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@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@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



_______________________________________________
Gretl-users mailing list
Gretl-users@lists.wfu.edu
http://lists.wfu.edu/mailman/listinfo/gretl-users


_______________________________________________
Gretl-users mailing list
Gretl-users@lists.wfu.edu
http://lists.wfu.edu/mailman/listinfo/gretl-users



_______________________________________________
Gretl-users mailing list
Gretl-users@lists.wfu.edu
http://lists.wfu.edu/mailman/listinfo/gretl-users


_______________________________________________
Gretl-users mailing list
Gretl-users@lists.wfu.edu
http://lists.wfu.edu/mailman/listinfo/gretl-users