Forecasting ARDL in EViews 10
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 Joined: Thu May 16, 2019 3:35 pm
Forecasting ARDL in EViews 10
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 EViews 10? Thanks for your help.

 Fe ddaethom, fe welon, fe amcangyfrifon
 Posts: 12291
 Joined: Tue Sep 16, 2008 5:38 pm
Re: Forecasting ARDL in EViews 10
Use d(dlog(passenger)) as your dependent variable.
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 Posts: 4
 Joined: Thu May 16, 2019 3:35 pm
Re: Forecasting ARDL in EViews 10
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
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

 Fe ddaethom, fe welon, fe amcangyfrifon
 Posts: 12291
 Joined: Tue Sep 16, 2008 5:38 pm
Re: Forecasting ARDL in EViews 10
I don't understand the question.
Isn't that just what a forecast is?
Isn't that just what a forecast is?
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 Joined: Thu May 16, 2019 3:35 pm
Re: Forecasting ARDL in EViews 10
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
Sample (adjusted): 2015M11 2019M03
Included observations: 41 after adjustments
Dependent lags: 8 (Fixed)
Dynamic regressors (8 lags, fixed): DDLOGSEACHESYLWINBC
Fixed regressors: C
Variable Coefficient Std. Error tStatistic 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
Rsquared 0.964200 Mean dependent var 0.002249
Adjusted Rsquared 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 HannanQuinn criter. 2.586861
Fstatistic 36.43846 DurbinWatson stat 1.870587
Prob(Fstatistic) 0.000000
*Note: pvalues and any subsequent tests do not account for model selection.
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
Sample (adjusted): 2015M11 2019M03
Included observations: 41 after adjustments
Dependent lags: 8 (Fixed)
Dynamic regressors (8 lags, fixed): DDLOGSEACHESYLWINBC
Fixed regressors: C
Variable Coefficient Std. Error tStatistic 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
Rsquared 0.964200 Mean dependent var 0.002249
Adjusted Rsquared 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 HannanQuinn criter. 2.586861
Fstatistic 36.43846 DurbinWatson stat 1.870587
Prob(Fstatistic) 0.000000
*Note: pvalues and any subsequent tests do not account for model selection.

 Nonnormality and collinearity are NOT problems!
 Posts: 3497
 Joined: Wed Sep 17, 2008 2:25 pm
Re: Forecasting ARDL in EViews 10
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.

 Posts: 4
 Joined: Thu May 16, 2019 3:35 pm
Re: Forecasting ARDL in EViews 10
OK. Thanks

 Posts: 1
 Joined: Tue Aug 20, 2019 10:07 am
Re: Forecasting ARDL in EViews 10
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 tStatistic 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) 8.76E06 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
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 tStatistic 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) 8.76E06 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
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