Announcement

Collapse
No announcement yet.
X
  • Filter
  • Time
  • Show
Clear All
new posts

  • actual vs predicted value

    Dear all,
    i want to compare the actual with the predicted mean hours. I run the following model:
    PHP Code:
    mixlogit didep hours if sex==marital_status==1rand(disposable_incomeid (idperson
    > ) group(strata)

    Iteration 0:   log likelihood = -786.53313  (not concave)
    Iteration 1:   log likelihood = -595.57084  (not concave)
    Iteration 2:   log likelihood = -368.88205  (not concave)
    Iteration 3:   log likelihood = -326.03393  
    Iteration 4
    :   log likelihood = -324.29482  
    Iteration 5
    :   log likelihood = -322.96199  
    Iteration 6
    :   log likelihood = -322.80673  
    Iteration 7
    :   log likelihood = -322.80599  
    Iteration 8
    :   log likelihood = -322.80599  

    Mixed logit model                                 Number of obs   
    =       1455
                                                      LR chi2
    (1)      =       0.00
    Log likelihood 
    = -322.80599                       Prob chi2     =     0.9875

    ------------------------------------------------------------------------------
           
    didep |      Coef.   StdErr.      z    P>|z|     [95ConfInterval]
    -------------+----------------------------------------------------------------
    Mean         |
           
    hours |  -.1618746    .010528   -15.38   0.000    -.1825091   -.1412401
    disposable
    ~|   .0035452   .0003435    10.32   0.000     .0028719    .0042186
    -------------+----------------------------------------------------------------
    SD           |
    disposable~|   7.67e-06   .0004909     0.02   0.988    -.0009546    .0009699
    ------------------------------------------------------------------------------ 
    If i use the mixlpred.. i get the didep_hat .
    How i get the predicted mean hours?

    Thank you.

  • #2
    In your model you use hours to predict didep, so hours is treated as given and thus cannot be predicted from your model.
    ---------------------------------
    Maarten L. Buis
    University of Konstanz
    Department of history and sociology
    box 40
    78457 Konstanz
    Germany
    http://www.maartenbuis.nl
    ---------------------------------

    Comment


    • #3
      The hours is a variable that takes the values 10, 20, 30 ( 3 choices) and the variable didep is a categorical variable that takes the value 1 for the alternative of the 3 choices of the hours.
      So by the model i get the prediction for the didep?
      Is there any way to get the prediction of the hours?

      Comment


      • #4
        With models like the one you are using there is typically a dependent/explained/left-hand-side/y-variable and one or more independent/explanatory/right-hand-side/x-variables. In your model hours is an explanatory variable, so it is considered as given. If something is given it cannot be predicted other than trivially use an exact copy of the original variable. So the answer to your questions are yes, it predict didep, and no hours cannot be (meaningfully) predicted.
        ---------------------------------
        Maarten L. Buis
        University of Konstanz
        Department of history and sociology
        box 40
        78457 Konstanz
        Germany
        http://www.maartenbuis.nl
        ---------------------------------

        Comment


        • #5
          I didn't explain my model correctly i think.
          The model that i wanted to estimate is the discrete labour supply model following van Soest (1995), Aaberge et al.(1995, 1999), Hoynes (1996), Blundell et al. (2000), Eissa and Hoynes (2004) Eissa et al. (2008).
          we adopt the flexible quadratic utility function U(H,Y, Z) where h are the hours and y the income.
          As we estimate a discrete choice model, we must first decide the finite set of hours according to which individuals choose their hours. For each choice hi we have the analogous disposable income yi.

          To estimate the model we must add stochastic terms to the utility function. In what follows, we only add shocks specific to the state or hours regime for each of the possible choices, which we assume are generated by extreme value distributions. Following these assumptions, we derive the choice probability for agent i.
          I used the mixelogit to estimate all the parameters in the model.

          Comment


          • #6
            The code you gave us and the description you just sent us are so far removed from one another that I don't think it is possible to help you through a forum like this. Forums like these are great, but like any form of comunication they have their weak points; Forums are good at small quick questions, but they don't work well when extensive consulting/teaching is needed. What you need is face to face consulting. So I suggest you find someone local who can help you.
            ---------------------------------
            Maarten L. Buis
            University of Konstanz
            Department of history and sociology
            box 40
            78457 Konstanz
            Germany
            http://www.maartenbuis.nl
            ---------------------------------

            Comment

            Working...
            X