[Limdep Nlogit List] ASCs calibration
William Greene
wgreene at stern.nyu.edu
Sat Sep 12 04:32:01 AEST 2020
Train's suggestion would apply if you are using an MNL fit on one sample to
simulate
the data from a different sample. That does not appear to be the case in
your application.
You don't need to calibrate the ASCs when you estimate the model and
simulate the
data used to do the estimation. The ASCs are calibrated by the FOCs of the
ML estimation.
/B. Greene
On Thu, Sep 10, 2020 at 8:45 PM Thao Thai via Limdep <
limdep at mailman.sydney.edu.au> wrote:
> Hi Nlogit users,
>
> Train's textbook (2000) page 75 pointed out that when using the results
> from SP for simulations, ASCs should be calibrated to the actual shares of
> alternatives.
> (1) I followed Train's suggestion to adjust ASCs to new values and run the
> syntax below, the coefficients are different from the coefficients
> obtained from the original SP. I guess it is not the correct way to do ASCs
> calibration?
> |-> sample;all$
> |-> Nlogit
> ;lhs = cho, cset, alti
> ;choices = H, C, P, I, G, N
> ;Alg=BFGS
> ;checkdata
> ;model:
> U(H) = rl_h1 * RL_H1 + rl_h2 * RL_H2
> + fl_ * FL_H
> + cr_h1 * CR_H1 + cr_2 * CR_H2
> + lo_h1 * LO_H1
> + sa_ * SA_H
> /
> U(C) = com[0.960]
> + rl_c1 * RL_C1 + rl_c2 * RL_C2
> + fl_ * FL_C
> + cr_c1 * CR_C1 + cr_2 * CR_C2
> + lo_c1 * LO_C1 + lo_c2 * LO_C2
> + sa_ * SA_C/
> U(P) = pri[-1.225]
> + rl_p1 * RL_P1
> + fl_ * FL_P
> + cr_p1 * CR_P1 + cr_2 * CR_P2
> + lo_p1 * LO_P1 + lo_p2 * LO_P2
> + sa_ * SA_P/
> U(I) = ind[-2.440]
> + rl_i1 * RL_I1 + rl_i2 * RL_I2
> + fl_ * FL_I
> + cr_i1 * CR_I1
> + lo_i1 * LO_I1
> + sa_ * SA_I/
> U(G) = gov[-0.548]
> + rl_g1 * RL_G1
> + fl_ * FL_G
> + cr_g1 * CR_G1
> + lo_g1 * LO_G1
> + sa_ * SA_G/
> U(N) = non[-0.844]
> + rl_n1 * RL_N1
> + fl_ * FL_N
> + cr_n1 * CR_N1
> + lo_n1 * LO_N1 + lo_n2 * LO_N2
> + sa_ * SA_N
> $
> (2) I then used the endogenous weighting to the SP model, the coefficient
> results are different from the original SP model. I guess this way is not
> correct either.
>
> |-> sample;all$
> |-> Nlogit
> ;lhs = cho, cset, alti
> ;choices = H, C, P, I, G, N/0.242,0.5134,0.0388,0.0468,0.0922,0.0668
> ;Alg=BFGS
> ;checkdata
> ;model:
> U(H) = rl_h1 * RL_H1 + rl_h2 * RL_H2
> + fl_ * FL_H
> + cr_h1 * CR_H1 + cr_2 * CR_H2
> + lo_h1 * LO_H1
> + sa_ * SA_H
> /
> U(C) = com
> + rl_c1 * RL_C1 + rl_c2 * RL_C2
> + fl_ * FL_C
> + cr_c1 * CR_C1 + cr_2 * CR_C2
> + lo_c1 * LO_C1 + lo_c2 * LO_C2
> + sa_ * SA_C/
> U(P) = pri
> + rl_p1 * RL_P1
> + fl_ * FL_P
> + cr_p1 * CR_P1 + cr_2 * CR_P2
> + lo_p1 * LO_P1 + lo_p2 * LO_P2
> + sa_ * SA_P/
> U(I) = ind
> + rl_i1 * RL_I1 + rl_i2 * RL_I2
> + fl_ * FL_I
> + cr_i1 * CR_I1
> + lo_i1 * LO_I1
> + sa_ * SA_I/
> U(G) = gov
> + rl_g1 * RL_G1
> + fl_ * FL_G
> + cr_g1 * CR_G1
> + lo_g1 * LO_G1
> + sa_ * SA_G/
> U(N) = non
> + rl_n1 * RL_N1
> + fl_ * FL_N
> + cr_n1 * CR_N1
> + lo_n1 * LO_N1 + lo_n2 * LO_N2
> + sa_ * SA_N
> $
> (3) I then run a model with fixed coefficient estimates but ASCs with
> endogenous weighting. The ASCs estimates are similar to the adjusted ASCs
> in step (1) as per suggestion of Train (2000). However, the base shares are
> not the same as the shares that I specified in the model
> |-> sample;all$
> |-> Nlogit
> ;lhs = cho, cset, alti
> ;choices = H, C, P, I, G, N/0.241,0.5153,0.0386,0.0466,0.0919,0.0666
> ;checkdata
> ;show
> 'Alg=BFGS
> ;calibrate
> ;model:
> U(H) = rl_h1[0.0700481] * RL_H1 + rl_h2[0.231038] * RL_H2
> + fl_[0.178665] * FL_H
> + cr_h1[0.271042] * CR_H1 + cr_2[0.0529584] * CR_H2
> + lo_h1[-0.414659] * LO_H1
> + sa_[0.0123069] * SA_H
> /
> U(C) = com
> + rl_c1[0.387869] * RL_C1 + rl_c2[0.263637] * RL_C2
> + fl_[0.178665] * FL_C
> + cr_c1[0.19661] * CR_C1 + cr_2[0.0529584] * CR_C2
> + lo_c1[-0.222456] * LO_C1 + lo_c2[-0.820609] * LO_C2
> + sa_[0.0123069] * SA_C
> /
> U(P) = pri
> + rl_p1[0.0167174] * RL_P1
> + fl_[0.178665] * FL_P
> + cr_p1[0.333571] * CR_P1 + cr_2[0.0529584] * CR_P2
> + lo_p1[-0.951557] * LO_P1 + lo_p2[-1.03276] * LO_P2
> + sa_[0.0123069] * SA_P
> /
> U(I) = ind
> + rl_i1[0.643119] * RL_I1 + rl_i2[0.790318] * RL_I2
> + fl_[0.178665] * FL_I
> + cr_i1[0.610239] * CR_I1
> + lo_i1[-0.64615] * LO_I1
> + sa_[0.0123069] * SA_I
> /
> U(G) = gov
> + rl_g1[-0.331743] * RL_G1
> + fl_[0.178665] * FL_G
> + cr_g1[0.516844] * CR_G1
> + lo_g1[-0.57783] * LO_G1
> + sa_[0.0123069] * SA_G
> /
> U(N) = non
> + rl_n1[-0.0814267] * RL_N1
> + fl_[0.178665] * FL_N
> + cr_n1[0.36292] * CR_N1
> + lo_n1[-0.585064] * LO_N1 + lo_n2[-0.466387] * LO_N2
> + sa_[0.0123069] * SA_N$
> The base share results below
> +----------+--------------+--------------+------------------+
> |Choice | Base | Scenario | Scenario - Base |
> | |%Share Number |%Share Number |ChgShare ChgNumber|
> +----------+--------------+--------------+------------------+
> |H | 24.823 604 | 22.801 555 | -2.023% -49 |
> |C | 48.452 1179 | 52.560 1279 | 4.108% 100 |
> |P | 4.596 112 | 4.208 102 | -.388% -10 |
> |I | 5.192 126 | 4.804 117 | -.388% -9 |
> |G | 9.971 243 | 9.150 223 | -.820% -20 |
> |N | 6.965 170 | 6.476 158 | -.489% -12 |
> |Total |100.000 2434 |100.000 2434 | .000% 0 |
> +----------+--------------+--------------+------------------+
>
> Question: (1) Could you kindly please let me know how I should calibrate
> ASCs in simulation in Nlogit?
> (2) I'd like to do the simulation using a Mixed logit with
> all ASCs being random parameters. Please let me know if there are any
> special considerations when calibrating ASCs.
>
> Thank you so much. I truly look forward to your help.
> Best regards,
> Thao
>
>
> --
> *Thao Thai*| MHEcon(Adv), BPharm
> PhD candidate
> Centre for Health Economics <https://protect-au.mimecast.com/s/3hXKCp81lrtZoNwAuPShT1?domain=monash.edu>
> Monash Business School | Monash University
> Tel (03) 99029847| Thao.T.Thai at monash.edu <Thao.T.Thai at monash.edu>
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>
--
William Greene
Department of Economics, emeritus
Stern School of Business, New York University
44 West 4 St.
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/oKQYCq71mwfvNP2XUXkZla?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.646.596.3296
Editor in Chief: Journal of Productivity Analysis
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