Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends

Autores/as

DOI:

https://doi.org/10.17533/udea.le.n95a343462

Palabras clave:

demanda de turismo, Google Trend, proyecciones, mixed data sampling, llegada de turistas

Resumen


Este artículo examina si los criterios de búsqueda de Google Trends son útiles para predecir la llegada mensual de turistas a Colombia. Para este fin, se compara un modelo base que utiliza como predictor los rezagos propios de la llegada de turistas con dos especificaciones alternativas: (i) el modelo base aumentado con la inclusión de datos mensuales de Google Trends; y (ii) el modelo base, pero modificado con la inclusión de datos semanales de Google Trends. Los resultados obtenidos presentan evidencia estadísticamente significativa de que los datos de Google Trends aportan beneficios a la evaluación y predicción de llegadas de turistas a Colombia. En particular, se encuentra que datos de alta frecuencia (semanales) agregan alto valor predictivo en comparación con los modelos que usan datos de la misma frecuencia (mensuales). De este modo, la industria del turismo y los encargados de la política pública de turismo pueden apoyarse de la capacidad predictiva de los datos de Google Trends para mejorar sus procesos de planeación en el corto y mediano plazo.

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Biografía del autor/a

Alexander Correa, Universidad EAN

Profesor asociado de la Universidad EAN

Citas

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Publicado

2021-05-05

Cómo citar

Correa, A. (2021). Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends. Lecturas De Economía, (95), 105–134. https://doi.org/10.17533/udea.le.n95a343462

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