Figures index

From

Forecast of Sarima Models: Αn Application to Unemployment Rates of Greece

Chaido Dritsaki

American Journal of Applied Mathematics and Statistics. 2016, 4(5), 136-148 doi:10.12691/ajams-4-5-1
  • Figure 1. Time series plot of Greece monthly unemployment rate (Linear Trend Model UNEt=5.860+0.078*t)
  • Figure 2. Trend plot analysis of Greece monthly unemployment rate (Linear Trend Model UNEt=5.860+0.078*t)
  • Figure 3. Autocorrelation and Partial Correlation Plot of Greece’s monthly unemployment rate
  • Figure 4. Time series plot of first difference of the original data (Linear Trend Model ΔUNEt=-0.041+0.001*t)
  • Figure 5. Trend analysis of first difference of the original data (Linear Trend Model ΔUNEt=-0.041+0.001*t)
  • Figure 6. Autocorrelation and partial function of first differences of the original data
  • Figure 7. Time series plot of second differences of the original data (Linear Trend Model Δ2UNEt=0.0009-1.8E-0.5*t)
  • Figure 8. Trend analysis for second differences of the original data (Linear Trend Model Δ2UNEt=0.0009-1.8E-0.5*t)
  • Figure 9. Autocorrelation and partial function of second difference of the original data
  • Figure 10. Time series plot of the seasonal difference of the second difference data (lag=12)
  • Figure 11. Trend analysis of the seasonal difference of the second difference data (Linear trend model D2SUNEt =0.088-0.0015*t)
  • Figure 12. Autocorrelation and partial function of seasonal difference of the second difference data
  • Figure 13. Diagnostic residuals’ autocorrelation test of SARIMA(0,2,1)(1,2,1)12 model
  • Figure 14. Diagnostic test for residuals’ conditional autocorrelation of SARIMA(0,2,1)(1,2,1)12 model
  • Figure 15. Dynamic Forecast of Unemployment
  • Figure 16. Static Forecast of Unemployment