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on Econometrics |
| By: | Klebel, Thomas; Traag, Vincent |
| Abstract: | Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might be ineffective or even detrimental to policy recommendations. Many publications in science studies lack appropriate methods to substantiate their causal claims. We here provide an introduction to structural causal models. Such models, usually represented in a graphical form, allow researchers to make their causal assumptions transparent and provide a foundation for causal inference. We illustrate how to use structural causal models to conduct causal inference using regression models based on simulated data of a hypothetical structural causal model of Open Science. The graphical representation of structural causal models allows researchers to clearly communicate their assumptions and findings, thereby fostering further discussion. We hope our introduction helps more researchers in science studies to consider causality explicitly. |
| Date: | 2024–02–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4bw9e_v1 |
| By: | Yoann Potiron (Keio University - Faculty of Business and Commerce); O. Scaillet (Swiss Finance Institute - University of Geneva); Vladimir Volkov (Tasmania School of Business and Economics, University of Tasmania); Seunghyeon Yu (Northwestern University - Kellogg School of Management) |
| Abstract: | We consider Hawkes self-exciting processes with a baseline driven by an Itô semimartingale with possible jumps. Under in-fill asymptotics, we characterize feasible statistics induced by central limit theory for empirical average and variance of local Poisson estimates. As a byproduct, we develop a test for the absence of a Hawkes component and a test for baseline constancy. Simulation studies corroborate the asymptotic theory. An empirical application on high-frequency data of the E-mini S&P500 future contracts shows that the absence of a Hawkes component and baseline constancy is always rejected. |
| Keywords: | Hawkes tests, in-fill asymptotics, high-frequency data, Itô semimartingale, selfexciting process, time-varying baseline |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2513 |
| By: | Nikolay Iskrev |
| Abstract: | In this paper, I evaluate the properties and performance of band spectral estimators applied to business cycle models. Band spectral methods are widely used to study frequency-dependentrelationships among time series. In business cycle research, the Whittle likelihood approximation enables researchers to estimate models using only the frequencies those models are best suited to represent, such as the business cycle frequencies. Using the medium-scale model of Angeletos et al. (2018) as a data-generating process, I conduct a Monte Carlo study to assess the finite-sample properties of the band spectral maximum likelihood estimator (MLE) and compare its performance with that of the full-spectrum and exact time-domain MLEs. The results show that the band spectral estimator exhibits considerable biases and efficiency losses for most estimated parameters. Moreover, both the full-information and band spectral Whittle estimators perform poorly in contrast to the time domain estimator, which successfully recovers all model parameters. I demonstrate how these findings can be understood through the theoretical properties of the underlying model, and describe simple tools and diagnostics that can be used to detect potential problems in band spectral estimation for a wide class of macroeconomic models. |
| JEL: | C32 C52 C51 E32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202419 |
| By: | Geraldo, Pablo |
| Abstract: | Causal inference with observational data critically relies on untestable and extra-statistical assumptions that have (sometimes) testable implications. Well-known sets of assumptions that are sufficient to justify the causal interpretation of certain estimators are called identification strategies. These templates for causal analysis, however, do not perfectly map into empirical research practice. Researchers are often left in the disjunctive of either abstracting away from their particular setting to fit in the templates, risking erroneous inferences, or avoiding situations in which the templates cannot be applied, missing valuable opportunities for conducting empirical analysis. In this article, I show how directed acyclic graphs (DAGs) can help researchers to conduct empirical research and assess the quality of evidence without excessively relying on research templates. First, I offer a concise introduction to causal inference frameworks. Then I survey the arguments in the methodological literature in favor of using research templates, while either avoiding or limiting the use of causal graphical models. Third, I discuss the problems with the template model, arguing for a more flexible approach to DAGs that helps illuminating common problems in empirical settings and improving the credibility of causal claims. I demonstrate this approach in a series of worked examples, showing the gap between identification strategies as invoked by researchers and their actual applications. Finally, I conclude highlighting the benefits that routinely incorporating causal graphical models in our scientific discussions would have in terms of transparency, testability, and generativity. |
| Date: | 2024–02–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:x4526_v1 |
| By: | Serrano-Serrat, Josep |
| Abstract: | This paper examines critical aspects of analyzing interactions with continuous treatment variables. A theoretical estimand is defined, the Average Interactive Effects, which is the mean of the difference in the slopes of the treatment variable at different levels of the moderator. Crucially, this theoretical estimand is distinct from the difference in Conditional Average Marginal Effects (CAME) at different levels of the moderator. The paper proposes a flexible parametric model that can be used to estimate three important pieces of information: i) predicted values at different levels of the moderator; ii) the difference in the slopes of the relationship between the treatment and the dependent variable at different values of the moderator; and iii) the mean of these slope differences. Simulations show that this approach is better than models that only estimatethe differences in the CAMEs. However, since the unbiasedness of the proposed method depends on the correct specification of the functional form, it is proposed to complement the parametric model with results from Generalized Additive Models. Finally, the effectiveness of the proposed approach is illustrated by two examples. |
| Date: | 2024–03–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4e8h2_v1 |
| By: | Ermisch, John |
| Abstract: | Estimation of relationships between a dependent variable constructed by the aggregation of individual behaviour and aggregate independent variables such as mean income is common. The aim and contribution of the paper is to clarify when and how parameter estimates based on aggregates leads to bias and the likely degree of such bias. It demonstrates that use of aggregate data to estimate parameters associated with a model of individual behaviour when the outcome variable is binary (e.g. a birth) is not advisable. It only ‘works’ when the independent variables do not vary at the individual level (e.g. prices or the unemployment rate). Even then it requires prior distributional knowledge or assumptions. When the individual model also contains variables that vary across individuals, then the analysis in the paper suggests that all parameter estimates based solely on variation in the aggregates usually understate the size of their true value, even ones associated with variables which do not vary over individuals. Indeed, it is often the case that the 95% confidence interval of these latter parameter estimates never contains the parameter’s true value. |
| Date: | 2024–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:3hrkp_v1 |
| By: | Wiley, James Christopher; English, Simon; Church, Kinsey; Ward, Richard A; Flowers, James; Chamoun, Céline |
| Abstract: | This paper presents a method for classifying predictors Xp based on their relationship to an outcome variable Y; as having main effects, interactions, collinearity, or no effects. The presented method operates by combining complimentary information from a multivariate model and a series of bivariate models. We demonstrate how the method works using simulated data. In addition, we experimentally vary the effect sizes in our data generation process to see if the proposed method can detect different relationships between predictors Xp and outcome Y at varied strengths. We also vary the sample size (n) and observe the impact on relationship classification. We find that the proposed method functions as desired within the constraints of this study. We propose future simulation designs for continued testing of said method. We conclude by providing broad instructions for applying this method. Our goal is to use this method to develop initial analytical profiles of high-dimensional data in naïve data exploration contexts. This work stems from trying to find an efficient alternative to scatterplot matrices when exploring data that contain thousands of variables. |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:3h9yp_v1 |
| By: | Adao, Rodrigo; Costinot, Arnaud; Donaldson, Dave |
| Abstract: | The primary motivation behind quantitative modeling in international trade and many other fields is to shed light on the economic consequences of policy changes. To help assess and potentially strengthen the credibility of such quantitative predictions we introduce an IV-based goodness-of-fit measure that provides the basis for testing causal predictions in arbitrary general-equilibrium environments as well as for estimating the average misspecification in these predictions. As an illustration of how to use our IV-based goodness-of-fit measure in practice, we revisit the welfare consequences of Trump's trade war predicted by Fajgelbaum et al. (2020). |
| Keywords: | international trade; urban economics; testing economic models |
| JEL: | C52 C68 E17 F10 R10 |
| Date: | 2024–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126756 |
| By: | Evans, Clare; Leckie, George; Subramanian, SV; Bell, Andrew (University of Sheffield); Merlo, Juan |
| Abstract: | Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses. Please cite this paper as: Evans, C.R., G. Leckie, S.V. Subramanian, A. Bell, & J. Merlo. (2024.). A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). SSM - Population Health. https://doi.org/10.1016/j.ssmph.2024.101 664 |
| Date: | 2024–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:635hx_v1 |
| By: | Bell, Andrew (University of Sheffield); Evans, Clare; Holman, Daniel; Leckie, George |
| Abstract: | The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualisation of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009-2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond. |
| Date: | 2024–05–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:jq57s_v1 |