KULJLIVIILUPUISI Considering the following table forecast_accuracy(fc, test.values) #> { 'mape': 0.02250131357314834, #> 'me': 3.230783108990054, #> 'mae': 4.548322194530069, #> 'mpe': 0.016421001932706705, #> 'rmse': 6.373238534601827, #> 'acfl': 0.5105506325288692, #> 'corr': 0.9674576513924394, #> 'minmax': 0.02163154777672227} If MAPE is 2.2% It implies the model is about 2.2% accurate in predicting the next observations It implies the model is about 97.8% accurate in predicting the next observations O the model does not predict very well we cannot say nothing here Invest to income 1.5 1.2 0.9 0.6 0.3 0- -0.3 -0.6 0 2 4 6 8 10 How we can comment on the investment shock on income the response is positive the reponse is negative the VAR that produces this shock is unstable the response is not statistically significant KULJLIVIILUPUISI Considering the following table forecast_accuracy(fc, test.values) #> { 'mape': 0.02250131357314834, #> 'me': 3.230783108990054, #> 'mae': 4.548322194530069, #> 'mpe': 0.016421001932706705, #> 'rmse': 6.373238534601827, #> 'acfl': 0.5105506325288692, #> 'corr': 0.9674576513924394, #> 'minmax': 0.02163154777672227} If MAPE is 2.2% It implies the model is about 2.2% accurate in predicting the next observations It implies the model is about 97.8% accurate in predicting the next observations O the model does not predict very well we cannot say nothing here Invest to income 1.5 1.2 0.9 0.6 0.3 0- -0.3 -0.6 0 2 4 6 8 10 How we can comment on the investment shock on income the response is positive the reponse is negative the VAR that produces this shock is unstable the response is not statistically significant