The estimated equation is
Model 1: OLS, using observations 1950-1957 (T = 8)
Dependent variable: Stock
coefficient std. error t-ratio p-value
--------------------------------------------------------
const 225.000 6.21177 36.22 2.95e-08 ***
Time -6.25000 0.976086 -6.403 0.0007 ***
Mean dependent var 190.6250 S.D. dependent var 22.90313
Sum squared resid 468.7500 S.E. of regression 8.838835
R-squared 0.872340 Adjusted R-squared 0.851064
F(1, 6) 41.00000 P-value(F) 0.000684
Log-likelihood -27.63402 Akaike criterion 59.26804
Schwarz criterion 59.42692 Hannan-Quinn 58.19644
rho -0.010000 Durbin-Watson 1.650000
Now substitute time values in the estimated equation
225- 6.25 X
Give X values like 1 , 2 3 up to 10
You will get predicted values like this ( by manual calculation)
when X is 5 ( value for 1954 ) the estimated value is 193.75. When X is 6 (
value for 1955) the predicted value is 187.5. By OLS this is the answer.
Click here to Reply or Forward
On Sun, May 18, 2014 at 4:00 PM, Huffelpuff <huffelpuff420(a)gmail.com> wrote:
Hi again,
Am I missing something? In my case the data is not actually interpolated.
I doesn't give me the "predicted" values for 55 and 56. It only shows the
fitted values for the data I provided, but not the data I'm missing. Here
is my output:
For 95% confidence intervals, t(7, 0,025) = 2,365
datavalues prediction std. error 95% interval
1 215,00 24,31 121,471 -262,93 - 311,54
2 220,00 48,61 122,030 -239,94 - 337,17
3 200,00 72,92 122,957 -217,83 - 363,66
4 195,00 97,22 124,243 -196,56 - 391,01
5 190,00 170,14 130,132 -137,58 - 477,85
6 185,00 194,44 132,723 -119,40 - 508,29
7 170,00 218,75 135,600 -101,89 - 539,39
8 150,00 243,06 138,744 -85,02 - 571,13
Forecast evaluation statistics
Mean Error 56,944
Mean Squared Error 12871
Root Mean Squared Error 113,45
Mean Absolute Error 94,757
Mean Percentage Error 24,365
Mean Absolute Percentage Error 48,319
Theil's U 7,1394
Bias proportion, UM 0,25193
Regression proportion, UR 0,74351
Disturbance proportion, UD 0,0045524
As you can see, the forecast only shows the fitted values (8 value). It
should be 10 values with the 1955 and 1956 values. Note the my current OLS
estimation is very poor (see the bad prediction values), but that doesn't
matter now.
Peter
On 2014/05/18 12:24 PM, Narandra Dashora wrote:
Peter
I used GRETL software . First I created the sheet on Excel and imported
the file on GRETL. Then I used the window Ordinary least Square and got
the regression results. The again I used OLS and click Forecasts . The
results have been the prediction for 1955-56. . Please feel free to
communicate . I am from India therefore there may be time lag in
communication
On Sun, May 18, 2014 at 11:20 AM, Huffelpuff <huffelpuff420(a)gmail.com>wrote:
> Hi,
>
> Thanks for this Narandra. I'm still a bit confused how you actually did
> the interpolation.
>
> This is what I've done:
> 1. Create a dataset with 8 entries. The dataset has a variable with the
> indexes (1, 2, 3, 4, 7, 8, 9, 10) and a second variable that holds the
> stock prices.
> 2. I then estimate a model (for instance AR(1) or ARIMA) and used the
> stock prices as dependent variable and the indexes as the regressor.
> 3. Once estimated, I tried to forecast the values, but it only calculates
> the values for the given indexes, but not for index 5 and 6.
>
> How exactly did you get the interpolated values? Directly via the GUI or
> with a hansl script?
>
> Peter
>
>
>
>
>
> On 2014/05/17 04:42 PM, Narandra Dashora wrote:
>
> Give time series a numerical value such as 1 for 1950 and so on , But
> Give 1956 the value 7 . This will give you regression equation
> The put in the value of X. The solution of your problem may be by using
> Time Stock 1 215 2 220 3 200 4 195 7 190 8 185 9 170 10 150
>
>
>
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