Downloads provided by UsageCounts
handle: 10017/50450
En este trabajo se realiza un modelo de factores dinámicos de escala grande para calcular predicciones, a corto plazo, de la tasa de crecimiento del PIB español. Con este modelo, se realiza un ejercicio de Nowcasting en pseudo-tiempo real para realizar predicciones, durante el periodo de tiempo 2015-2019, de la tasa de crecimiento del PIB español. Además, se realiza una comparación de predicción de este modelo con un modelo de paseo aleatorio y un modelo ARIMA, resultando el DFM ganador. Se concluye que el modelo puede ser apto para realizar predicciones del PIB español.
In this paper, a large-scale dynamic factor model is carried out to calculate short-term predictions of the Spanish GDP growth rate in real time. With this model, a Nowcasting exercise is carried out in pseudo-real time to make predictions, during the period 2015-2019, of the Spanish GDP growth rate. Furthermore, a prediction comparison of this model is performed with a random walk model and an ARIMA model, resulting in the winning DFM. It is concluded that the model may be adequate for making predictions of the Spanish GDP.
Máster Universitario en Análisis Económico Aplicado (M210)
Principal components, PIB, Economics, GDP, Economía, Componentes principales, Indicadores económicos, Modelo de factores dinámicos, Economics indicators, Forecast, Predicción, Nowcasting, Dynamic factor model
Principal components, PIB, Economics, GDP, Economía, Componentes principales, Indicadores económicos, Modelo de factores dinámicos, Economics indicators, Forecast, Predicción, Nowcasting, Dynamic factor model
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 120 | |
| downloads | 135 |

Views provided by UsageCounts
Downloads provided by UsageCounts