|
on Econometrics |
| By: | Gayani Rathnayake; Akanksha Negi; Otavio Bartalotti; Xueyan Zhao |
| Abstract: | Endogenous treatment and sample selection are two concomitant sources of endogeneity that challenge the validity of causal inference. In this paper, we focus on the partial identification of treatment effects within a standard two-period difference-in-differences framework when the outcome is observed for an endogenously selected subpopulation. The identification strategy embeds Lee's (2009) bounding approach based on principal stratification, which divides the population into latent subgroups based on selection behaviour in counterfactual treatment states in both periods. We establish identification results for four latent types and illustrate the proposed approach by applying it to estimate 1) the effect of a job training program on earnings and 2) the effect of a working-from-home policy on employee performance. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09221 |
| By: | Kenichiro Shiraya; Kanji Suzuki; Tomohisa Yamakami |
| Abstract: | Two formulations are proposed to filter out correlations in the residuals of the multivariate GARCH model. The first approach is to estimate the correlation matrix as a parameter and transform any joint distribution to have an arbitrary correlation matrix. The second approach transforms time series data into an uncorrelated residual based on the eigenvalue decomposition of a correlation matrix. The empirical performance of these methods is examined through a prediction task for foreign exchange rates and compared with other methodologies in terms of the out-of-sample likelihood. By using these approaches, the DCC-GARCH residual can be almost independent. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08246 |
| By: | Haoyu Chen; Tiantian Mao; Fan Yang |
| Abstract: | In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient estimation methods for the adjusted standard-deviatile at intermediate and extreme levels. By using techniques from extreme value theory, the asymptotic normality is proved for both estimators. Simulations and real data applications are conducted to examine the performance of the proposed estimators. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.07203 |
| By: | Wenchao Xu; Xinyu Zhang |
| Abstract: | Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or the assumption that there is no true model with fixed dimensions in the candidate set. The focus of this paper is to overcome these difficulties. Surprisingly, we discover that when the penalty factor in the weight selection criterion diverges with a certain order and the true model dimension is fixed, asymptotic loss optimality does not hold, but asymptotic risk optimality does. This result differs from the corresponding result of Fang et al. (2023, Econometric Theory 39, 412-441) and reveals that using the discrete weight set of Hansen (2007, Econometrica 75, 1175-1189) can yield opposite asymptotic properties compared to using the usual weight set. Simulation studies illustrate the theoretical findings in a variety of settings. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09258 |
| By: | Mathur, Maya B; Shpitser, Ilya |
| Abstract: | We respond to Madley-Dowd et al's recent article in American Journal of Epidemiology. We show that standard imputation algorithms can fail for simple graphs (such as those used in Madley-Dowd et al's simulation study) even when the full data distribution is identified and an appropriate imputation estimator would be straightforward to design. |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:c3zrh |
| By: | Kranz, Sebastian |
| Abstract: | We compare heteroskedasticity-robust inference methods with a large-scale Monte Carlo study based on regressions from 155 reproduction packages of leading economic journals. The results confirm established wisdom and uncover new insights. Among well established methods HC2 standard errors with the degree of freedom specification proposed by Bell and McCaffrey (2002) perform best. To further improve the accuracy of t-tests, we propose a novel degree-of-freedom specification based on partial leverages. We also show how HC2 to HC4 standard errors can be refined by more effectively addressing the 15.6% of cases where at least one observation exhibits a leverage of one. |
| Keywords: | hetereoskedasticity, robust standard errors, meta study, replications, degree of freedom correction |
| JEL: | C1 C12 C15 C87 |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122724 |
| By: | Sokbae Lee |
| Abstract: | Joel L. Horowitz has made profound contributions to many areas in econometrics and statistics. These include bootstrap methods, semiparametric and nonparametric estimation, specification testing, nonparametric instrumental variables estimation, high-dimensional models, functional data analysis, and shape restrictions, among others. Originally trained as a physicist, Joel made a pivotal transition to econometrics, greatly benefiting our profession. Throughout his career, he has collaborated extensively with a diverse range of coauthors, including students, departmental colleagues, and scholars from around the globe. Joel was born in 1941 in Pasadena, California. He attended Stanford for his undergraduate studies and obtained his Ph.D. in physics from Cornell in 1967. He has been Charles E. and Emma H. Morrison Professor of Economics at Northwestern University since 2001. Prior to that, he was a faculty member at the University of Iowa (1982-2001). He has served as a co-editor of Econometric Theory (1992-2000) and Econometrica (2000-2004). He is a Fellow of the Econometric Society and of the American Statistical Association, and an elected member of the International Statistical Institute. The majority of this interview took place in London during June 2022. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00886 |
| By: | Stephen J. Redding |
| Abstract: | This paper reviews recent quantitative urban models. These models are sufficiently rich to capture observed features of the data, such as many asymmetric locations and a rich geography of the transport network. Yet these models remain sufficiently tractable as to permit an analytical characterization of their theoretical properties. With only a small number of structural parameters (elasticities) to be estimated, they lend themselves to transparent identification. As they rationalize the observed spatial distribution of economic activity within cities, they can be used to undertake counterfactuals for the impact of empirically-realistic public-policy interventions on this observed distribution. Empirical applications include estimating the strength of agglomeration economies and evaluating the impact of transport infrastructure improvements (e.g., railroads, roads, Rapid Bus Transit Systems), zoning and land use regulations, place-based policies, and new technologies such as remote working. |
| JEL: | R32 R41 R52 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33130 |