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### Forecasting ARDL in EViews 10

Posted: Thu May 16, 2019 3:44 pm
My dependent variable is a ddlog number of passengers at an airport and so the forecast is of ddlog passengers. How do I convert back into a real number of passengers ? I think in EViews 11, you have the option to have the forecast produced in real numbers? But not in E-Views 10? Thanks for your help.

### Re: Forecasting ARDL in EViews 10

Posted: Thu May 16, 2019 6:19 pm
Use d(dlog(passenger)) as your dependent variable.

### Re: Forecasting ARDL in EViews 10

Posted: Fri May 17, 2019 5:46 am
Many thanks, Gareth.

How do I get a forecast of passengers for subsequent months using the lagged independent variable coefficients? For example, I want to get the forecast passengers for 12 months beyond the last month with an actual independent variable value based on the lagged coefficients?

Mike

### Re: Forecasting ARDL in EViews 10

Posted: Fri May 17, 2019 7:39 am
I don't understand the question.

Isn't that just what a forecast is?

### Re: Forecasting ARDL in EViews 10

Posted: Sat May 18, 2019 4:18 am
Gareth

Here's the equation. Pax is the dependent variable and searches is the independent variable. I have data for the independent variable up to May 2019. What I would like the forecast to do is to estimate the dependent variable for 8 months after May 2019 using the lagged coefficients but it will only forecast to May 2019.

Thanks

Mike

Dependent Variable: D(DLOG(PAX))
Method: ARDL
Date: 05/17/19 Time: 08:54
Dependent lags: 8 (Fixed)
Dynamic regressors (8 lags, fixed): DDLOGSEACHESYLWINBC
Fixed regressors: C

Variable Coefficient Std. Error t-Statistic Prob.*

D(DLOG(PAX(-1))) -1.266072 0.189484 -6.681682 0.0000
D(DLOG(PAX(-2))) -1.790391 0.274034 -6.533469 0.0000
D(DLOG(PAX(-3))) -2.078512 0.353590 -5.878316 0.0000
D(DLOG(PAX(-4))) -2.296622 0.429315 -5.349507 0.0000
D(DLOG(PAX(-5))) -1.584443 0.442105 -3.583862 0.0016
D(DLOG(PAX(-6))) -1.630204 0.341099 -4.779274 0.0001
D(DLOG(PAX(-7))) -0.688054 0.278615 -2.469555 0.0214
D(DLOG(PAX(-8))) -0.265106 0.177810 -1.490951 0.1496
DDLOGSEACHESYLWINBC 0.096754 0.032105 3.013658 0.0062
DDLOGSEACHESYLWINBC(-1) 0.128840 0.035171 3.663267 0.0013
DDLOGSEACHESYLWINBC(-2) 0.187252 0.040185 4.659723 0.0001
DDLOGSEACHESYLWINBC(-3) 0.130813 0.044496 2.939885 0.0074
DDLOGSEACHESYLWINBC(-4) 0.250937 0.045652 5.496779 0.0000
DDLOGSEACHESYLWINBC(-5) 0.144448 0.051890 2.783722 0.0106
DDLOGSEACHESYLWINBC(-6) 0.129838 0.051059 2.542881 0.0182
DDLOGSEACHESYLWINBC(-7) 0.142372 0.040723 3.496081 0.0019
DDLOGSEACHESYLWINBC(-8) 0.083309 0.029395 2.834107 0.0094
C -0.002394 0.007899 -0.303030 0.7646

R-squared 0.964200 Mean dependent var 0.002249
Adjusted R-squared 0.937739 S.D. dependent var 0.199664
S.E. of regression 0.049821 Akaike info criterion -2.860807
Sum squared resid 0.057088 Schwarz criterion -2.108507
Log likelihood 76.64655 Hannan-Quinn criter. -2.586861
F-statistic 36.43846 Durbin-Watson stat 1.870587
Prob(F-statistic) 0.000000

*Note: p-values and any subsequent tests do not account for model selection.

### Re: Forecasting ARDL in EViews 10

Posted: Sat May 18, 2019 6:33 am
Your model says you need the contemporaneous value of the independent variable to forecast the dependent variable in a given period. You don't have that. The forecast can't be done with this model.

### Re: Forecasting ARDL in EViews 10

Posted: Sun May 19, 2019 4:07 am
OK. Thanks

### Re: Forecasting ARDL in EViews 10

Posted: Tue Aug 20, 2019 10:47 am
I want to forecast with ARDL forecast function with sample size extended to 50000( the initial size was 43380),but after I ran the forecast function, the result back to the initial sample size, I didn't get more values. May I have some suggestions about this?
The Following is my ARDL result.FYI, Many thanks Eviews team!
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (11 lags, automatic): (ACGROUP1+1)
LOG(ACGROUP2+1) LOG(ACGROUP3+1) LOG(DCHI1+1)
LOG(DCHI2+1)
Fixed regressors: LOG(AVEAMB+1) C
Number of models evalulated: 2737152
Selected Model: ARDL(11, 0, 4, 4, 0, 6)
Note: final equation sample is larger than selection sample

Variable Coefficient Std. Error t-Statistic Prob.*

LOG(SUMNONITEC(-1)+1) -0.133066 0.015467 -8.603069 0.0000
LOG(SUMNONITEC(-2)+1) 0.229836 0.015682 14.65586 0.0000
LOG(SUMNONITEC(-3)+1) 0.137875 0.015966 8.635751 0.0000
LOG(SUMNONITEC(-4)+1) 0.160877 0.015271 10.53496 0.0000
LOG(SUMNONITEC(-5)+1) 0.087709 0.015446 5.678599 0.0000
LOG(SUMNONITEC(-6)+1) 0.010027 0.014861 0.674768 0.4999
LOG(SUMNONITEC(-7)+1) 0.023945 0.013939 1.717929 0.0859
LOG(SUMNONITEC(-8)+1) 0.034400 0.013615 2.526645 0.0116
LOG(SUMNONITEC(-9)+1) 0.004576 0.013577 0.337036 0.7361
LOG(SUMNONITEC(-10)+1) 0.025915 0.013137 1.972731 0.0486
LOG(SUMNONITEC(-11)+1) 0.031680 0.013043 2.428806 0.0152
ACGROUP1+1 -0.020112 0.007652 -2.628150 0.0086
LOG(ACGROUP2+1) -0.755454 0.256220 -2.948463 0.0032
LOG(ACGROUP2(-1)+1) 1.013845 0.328537 3.085942 0.0020
LOG(ACGROUP2(-2)+1) 0.139595 0.329903 0.423140 0.6722
LOG(ACGROUP2(-3)+1) 0.134588 0.327992 0.410338 0.6816
LOG(ACGROUP2(-4)+1) -0.615708 0.255283 -2.411868 0.0159
LOG(ACGROUP3+1) -4.688732 0.178070 -26.33087 0.0000
LOG(ACGROUP3(-1)+1) 1.612119 0.282312 5.710416 0.0000
LOG(ACGROUP3(-2)+1) 1.689247 0.289197 5.841165 0.0000
LOG(ACGROUP3(-3)+1) 0.554285 0.287221 1.929818 0.0537
LOG(ACGROUP3(-4)+1) 0.535534 0.201178 2.661991 0.0078
LOG(DCHI1+1) 0.001194 0.004461 0.267565 0.7890
LOG(DCHI2+1) -8.76E-06 0.009588 -0.000913 0.9993
LOG(DCHI2(-1)+1) 0.010357 0.009738 1.063627 0.2876
LOG(DCHI2(-2)+1) -0.005098 0.009700 -0.525610 0.5992
LOG(DCHI2(-3)+1) -0.010116 0.010255 -0.986455 0.3240
LOG(DCHI2(-4)+1) 0.001019 0.009582 0.106320 0.9153
LOG(DCHI2(-5)+1) 0.021299 0.009542 2.232213 0.0257
LOG(DCHI2(-6)+1) -0.021608 0.009495 -2.275787 0.0229
LOG(AVEAMB+1) 0.061792 0.012298 5.024762 0.0000
C 5.523917 0.508882 10.85500 0.0000