{"title":"Perturbating and Estimating DSGE Models in Julia","authors":"Alvaro Salazar-Perez, Hernán D. Seoane","doi":"10.1007/s10614-024-10632-2","DOIUrl":null,"url":null,"abstract":"<p>This paper illustrates the power of Julia language for the solution and estimation of Dynamic Stochastic General Equilibrium models. We document large gains of the Julia implementation of Perturbation solution (first and higher orders) and Bayesian estimation using two workhorse models in the literature: the Real Business Cycle Model and a medium scale New-Keynesian Model. We release a companion package that implements 1st, 2nd a 3rd order approximation of Dynamic Stochastic General Equilibrium models and allows for estimation of (log-)linearized models using Sequential Monte-Carlo Methods. Our examples highlight that Julia has low entry costs and it is a language where it is easy to deal with parallelization.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"33 1","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s10614-024-10632-2","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper illustrates the power of Julia language for the solution and estimation of Dynamic Stochastic General Equilibrium models. We document large gains of the Julia implementation of Perturbation solution (first and higher orders) and Bayesian estimation using two workhorse models in the literature: the Real Business Cycle Model and a medium scale New-Keynesian Model. We release a companion package that implements 1st, 2nd a 3rd order approximation of Dynamic Stochastic General Equilibrium models and allows for estimation of (log-)linearized models using Sequential Monte-Carlo Methods. Our examples highlight that Julia has low entry costs and it is a language where it is easy to deal with parallelization.
本文展示了 Julia 语言在动态随机一般均衡模型求解和估计方面的强大功能。我们利用文献中的两个主要模型：实际商业周期模型和中等规模的新凯恩斯主义模型，记录了 Julia 实现扰动求解（一阶和高阶）和贝叶斯估计的巨大收益。我们发布的配套软件包实现了动态随机一般均衡模型的一阶、二阶和三阶近似，并允许使用序列蒙特卡洛方法对（对数）线性化模型进行估计。我们的示例突出表明，Julia 的入门成本很低，是一种易于处理并行化问题的语言。
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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