model in WTP-space and ASCs
Posted: 03 Jan 2022, 14:10
Hi
I have specified the following model;
V = list()
V[['alt1']] = Certain *(cost_B*(asc_B + torsk_B * KT1 + Cost1 + laks_B * VL1 + bunn_B * HB1 + land_B * KL1))+(1-Certain)*(cost_T*(asc_T + torsk_T * KT1 + Cost1 + laks_T * VL1 + bunn_T * HB1 + land_T * KL1))
V[['alt2']] = Certain * (cost_B*(torsk_B * KT2 + Cost2 + laks_B * VL2 + bunn_B * HB2 + land_B * KL2))+(1-Certain)* (cost_T*(torsk_T * KT2 + Cost2 + laks_T * VL2 + bunn_T * HB2 + land_T * KL2))
V[['alt3']] = Certain * (cost_B*(torsk_B * KT3 + Cost3 + laks_B * VL3 + bunn_B * HB3 + land_B * KL3))+
(1-Certain)* (cost_T*(torsk_T * KT3 + Cost3 + laks_T * VL3 + bunn_T * HB3 + land_T * KL3))
Certain and (1-Certain) denote two different sub-samples, each with their separate attribute coefficients (_B for baseline, _T for treatment). The attributes are KT, HB, KL, VL, Cost. In addition I have an ASC in alternative 1 for each sub-sample, indicating a preference for the SQ.
When I estimate this model, one set of attribute coefficients "explode", whereas the other behave more "normal". This is an example from one estimation:
Model run using Apollo for R, version 0.2.1 on Windows by maa033
www.ApolloChoiceModelling.com
Model name : MMNL_aqua_exp
Model description : MMNL model with fixed price
Model run at : 2022-01-03 13:53:21
Estimation method : bfgs
Model diagnosis : successful convergence
Number of individuals : 293
Number of observations : 2599
Number of cores used : 3
Number of inter-individual draws : 200 (SobolOwenFaureTezuka)
LL(start) : -2855.293
LL(0) : -2855.293
LL(final) : -1935.703
Rho-square (0) : 0.3221
Adj.Rho-square (0) : 0.3151
AIC : 3911.41
BIC : 4028.66
Estimated parameters : 20
Time taken (hh:mm:ss) : 00:11:36.62
pre-estimation : 00:00:29.09
estimation : 00:05:39.16
post-estimation : 00:05:28.37
Iterations : 192
Min abs eigenvalue of Hessian : 0.028502
Some eigenvalues of Hessian are positive, indicating potential problems!
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_B -1.988460 NA NA NA NA
asc_T -628.727387 NA NA NA NA
cost_B -0.647537 NA NA NA NA
torsk_B_mu -1.634535 NA NA NA NA
torsk_B_sig -1.628396 NA NA NA NA
laks_B_mu -1.651847 NA NA NA NA
laks_B_sig -1.570543 NA NA NA NA
bunn_B_mu 0.185837 NA NA NA NA
bunn_B_sig 1.000276 NA NA NA NA
land_B_mu -4.838971 NA NA NA NA
land_B_sig -1.911782 NA NA NA NA
cost_T 0.005645 NA NA NA NA
torsk_T_mu -146.111788 NA NA NA NA
torsk_T_sig -134.243391 NA NA NA NA
laks_T_mu -10.857449 NA NA NA NA
laks_T_sig -0.050540 NA NA NA NA
bunn_T_mu -11.771550 NA NA NA NA
bunn_T_sig -0.783184 NA NA NA NA
land_T_mu -4.654447 NA NA NA NA
land_T_sig -13.262613 NA NA NA NA
I have run the model and got non-NAs for std.errors etc.
However, if I specify the ASCs in preference space, while letting the attributes remain in WTP-space, the estimated coefficients turn out nicely and are comparable. Hence, I wonder why the model behave OK when ASCs are NOT included in WTP-space, whereas it collapse when they are included in WTP-space? I have tried various distributions of the attributes, have tried with log-normally distributed cost parameter, a common cost-parameter, etc. The result is always the same; I have to take the ASCs out of the WTP-formulation of the model and specify them in preference space.
So I wonder if there is a way to include the ASCs in the WTP-space part of the model and still get reasonable estimation results?
Thank you in advance for any response to this request.
Margrethe
I have specified the following model;
V = list()
V[['alt1']] = Certain *(cost_B*(asc_B + torsk_B * KT1 + Cost1 + laks_B * VL1 + bunn_B * HB1 + land_B * KL1))+(1-Certain)*(cost_T*(asc_T + torsk_T * KT1 + Cost1 + laks_T * VL1 + bunn_T * HB1 + land_T * KL1))
V[['alt2']] = Certain * (cost_B*(torsk_B * KT2 + Cost2 + laks_B * VL2 + bunn_B * HB2 + land_B * KL2))+(1-Certain)* (cost_T*(torsk_T * KT2 + Cost2 + laks_T * VL2 + bunn_T * HB2 + land_T * KL2))
V[['alt3']] = Certain * (cost_B*(torsk_B * KT3 + Cost3 + laks_B * VL3 + bunn_B * HB3 + land_B * KL3))+
(1-Certain)* (cost_T*(torsk_T * KT3 + Cost3 + laks_T * VL3 + bunn_T * HB3 + land_T * KL3))
Certain and (1-Certain) denote two different sub-samples, each with their separate attribute coefficients (_B for baseline, _T for treatment). The attributes are KT, HB, KL, VL, Cost. In addition I have an ASC in alternative 1 for each sub-sample, indicating a preference for the SQ.
When I estimate this model, one set of attribute coefficients "explode", whereas the other behave more "normal". This is an example from one estimation:
Model run using Apollo for R, version 0.2.1 on Windows by maa033
www.ApolloChoiceModelling.com
Model name : MMNL_aqua_exp
Model description : MMNL model with fixed price
Model run at : 2022-01-03 13:53:21
Estimation method : bfgs
Model diagnosis : successful convergence
Number of individuals : 293
Number of observations : 2599
Number of cores used : 3
Number of inter-individual draws : 200 (SobolOwenFaureTezuka)
LL(start) : -2855.293
LL(0) : -2855.293
LL(final) : -1935.703
Rho-square (0) : 0.3221
Adj.Rho-square (0) : 0.3151
AIC : 3911.41
BIC : 4028.66
Estimated parameters : 20
Time taken (hh:mm:ss) : 00:11:36.62
pre-estimation : 00:00:29.09
estimation : 00:05:39.16
post-estimation : 00:05:28.37
Iterations : 192
Min abs eigenvalue of Hessian : 0.028502
Some eigenvalues of Hessian are positive, indicating potential problems!
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_B -1.988460 NA NA NA NA
asc_T -628.727387 NA NA NA NA
cost_B -0.647537 NA NA NA NA
torsk_B_mu -1.634535 NA NA NA NA
torsk_B_sig -1.628396 NA NA NA NA
laks_B_mu -1.651847 NA NA NA NA
laks_B_sig -1.570543 NA NA NA NA
bunn_B_mu 0.185837 NA NA NA NA
bunn_B_sig 1.000276 NA NA NA NA
land_B_mu -4.838971 NA NA NA NA
land_B_sig -1.911782 NA NA NA NA
cost_T 0.005645 NA NA NA NA
torsk_T_mu -146.111788 NA NA NA NA
torsk_T_sig -134.243391 NA NA NA NA
laks_T_mu -10.857449 NA NA NA NA
laks_T_sig -0.050540 NA NA NA NA
bunn_T_mu -11.771550 NA NA NA NA
bunn_T_sig -0.783184 NA NA NA NA
land_T_mu -4.654447 NA NA NA NA
land_T_sig -13.262613 NA NA NA NA
I have run the model and got non-NAs for std.errors etc.
However, if I specify the ASCs in preference space, while letting the attributes remain in WTP-space, the estimated coefficients turn out nicely and are comparable. Hence, I wonder why the model behave OK when ASCs are NOT included in WTP-space, whereas it collapse when they are included in WTP-space? I have tried various distributions of the attributes, have tried with log-normally distributed cost parameter, a common cost-parameter, etc. The result is always the same; I have to take the ASCs out of the WTP-formulation of the model and specify them in preference space.
So I wonder if there is a way to include the ASCs in the WTP-space part of the model and still get reasonable estimation results?
Thank you in advance for any response to this request.
Margrethe