Grandes datos, Google y desempleo
We use Google Trends data for employment opportunities related reply in order to forecast the unemployment rate in Mexico. We begin by discussing the literature related to big data and nowcasting in which user generated data is used to forecast unemployment. Afterwards, we explain the basics of seve...
| Autores principales: | , |
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| Formato: | Online |
| Idioma: | espanhol |
| Editor: |
El Colegio de México, A.C.
2020
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| Assuntos: | |
| Acesso em linha: | https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/399 |
| Recursos: |
Estudios Económicos |
| authentication_code | dc |
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| _version_ | 1853489753287032832 |
| author | Campos Vázquez, Raymundo M. López-Araiza B., Sergio E. |
| author_facet | Campos Vázquez, Raymundo M. López-Araiza B., Sergio E. |
| author_sort | Campos Vázquez, Raymundo M. |
| category_str_mv |
"Bolivia", "hyperinflation", "economic crisis", "Bolivia", "hiperinflación", "crisis económica"
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| collection | OJS |
| description | We use Google Trends data for employment opportunities related reply in order to forecast the unemployment rate in Mexico. We begin by discussing the literature related to big data and nowcasting in which user generated data is used to forecast unemployment. Afterwards, we explain the basics of several machine learning algorithms. Finally, we implement such algorithms in order to find the best model to predict unemployment using both Google Trends queries and unemployment lags. From a public policy perspective, we believe that both user generated data and new statistical methods may provide great tools for the design of policy interventions. |
| format | Online |
| id | oai:oai.estudioseconomicos.colmex.mx:article-399 |
| index_str_mv | CONAHCYT LATINDEX PKP Index DORA Redalyc Scielo México Handbook of Latin American Studies (HLAS) JSTOR Dialnet HAPI Ulrich’s International Periodicals Directory Google Scholar IBSS Gale OneFile: Informe Académico Global Issues in Context InfoTracCustom Cengage Learning EconLit Índice bibliográfico Publindex RePEc The Journal of Economic Literature |
| journal | Estudios Económicos |
| language | spa |
| publishDate | 2020 |
| publisher | El Colegio de México, A.C. |
| record_format | ojs |
| data_source_entry/ISSN | Estudios Económicos de El Colegio de México; 69-vol. 35, no. 1, january-june, 2020; 125-151 Estudios Económicos de El Colegio de México; 69-vol. 35, núm. 1, enero-junio, 2020; 125-151 0186-7202 0188-6916 |
| spelling | oai:oai.estudioseconomicos.colmex.mx:article-3992025-12-05T14:44:11Z Big data, Google and unemployment Grandes datos, Google y desempleo Campos Vázquez, Raymundo M. López-Araiza B., Sergio E. unemployment Google big data machine learning prediction C52 C53 E24 J64 O54 desempleo Google grandes datos aprendizaje automático predicción México C52 C53 E24 J64 O54 We use Google Trends data for employment opportunities related reply in order to forecast the unemployment rate in Mexico. We begin by discussing the literature related to big data and nowcasting in which user generated data is used to forecast unemployment. Afterwards, we explain the basics of several machine learning algorithms. Finally, we implement such algorithms in order to find the best model to predict unemployment using both Google Trends queries and unemployment lags. From a public policy perspective, we believe that both user generated data and new statistical methods may provide great tools for the design of policy interventions. Utilizamos datos de búsquedas en Google sobre empleo para pronosticar la tasa de desempleo en México. Discutimos la bibliografía relacionada con nowcasting y big data donde se utilizan datos generados en internet para predecir desempleo. Además, explicamos algoritmos de aprendizaje que sirven para escoger el mejor modelo de predicción. Finalmente, se aplican estos algoritmos para encontrar el modelo que mejor prediga la tasa de desempleo en México. En términos de políticas públicas, creemos que los datos generados a través de internet y los nuevos métodos estadísticos son claves para mejorar el diseño y la pertinencia de las intervenciones. El Colegio de México, A.C. 2020-01-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/399 10.24201/ee.v35i1.399 Estudios Económicos de El Colegio de México; 69-vol. 35, no. 1, january-june, 2020; 125-151 Estudios Económicos de El Colegio de México; 69-vol. 35, núm. 1, enero-junio, 2020; 125-151 0186-7202 0188-6916 spa https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/399/483 https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/399/494 |
| spellingShingle | unemployment big data machine learning prediction C52 C53 E24 J64 O54 desempleo grandes datos aprendizaje automático predicción México C52 C53 E24 J64 O54 Campos Vázquez, Raymundo M. López-Araiza B., Sergio E. Grandes datos, Google y desempleo |
| title | Grandes datos, Google y desempleo |
| title_alt | Big data, Google and unemployment |
| title_full | Grandes datos, Google y desempleo |
| title_fullStr | Grandes datos, Google y desempleo |
| title_full_unstemmed | Grandes datos, Google y desempleo |
| title_short | Grandes datos, Google y desempleo |
| title_sort | grandes datos google y desempleo |
| topic | unemployment big data machine learning prediction C52 C53 E24 J64 O54 desempleo grandes datos aprendizaje automático predicción México C52 C53 E24 J64 O54 |
| topic_facet | unemployment big data machine learning prediction C52 C53 E24 J64 O54 desempleo grandes datos aprendizaje automático predicción México C52 C53 E24 J64 O54 |
| url | https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/399 |
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