Digital Divide: Empirical Study of CIUS 2020

Classés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.

Les articles les plus proches

1. Apparenté Pas pertinent

   A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising

   Caio Waisman, Brett R. Gordon · 2023 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Experiments are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance, extant empirical approaches may not produce the estimates a decision-maker needs to solve their problem. For example, these experimental designs are common in digital advertising settings, but they are uninformative of decisions regarding the intensive margin -- how much should be spent or how many consumers should be reached with a campaign. We propose a solution that combines a novel multi-cell experimental design with modern estimation techniques that enables decision-makers to recover enough information to solve problems with an intensive margin. Our …

2. Apparenté Pas pertinent

   Understanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19

   Avik Das, Dr. Devanjali Nandi Das · 2022 · International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 6, pp.c261-c269, June 2022 · Résumé IA disponible

   Résumé scientifique

   Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivariate BEKK and t- DCC (1,1) GARCH (1,1) models have been used. Our research shows how the dynamics of volatility spillover between India and the G7 countries shift before and during COVID-19. Findings: The findings reveal that the extent of volatility spillover has altered during COVID compared to the pre-COVID environment. During this pande…

3. Apparenté Pas pertinent

   Deep Learning Macroeconomics

   Rafael R. S. Guimaraes · 2022 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We expl…

4. Apparenté Pas pertinent

   Modeling Macroeconomic Variations After COVID-19

   Serena Ng · 2021 · Économétrie · Résumé IA disponible

   Résumé scientifique

   The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use covid indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and covid uncertainty has receded. Dynamic…

5. Apparenté Pas pertinent

   Econometric Inference with Machine-Learned Proxies: Partial Identification via Data Combination

   Lixiong Li · 2026 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Empirical researchers increasingly use upstream machine-learning (ML) methods to construct proxies for latent target variables from complex, unstructured data. A naive plug-in use of such proxies in downstream econometric models, however, can lead to biased estimation and invalid inference. This paper develops a framework for partial identification and inference in general moment models with ML-generated proxies. Our approach does not require restrictive assumptions on the upstream ML procedure, such as consistency or known convergence rates, nor does it require a complete validation sample containing all variables used in the downstream analysis. Instead, we assume access to two datasets: a…

6. Apparenté Pas pertinent

   Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap

   Alexius Wadell, Matthew Guttenberg, Christopher P. Kempes et al. · 2022 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the US at a county-level resolution. Surprisingly, we find that the current EV infrastructure deployment scales super-linearly with population, deviating from the sub-linear scaling of gasoline stations and other infrastructure. We discuss how this demonstrates the infancy of EV station abundance compared to other mature transportation infrastructures. By considering the power delivery of existing gasoline stations, and appropriate EV…

7. Apparenté Pas pertinent

   Robust Synthetic Control

   Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen · 2017 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided. To begin, we establish the condition under which the fundamental assumption in synthetic control-like approaches holds, i.e. when the lin…

8. Apparenté Pas pertinent

   Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

   Vasilis Syrgkanis, Victor Lei, Miruna Oprescu et al. · 2019 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all …