大数据变量的使用是否改善了货币政策的估计？来自墨西哥的证据

IF 0.9 Q3 ECONOMICS

Economics and Business Letters Pub Date : 2021-12-09 DOI:10.17811/ebl.10.4.2021.383-393

Luis Alberto Delgado-de-la-Garza, Gonzalo Adolfo Garza-Rodríguez, Daniel Alejandro Jacques-Osuna, Alejandro Múgica-Lara, C. A. Carrasco

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引用次数: 0

摘要

我们分析了引入使用大数据生成的非传统市场注意力（NCMA）指数的货币政策模型的性能改进。为了实现这一目标，我们通过文本挖掘墨西哥银行的会议记录提取了热门关键词。然后，我们根据排名靠前的关键词和相关查询，使用谷歌搜索信息生成NCMA索引。最后，我们将NCMA指数作为协变量引入货币政策的双变量probit模型中，并对比几种规范来检验模型估计的改进。我们的结果证明了NCMA指数的统计显著性，其中扩展模型的表现优于仅包括传统经济和金融变量的模型。

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Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico

We analyse the performance improvement on a monetary policy model of introducing non-conventional market attention (NCMA) indices generated using big data. To address this aim, we extracted top keywords by text mining Banco de Mexico’s minutes. Then, we used Google search information according to the top keywords and related queries to generate NCMA indices. Finally, we introduce as covariates the NCMA indices into a bivariate probit model of monetary policy and contrast several specifications to examine the improvement in the model estimates. Our results show evidence of the statistical significance of the NCMA indices where the expanded model performed better than models only including conventional economic and financial variables.

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来源期刊

Economics and Business Letters ECONOMICS-

CiteScore

1.80

自引率

11.10%

发文量

期刊介绍： Economics and Business Letters is an open access journal that publishes both theoretical and empirical quality original papers in all economics and business fields. In addition, relevant discussions on current policy issues will be considered for the Policy Watch section. As general strategy of EBL, the journal will launch calls for papers for special issues on topics of interest, generally with invited guest editors. The maximum length of the letters is limited to 2,500 words.