Modeling Macroeconomic Variations After COVID-19

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   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…

2. Proche 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…

3. Apparenté Pas pertinent

   On Bayesian Filtering for Markov Regime Switching Models

   Nigar 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 …

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   Robust decision-making under risk and ambiguity

   Maximilian 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.

5. Apparenté Pas pertinent

   Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement

   Brantly 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 …

6. Apparenté Pas pertinent

   How does stock market volatility react to oil shocks?

   Andrea Bastianin, Matteo Manera · 2018 · Économétrie · Résumé IA disponible

   Résumé scientifique

   We study the impact of oil price shocks on the U.S. stock market volatility. We jointly analyze three different structural oil market shocks (i.e., aggregate demand, oil supply, and oil-specific demand shocks) and stock market volatility using a structural vector autoregressive model. Identification is achieved by assuming that the price of crude oil reacts to stock market volatility only with delay. This implies that innovations to the price of crude oil are not strictly exogenous, but predetermined with respect to the stock market. We show that volatility responds significantly to oil price shocks caused by unexpected changes in aggregate and oil-specific demand, whereas the impact of supp…

7. Apparenté Pas pertinent

   Nonparametric Tests of Tail Behavior in Stochastic Frontier Models

   William, 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.

8. Apparenté Pas pertinent

   A Descriptive Method of Firm Size Transition Dynamics Using Markov Chain

   Boyang You, Kerry Papps · 2022 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Social employment, which is mostly carried by firms of different types, determines the prosperity and stability of a country. As time passing, the fluctuations of firm employment can reflect the process of creating or destroying jobs. Therefore, it is instructive to investigate the firm employment (size) dynamics. Drawing on the firm-level panel data extracted from the Chinese Industrial Enterprises Database 1998-2013, this paper proposes a Markov-chain-based descriptive approach to clearly demonstrate the firm size transfer dynamics between different size categories. With this method, any firm size transition path in a short time period can be intuitively demonstrated. Furthermore, by utili…