: | Expected Shortfall, extrapolation, inference, extreme value moment estimator, second-order regular variation, stable distribution, weak convergence. |
| JEL: | G20 G30 G32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:129693 |
| By: | Yan-Feng Wu; Xiangyu Yang; Jian-Qiang Hu |
| Abstract: | We develop moment estimators for the parameters of affine stochastic volatility models. We first address the challenge of calculating moments for the models by introducing a recursive equation for deriving closed-form expressions for moments of any order. Consequently, we propose our moment estimators. We then establish a central limit theorem for our estimators and derive the explicit formulas for the asymptotic covariance matrix. Finally, we provide numerical results to validate our method. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.09185 |
| By: | Igor Custodio João (Vrije Universiteit Amsterdam) |
| Abstract: | I refine the test for clustering of Patton and Weller (2022) to allow for cluster switching. In a multivariate panel setting, clustering on time- averages produces consistent estimators of means and group assignments. Once switching is introduced, we lose the consistency. In fact, under switch- ing the time-averaged k-means clustering converges to equal, indistinguishable means. This causes the test for a single cluster to lose power under the alternative of multiple clusters. Power can be regained by clustering the N times T observations independently and carefully subsampling the time dimension. When applied to the empirical setting of Bonhomme and Manresa (2015) of an autoregression of democracy in a panel of countries, we are able to detect clusters in the data under noisier conditions than the original test. |
| JEL: | C12 C33 C38 |
| Date: | 2024–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240052 |
| By: | Yuya Sasaki; Yulong Wang |
| Abstract: | We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures robust performance even in extreme tails, where data may be sparse or entirely absent. Simulation studies confirm the theoretical robustness of our approach. Applying our method to assess the impact of job training provided by the Job Training Partnership Act (JTPA), we find significantly negative QTEs for the lowest quantiles (i.e., the most disadvantaged individuals), contrasting with previous literature that emphasizes positive QTEs for intermediate quantiles. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.03979 |
| By: | Damir Filipović (École Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Paul Schneider (University of Lugano - Institute of Finance; Swiss Finance Institute) |
| Abstract: | In this short note, we study conditional linear factor models in the context of asset pricing panels. Our analysis focuses on conditional means and covariances to characterize the cross-sectional and inter-temporal properties of returns and factors as well as their interrelationships. We also review the conditions outlined in Kozak and Nagel (2024) and show how the conditional mean-variance efficient portfolio of an unbalanced panel can be spanned by low-dimensional factor portfolios, even without assuming invertibility of the conditional covariance matrices. Our analysis provides a comprehensive foundation for the specification and estimation of conditional linear factor models. |
| Keywords: | asset pricing, factor models, characteristics, covariances, meanvariance efficient portfolio, stochastic discount factor, covariance estimation |
| JEL: | G11 G12 C38 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2442 |
| By: | Harvey, David I; Leybourne, Stephen J; Taylor, AM Robert; Zu, Yang |
| Abstract: | We propose a new class of modified regression-based tests for detecting asset price bubbles designed to be robust to the presence of general forms of both conditional and unconditional heteroskedasticity in the price series. This modification, based on the approach developed in Beare (2018) in the context of conventional unit root testing, is achieved by purging the impact of unconditional heteroskedasticity from the data using a kernel estimate of volatility before the application of the bubble detection methods proposed in Phillips, Shi and Yu (2015) [PSY]. The modified statistic is shown to achieve the same limiting null distribution as the corresponding (heteroskedasticity-uncorrected) statistic from PSY would obtain under homoskedasticity, such that the usual critical values provided in PSY may still be used. Versions of the test based on regressions including either no intercept or a (redundant) intercept are considered. Representations for asymptotic local power against a single bubble model are also derived. Monte Carlo simulation results highlight that neither one of these tests dominates the other across different bubble locations and magnitudes, and across different models of time-varying volatility. Accordingly, we also propose a test based on a union of rejections between the with and without intercept variants of the modified PSY test. The union procedure is shown to perform almost as well as the better of the constituent tests for a given DGP, and also performs very well compared to existing heteroskedasticity-robust tests across a large range of simulation DGPs. |
| Keywords: | Rational bubble; explosive autoregression; time-varying volatility; kernel smoothing; right-tailed unit root testing; union of rejections |
| Date: | 2024–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:esy:uefcwp:39178 |
| By: | Shinji Koiso; Suguru Otani |
| Abstract: | This paper proposes a constrained maximum likelihood estimator for sequential search models, using the MPEC (Mathematical Programming with Equilibrium Constraints) approach. This method enhances numerical accuracy while avoiding ad hoc components and errors related to equilibrium conditions. Monte Carlo simulations show that the estimator performs better in small samples, with lower bias and root-mean-squared error, though less effectively in large samples. Despite these mixed results, the MPEC approach remains valuable for identifying candidate parameters comparable to the benchmark, without relying on ad hoc look-up tables, as it generates the table through solved equilibrium constraints. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.04378 |
| By: | Toru Yano |
| Abstract: | Volatility means the degree of variation of a stock price which is important in finance. Realized Volatility (RV) is an estimator of the volatility calculated using high-frequency observed prices. RV has lately attracted considerable attention of econometrics and mathematical finance. However, it is known that high-frequency data includes observation errors called market microstructure noise (MN). Nagakura and Watanabe[2015] proposed a state space model that resolves RV into true volatility and influence of MN. In this paper, we assume a dependent MN that autocorrelates and correlates with return as reported by Hansen and Lunde[2006] and extends the results of Nagakura and Watanabe[2015] and compare models by simulation and actual data. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.17187 |
| By: | Masselus, Lise; Petrik, Christina; Ankel-Peters, Jörg |
| Abstract: | While results from individual Randomized Controlled Trials (RCTs) often do not hold beyond their setting, the accumulation of many RCTs can be used to guide policy. But how many studies are required to confidently generalize? Our paper examines construct validity, an often neglected yet important element affecting generalizability. Construct validity deals with how the operationalization of a treatment corresponds to the broader construct it intends to speak to. The universe of potential operationalizations is referred to as the design space. As an empirical example, we review 45 microfinance RCTs to estimate the size of the design space and to underpin that variations in the treatment operationalization matter for the observed effects. We also show that most papers nevertheless generalize from the operationalized treatment to a broad construct, mostly without acknowledging underlying assumptions, and many lack a transparent reporting of construct validity-relevant features. |
| Abstract: | Während die Ergebnisse einzelner randomisierter kontrollierter Studien (RCTs) oft nicht über ihren Rahmen hinaus Gültigkeit haben, kann die Ansammlung vieler RCTs als Richtschnur für die Politik dienen. Aber wie viele Studien sind erforderlich, um eine sichere Verallgemeinerung zu erreichen? Unser Beitrag untersucht die Konstruktvalidität, ein oft vernachlässigtes, aber wichtiges Element, das die Verallgemeinerbarkeit beeinflusst. Bei der Konstruktvalidität geht es darum, inwieweit die Operationalisierung einer Behandlung dem breiteren Konstrukt entspricht, das sie ansprechen soll. Das Universum der möglichen Operationalisierungen wird als Designraum bezeichnet. Als empirisches Beispiel überprüfen wir 45 Mikrofinanz-RCTs, um die Größe des Designraums abzuschätzen und um zu untermauern, dass Variationen in der Operationalisierung der Behandlung für die beobachteten Effekte von Bedeutung sind. Wir zeigen auch, dass die meisten Arbeiten dennoch von der operationalisierten Behandlung auf ein breites Konstrukt verallgemeinern, meist ohne die zugrundeliegenden Annahmen anzuerkennen, und dass es vielen an einer transparenten Darstellung der für die Konstruktvalidität relevanten Merkmale fehlt. |
| Keywords: | Causal inference, generalizability, meta-science, microfinance |
| JEL: | A11 C18 C93 D04 O12 O16 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:302180 |