Modelo VAR para el pronóstico de la tasa de Desempleo, PBI e IPC en el Perú

• Autor: Castillo Yglesias, Elvis Edgardo
• Formato: tesis de grado
• Fecha de Publicación: 2023
• Institución: Universidad Nacional de Trujillo
• Repositorio: UNITRU-Tesis
• Lenguaje: español
• OAI Identifier: oai:dspace.unitru.edu.pe:20.500.14414/18557
• Enlace del recurso: https://hdl.handle.net/20.500.14414/18557
• Nivel de acceso: acceso abierto
• Materia: Modelo Vectorial Autorregresivo; PBI; IPC; TD

The main objective of this research work is to determine the vector autoregressive _x000D_ models (VAR) that best predicts the unemployment rate, GDP and CPI in Peru. With the help _x000D_ of the R programming language applied in RStudio. The methodology of VAR models was used, _x000D_ where the analysis was consolidated in graphs, tests such as Dickey Fuller, Phillips Perron and _x000D_ the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test to determine if a time series is stationary _x000D_ around a trend, for the validation of the model it was incurred to apply the three basic tests _x000D_ through which a model must pass (VAR), the Jarque-Bera normality test, No Autocorrelation _x000D_ and the Heteroskedasticity test.In the process of searching for a VAR model, a residual _x000D_ normality problem was incurred since the Jarque-Bera test showed a p – value = 0.00, _x000D_ consequently, impulse variables were applied to correct the normality problems of the residuals, In this way, a VAR(5) model was obtained, which was determined through the Akaike and Schwarz criteria, therefore the forecast was made