Large mixed-frequency VARs with a parsimonious time-varying parameter structure

Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

Evaluación de un clasificador de textos digitales basado en el contenido semántico a través de ontologías

Nowadays, the generation of information through digital text documents has increased exponentially, so there is a need to store documents in mass storage devices such as high capacity hard discs, storage servers, the cloud and others. However, the storage that is carried out lacks a thematic organization, therefore, a search for information becomes complex. Given this problem, this publication describes the development of a system that has the purpose of classifying a digital text document based on the thematic content. This system implements ontologies to achieve a better classification by taking advantage of its characteristics. The system is divided into five tasks: the first is the implementation of a word count to create a frequency vector; The second task performs a refinement on the frequency vector to eliminate the sentence connectors and prepositions; the third task orders the vector from the highest to the lowest frequency; the fourth task takes the most significant set of frequencies vector, in which the ontology of a domain is applied and the relation that the words have to determine the thematic of the document is sought; and the fifth task is to organize the documents in a folder structure based on the identified domains. The system was developed with the incremental development methodology. To validate the operation of the system, a set of tests was carried out in a controlled scenario in order to verify the correct classification of the documents.

Exploring the influence of economic policy uncertainty on the relationship between tourism and economic growth with an MF-VAR model

Using a mixed-frequency vector autoregressive (MF-VAR) model, this article attempts to determine whether or not the relationship between tourism and economic growth changes in the presence or absence of economic policy uncertainty (EPU) shock. Moreover, we further our analysis by focusing on whether or not there is a significant difference in the distinct impact intensity of Hong Kong, Chinese, and global EPU. The study period spans April 1998 to March 2018. The results indicate the following. First, the existence of Hong Kong, Chinese, and global EPU does not affect the direction of the impulse response; rather, its primary influence is on the size of the impact. Second, the different ranges of EPU have different impact intensities. Third, compared to the MF-VAR model, the quarterly frequency vector autoregressive model does not fully capture the impact of EPU, especially the negative impact of global EPU on tourism. Therefore, policymakers and tourism stakeholders should develop targeted marketing plans to maintain expected tourism demand if economic uncertainty increases.