Résumé IA · arXiv: 2208.13012v1 · econ.EM
A Descriptive Method of Firm Size Transition Dynamics Using Markov Chain
Posez des questions sur cet article à notre assistant IA
Comment discuter avec cet article ? Guide illustré
Vous pouvez aussi discutez avec plusieurs papiers à la fois ici.
Gardez cette conversation — un compte gratuit conserve vos questions et réponses, vos collections d’articles et vos alertes.
Créer un compte gratuitOutils pour cet article
Outils avancés Premium
Pour suivre un domaine de près : alertes de citations, diapositives de journal club, comparaison des versions et envoi vers Zotero. Découvrir Premium
Les articles autour de celui-ci
Les voir sur une carteClassés 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
-
Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job DisplacementBrantly Callaway · 2020 · Économétrie · Résumé IA disponible
Résumé scientifique
This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes -- an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for distributional treatment effect parameters than existing bounds and (ii) provide a more plausible set of conditions than existing methods that obtain point identification. I apply these bounds to study heterogeneity in the effect of …
-
Deep Learning MacroeconomicsRafael 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…
-
On Bayesian Filtering for Markov Regime Switching ModelsNigar Hashimzade, Oleg Kirsanov, Tatiana Kirsanova et al. · 2024 · Économétrie · Résumé IA disponible
Résumé scientifique
This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Keynesian DSGE model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. We show …
-
Modeling Macroeconomic Variations After COVID-19Serena 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…
-
Robust decision-making under risk and ambiguityMaximilian Blesch, Philipp Eisenhauer · 2021 · Économétrie · Résumé IA disponible
Résumé scientifique
Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model's implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model's transition dynamics.
-
Nonparametric Tests of Tail Behavior in Stochastic Frontier ModelsWilliam, C. Horrace, Yulong Wang · 2020 · Économétrie · Résumé IA disponible
Résumé scientifique
This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.
-
Machine Learning Estimation of Heterogeneous Treatment Effects with InstrumentsVasilis 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 …
-
Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure GapAlexius 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…
Aller plus loin
Comment ces articles sont-ils trouvés ?
Nous comparons le titre et le résumé de cet article avec ceux de tous les articles résumés sur ce site, et avec les articles que Semantic Scholar recommande, ceux qu’il cite et ceux qui le citent (pour un article très cité, ses citations les plus récentes et, depuis OpenAlex, les plus citées). Parmi les travaux antérieurs et les suites, les articles qui s’utilisent en profondeur, une méthode qu’il utilise et les suites très citées remontent un peu. Les niveaux (très proche, proche, apparenté) viennent de la similarité des textes, pas d’un jugement de qualité ; * signale un article sans résumé, comparé par son titre. La phrase sous un article est écrite par une IA à partir des deux résumés : vérifiez-la dans les articles. Un article jugé hors sujet par trois lecteurs quitte la liste.
Pour l’instant seulement parmi les articles résumés sur ce site : relancez la recherche pour ajouter les travaux antérieurs, les suites et les recommandations de Semantic Scholar.
Le résumé précédent a été créé il y a plus d'un an et peut être réexécuté (si nécessaire) en cliquant sur le bouton Exécuter ci-dessous.
Avertissement : Notre outil de synthèse basé sur l'IA et l'assistant virtuel fournis sur ce site Web peuvent ne pas toujours fournir des résumés complets ou des réponses exactes. Nous vous encourageons à examiner attentivement et à évaluer le contenu généré pour vous assurer de sa qualité et de sa pertinence par rapport à vos besoins.