Hi,
I am estimating WTP for health using the doubleb command. The command line and the results I draw are shown below
doubleb BID1 BID2 ANSWER1 ANSWER2
initial: log likelihood = -<inf> (could not be evaluated)
feasible: log likelihood = -8086.9392
rescale: log likelihood = -992.84364
rescale eq: log likelihood = -905.60652
Iteration 0: log likelihood = -905.60652
Iteration 1: log likelihood = -880.20383
Iteration 2: log likelihood = -877.53287
Iteration 3: log likelihood = -877.5177
Iteration 4: log likelihood = -877.51769
Number of obs = 318
Wald chi2(0) = .
Log likelihood = -877.51769 Prob > chi2 = .
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Beta |
_cons | 2093.911 55.61786 37.65 0.000 1984.902 2202.92
-------------+----------------------------------------------------------------
Sigma |
_cons | 939.4247 43.78531 21.46 0.000 853.6071 1025.242
------------------------------------------------------------------------------
First-Bid Variable: BID1
Second-Bid Variable: BID2
First-Response Dummy Variable: ANSWER1
Second-Response Dummy Variable: ANSWER2
If I am right, the beta coefficient is the WTP (that is 2093 euro).
Now I want to add control variables in my model. I have categorical variables for income (1=very high, 2=high 3=low 4=very low), education (1=primary 2=secondary 3=tertiary), working status (1=unemployed, 2= employed) etc.
Can I run the following command? Does it make any sense taking into account the type of my data?
doubleb BID1 BID2 ANSWER1 ANSWER2 age education work
If I do this, I take the following table
initial: log likelihood = -<inf> (could not be evaluated)
feasible: log likelihood = -14499.403
rescale: log likelihood = -1203.9853
rescale eq: log likelihood = -1169.0702
Iteration 0: log likelihood = -1169.0702
Iteration 1: log likelihood = -1144.0228
Iteration 2: log likelihood = -1140.6283
Iteration 3: log likelihood = -1140.6185
Iteration 4: log likelihood = -1140.6185
Number of obs = 318
Wald chi2(3) = 8.11
Log likelihood = -1140.6185 Prob > chi2 = 0.0437
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Beta |
income | 34.71665 55.11304 0.63 0.529 -73.30293 142.7362
education | 153.331 89.25955 1.72 0.086 -21.61448 328.2765
work | 56.95611 31.21453 1.82 0.068 -4.223234 118.1355
_cons | 1387.917 337.3133 4.11 0.000 726.7952 2049.039
---------------+----------------------------------------------------------------
Sigma |
_cons | 918.901 42.87693 21.43 0.000 834.8637 1002.938
--------------------------------------------------------------------------------
First-Bid Variable: BID1
Second-Bid Variable: BID2
First-Response Dummy Variable: ANSWER1
Second-Response Dummy Variable: ANSWER2
If the command I used is right, how do I interpret the coefficients? As education "increases" the WTP increases by 153 euros?
Any help would be precious!
Thanks!
I am estimating WTP for health using the doubleb command. The command line and the results I draw are shown below
doubleb BID1 BID2 ANSWER1 ANSWER2
initial: log likelihood = -<inf> (could not be evaluated)
feasible: log likelihood = -8086.9392
rescale: log likelihood = -992.84364
rescale eq: log likelihood = -905.60652
Iteration 0: log likelihood = -905.60652
Iteration 1: log likelihood = -880.20383
Iteration 2: log likelihood = -877.53287
Iteration 3: log likelihood = -877.5177
Iteration 4: log likelihood = -877.51769
Number of obs = 318
Wald chi2(0) = .
Log likelihood = -877.51769 Prob > chi2 = .
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Beta |
_cons | 2093.911 55.61786 37.65 0.000 1984.902 2202.92
-------------+----------------------------------------------------------------
Sigma |
_cons | 939.4247 43.78531 21.46 0.000 853.6071 1025.242
------------------------------------------------------------------------------
First-Bid Variable: BID1
Second-Bid Variable: BID2
First-Response Dummy Variable: ANSWER1
Second-Response Dummy Variable: ANSWER2
If I am right, the beta coefficient is the WTP (that is 2093 euro).
Now I want to add control variables in my model. I have categorical variables for income (1=very high, 2=high 3=low 4=very low), education (1=primary 2=secondary 3=tertiary), working status (1=unemployed, 2= employed) etc.
Can I run the following command? Does it make any sense taking into account the type of my data?
doubleb BID1 BID2 ANSWER1 ANSWER2 age education work
If I do this, I take the following table
initial: log likelihood = -<inf> (could not be evaluated)
feasible: log likelihood = -14499.403
rescale: log likelihood = -1203.9853
rescale eq: log likelihood = -1169.0702
Iteration 0: log likelihood = -1169.0702
Iteration 1: log likelihood = -1144.0228
Iteration 2: log likelihood = -1140.6283
Iteration 3: log likelihood = -1140.6185
Iteration 4: log likelihood = -1140.6185
Number of obs = 318
Wald chi2(3) = 8.11
Log likelihood = -1140.6185 Prob > chi2 = 0.0437
--------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Beta |
income | 34.71665 55.11304 0.63 0.529 -73.30293 142.7362
education | 153.331 89.25955 1.72 0.086 -21.61448 328.2765
work | 56.95611 31.21453 1.82 0.068 -4.223234 118.1355
_cons | 1387.917 337.3133 4.11 0.000 726.7952 2049.039
---------------+----------------------------------------------------------------
Sigma |
_cons | 918.901 42.87693 21.43 0.000 834.8637 1002.938
--------------------------------------------------------------------------------
First-Bid Variable: BID1
Second-Bid Variable: BID2
First-Response Dummy Variable: ANSWER1
Second-Response Dummy Variable: ANSWER2
If the command I used is right, how do I interpret the coefficients? As education "increases" the WTP increases by 153 euros?
Any help would be precious!
Thanks!
