1. Microeconometric Modeling
William Greene
Stern School of Business
New York University
New York NY USA
4.3 Mixed Models and Random Parameters

2. Concepts Models Random Effects Simulation Random Parameters
Maximum Simulated Likelihood
Cholesky Decomposition
Heterogeneity
Hierarchical Model
Conditional Means
Population Distribution
Nested Logit
Willingness to Pay (WTP)
Random Parameters and WTP
WTP Space
Endogeneity
Market Share Data
Random Parameters
RP Logit
Error Components Logit
Generalized Mixed Logit
Berry-Levinsohn-Pakes Model
Hybrid Choice
MIMIC Model

3. A Recast Random Effects Model

4. A Computable Log Likelihood

5. Simulation

6. Random Effects Model: Simulation
Random Coefficients Probit Model
Dependent variable DOCTOR (Quadrature Based)
Log likelihood function ( )
Restricted log likelihood
Chi squared [ 1 d.f.]
Simulation based on 50 Halton draws
Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]
|Nonrandom parameters
AGE| *** ( )
EDUC| *** ( )
HHNINC| ( )
|Means for random parameters
Constant| ** ( )
|Scale parameters for dists. of random parameters
Constant| ***
Implied  from these estimates is /( ) =

7. The Entire Parameter Vector is Random

8. Maximum Simulated Likelihood
True log likelihood
Simulated log likelihood

10. S M

11. MSSM

12. Modeling Parameter Heterogeneity

13. A Hierarchical Probit Model
Uit = 1i + 2iAgeit + 3iEducit + 4iIncomeit + it.
1i=1+11 Femalei + 12 Marriedi + u1i
2i=2+21 Femalei + 22 Marriedi + u2i
3i=3+31 Femalei + 32 Marriedi + u3i
4i=4+41 Femalei + 42 Marriedi + u4i
Yit = 1[Uit > 0]
All random variables normally distributed.

15. Estimating Individual Parameters
Model estimates = structural parameters, α, β, ρ, Δ, Σ, Γ
Objective, a model of individual specific parameters, βi
Can individual specific parameters be estimated?
Not quite – βi is a single realization of a random process; one random draw.
We estimate E[βi | all information about i]
(This is also true of Bayesian treatments, despite claims to the contrary.)

16. Estimating i

17. Conditional Estimate of i

18. Conditional Estimate of i

22. “Individual Coefficients”

23. The Random Parameters Logit Model
Multiple choice situations: Independent conditioned on the individual specific parameters

24. Continuous Random Variation in Preference Weights

25. Random Parameters Model
Allow model parameters as well as constants to be random
Allow multiple observations with persistent effects
Allow a hierarchical structure for parameters – not completely random
Uitj = 1’xi1tj + 2i’xi2tj + i’zit + ijt
Random parameters in multinomial logit model
1 = nonrandom (fixed) parameters
2i = random parameters that may vary across individuals and across time
Maintain I.I.D. assumption for ijt (given )

26. Modeling Variations Parameter specification Stochastic specification
“Nonrandom” – variance = 0
Correlation across parameters – random parts correlated
Fixed mean – not to be estimated. Free variance
Fixed range – mean estimated, triangular from 0 to 2
Hierarchical structure - ik = k + k’zi
Stochastic specification
Normal, uniform, triangular (tent) distributions
Strictly positive – lognormal parameters (e.g., on income)
Autoregressive: v(i,t,k) = u(i,t,k) + r(k)v(i,t-1,k) [this picks up time effects in multiple choice situations, e.g., fatigue.]

27. Model Extensions AR(1): wi,k,t = ρkwi,k,t-1 + vi,k,t
Dynamic effects in the model
Restricting sign – lognormal distribution:
Restricting Range and Sign: Using triangular distribution and range = 0 to 2.
Heteroscedasticity and heterogeneity

28. Estimating the Model Denote by 1 all “fixed” parameters in the model
Denote by 2i,t all random and hierarchical parameters in the model

29. Customers’ Choice of Energy Supplier
California, Stated Preference Survey
361 customers presented with 8-12 choice situations each
Supplier attributes:
Fixed price: cents per kWh
Length of contract
Local utility
Well-known company
Time-of-day rates (11¢ in day, 5¢ at night)
Seasonal rates (10¢ in summer, 8¢ in winter, 6¢ in spring/fall)
(TrainCalUtilitySurvey.lpj)

30. Population Distributions
Normal for:
Contract length
Local utility
Well-known company
Log-normal for:
Time-of-day rates
Seasonal rates
Price coefficient held fixed

31. Estimated Model Estimate Std error Price -.883 0.050
Contract mean
std dev
Local mean
std dev
Known mean
std dev
TOD mean*
std dev*
Seasonal mean*
std dev*
*Parameters of underlying normal.

32. Distribution of Brand Value
Standard deviation
=2.0¢
10% dislike local utility
2.5¢
Brand value of local utility

33. Random Parameter Distributions

34. Time of Day Rates (Customers do not like – lognormal coefficient
Time of Day Rates (Customers do not like – lognormal coefficient. Multiply variable by -1.)

35. Posterior Estimation of i
Estimate by simulation

36. Expected Preferences of Each Customer
Customer likes long-term contract, local utility, and non-fixed rates.
Local utility can retain and make profit from this customer by offering a long-term contract with time-of-day or seasonal rates.

37. Application: Shoe Brand Choice
Simulated Data: Stated Choice,
400 respondents,
8 choice situations, 3,200 observations
3 choice/attributes + NONE
Fashion = High / Low
Quality = High / Low
Price = 25/50/75,100 coded 1,2,3,4
Heterogeneity: Sex (Male=1), Age (<25, 25-39, 40+)
Underlying data generated by a 3 class latent class process (100, 200, 100 in classes)

38. Stated Choice Experiment: Unlabeled Alternatives, One Observation

39. Random Parameters Logit Model

42. Error Components Logit Modeling
Alternative approach to building cross choice correlation
Common ‘effects.’ Wi is a ‘random individual effect.’

43. Implied Covariance Matrix Nested Logit Formulation

44. Error Components Logit Model
Error Components (Random Effects) model
Dependent variable CHOICE
Log likelihood function
Estimation based on N = , K = 5
Response data are given as ind. choices
Replications for simulated probs. = 50
Halton sequences used for simulations
ECM model with panel has groups
Fixed number of obsrvs./group=
Number of obs.= 3200, skipped 0 obs
Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]
|Nonrandom parameters in utility functions
FASH| ***
QUAL| ***
PRICE| ***
ASC4|
SigmaE01| ***
Random Effects Logit Model
Appearance of Latent Random
Effects in Utilities
Alternative E01
| BRAND1 | * |
| BRAND2 | * |
| BRAND3 | * |
| NONE | |
Correlation = { / [ ]}1/2 =

45. Extended MNL Model
Utility Functions

46. Extending the Basic MNL Model
Random Utility

47. Error Components Logit Model

48. Random Parameters Model

49. Heterogeneous (in the Means) Random Parameters Model
Heterogeneity in Means

50. Heterogeneity in Both Means and Variances
Heteroscedasticity

51. ---------------------------------------------------------------
Random Parms/Error Comps. Logit Model
Dependent variable CHOICE
Log likelihood function ( for MNL)
Restricted log likelihood (Chi squared = 278.5)
Chi squared [ 12 d.f.]
Significance level
McFadden Pseudo R-squared
Estimation based on N = , K = 12
Information Criteria: Normalization=1/N
Normalized Unnormalized
AIC
R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj
No coefficients
Constants only
At start values
Response data are given as ind. choices
Replications for simulated probs. = 50
Halton sequences used for simulations
RPL model with panel has groups
Fixed number of obsrvs./group=
Number of obs.= 3200, skipped 0 obs

52. Estimated RP/ECL Model
Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]
|Random parameters in utility functions
FASH| ***
PRICE| ***
|Nonrandom parameters in utility functions
QUAL| ***
ASC4|
|Heterogeneity in mean, Parameter:Variable
FASH:AGE| ***
FAS0:AGE| ***
PRIC:AGE| ***
PRI0:AGE| ***
|Distns. of RPs. Std.Devs or limits of triangular
NsFASH| ***
NsPRICE|
|Heterogeneity in standard deviations
|(cF1, cF2, cP1, cP2 omitted...)
|Standard deviations of latent random effects
SigmaE01|
SigmaE02| **
Note: ***, **, * = Significance at 1%, 5%, 10% level.
Random Effects Logit
Model Appearance of
Latent Random Effects
in Utilities
Alternative E01 E02
| BRAND1 | * | |
| BRAND2 | * | |
| BRAND3 | * | |
| NONE | | * |
Heterogeneity in Means.
Delta: 2 rows, 2 cols.
AGE AGE39
FASH
PRICE

53. Estimated Elasticities
Multinomial Logit
| Elasticity averaged over observations.|
| Attribute is PRICE in choice BRAND |
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute. |
| Mean St.Dev |
| * Choice=BRAND |
| Choice=BRAND |
| Choice=BRAND |
| Choice=NONE |
| Attribute is PRICE in choice BRAND |
| Choice=BRAND |
| * Choice=BRAND |
| Choice=BRAND |
| Choice=NONE |
| Attribute is PRICE in choice BRAND |
| Choice=BRAND |
| Choice=BRAND |
| * Choice=BRAND |
| Choice=NONE |
| Effects on probabilities |
| * = Direct effect te. |
| Mean St.Dev |
| PRICE in choice BRAND1 |
| * BRAND |
| BRAND |
| BRAND |
| NONE |
| PRICE in choice BRAND2 |
| BRAND |
| * BRAND |
| BRAND |
| NONE |
| PRICE in choice BRAND3 |
| BRAND |
| BRAND |
| * BRAND |
| NONE |

54. Estimating Individual Distributions
Form posterior estimates of E[i|datai]
Use the same methodology to estimate E[i2|datai] and Var[i|datai]
Plot individual “confidence intervals” (assuming near normality)
Sample from the distribution and plot kernel density estimates

55. What is the ‘Individual Estimate?’
Point estimate of mean, variance and range of random variable i | datai.
Value is NOT an estimate of i ; it is an estimate of E[i | datai]
This would be the best estimate of the actual realization i|datai
An interval estimate would account for the sampling ‘variation’ in the estimator of Ω.
Bayesian counterpart to the preceding: Posterior mean and variance. Same kind of plot could be done.

56. Individual E[i|datai] Estimates*
The random parameters model is uncovering the latent class feature of the data.
*The intervals could be made wider to account for the sampling variability of the underlying (classical) parameter estimators.

57. WTP Application (Value of Time Saved)
Estimating Willingness to Pay for Increments to an Attribute in a Discrete Choice Model WTP = MU(attribute) / MU(Income)
Random

58. Extending the RP Model to WTP
Use the model to estimate conditional distributions for any function of parameters
Willingness to pay = -i,time / i,cost
Use simulation method

59. Simulation of WTP from i

61. WTP

62. A Generalized Mixed Logit Model

63. Generalized Multinomial Choice Model

64. Estimation in Willingness to Pay Space
Both parameters in the WTP calculation are random.

65. Estimated Model for WTP
Variable| Coefficient Standard Error b/St.Er. P[|Z|>z]
|Random parameters in utility functions
QUAL| *** renormalized
PRICE| (Fixed Parameter) renormalized
|Nonrandom parameters in utility functions
FASH| *** not rescaled
ASC4| *** not rescaled
|Heterogeneity in mean, Parameter:Variable
QUAL:AGE| interaction terms
QUA0:AGE|
PRIC:AGE|
PRI0:AGE|
|Diagonal values in Cholesky matrix, L.
NsQUAL| *** correlated parameters
CsPRICE| (Fixed Parameter) but coefficient is fixed
|Below diagonal values in L matrix. V = L*Lt
PRIC:QUA| (Fixed Parameter)......
|Heteroscedasticity in GMX scale factor
sdMALE| heteroscedasticity
|Variance parameter tau in GMX scale parameter
TauScale| *** overall scaling, tau
|Weighting parameter gamma in GMX model
GammaMXL| (Fixed Parameter)......
|Coefficient on PRICE in WTP space form
Beta0WTP| *** new price coefficient
S_b0_WTP| standard deviation
| Sample Mean Sample Std.Dev.
Sigma(i)| overall scaling
|Standard deviations of parameter distributions
sdQUAL| ***
sdPRICE| (Fixed Parameter)......
Estimated Model for WTP

75. Aggregate Data and Multinomial Choice: The Model of Berry, Levinsohn and Pakes

76. Resources
Automobile Prices in Market Equilibrium, S. Berry, J. Levinsohn, A. Pakes, Econometrica, 63, 4, 1995, (BLP)
A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models of Demand, A. Nevo, Journal of Economics and Management Strategy, 9, 4, 2000,
A New Computational Algorithm for Random Coefficients Model with Aggregate-level Data, Jinyoung Lee, UCLA Economics, Dissertation, 2011
Elasticities of Market Shares and Social Health Insurance Choice in Germany: A Dynamic Panel Data Approach, M. Tamm et al., Health Economics, 16, 2007,

77. Theoretical Foundation
Consumer market for J differentiated brands of a good
j =1,…, Jt brands or types
i = 1,…, N consumers
t = i,…,T “markets” (like panel data)
Consumer i’s utility for brand j (in market t) depends on
p = price
x = observable attributes
f = unobserved attributes
w = unobserved heterogeneity across consumers
ε = idiosyncratic aspects of consumer preferences
Observed data consist of aggregate choices, prices and features of the brands.

78. BLP Automobile Market Jt t

79. Random Utility Model
Utility: Uijt=U(wi,pjt,xjt,fjt|), i = 1,…,(large)N, j=1,…,J
wi = individual heterogeneity; time (market) invariant. w has a continuous distribution across the population.
pjt, xjt, fjt, = price, observed attributes, unobserved features of brand j; all may vary through time (across markets)
Revealed Preference: Choice j provides maximum utility
Across the population, given market t, set of prices pt and features (Xt,ft), there is a set of values of wi that induces choice j, for each j=1,…,Jt; then, sj(pt,Xt,ft|) is the market share of brand j in market t.
There is an outside good that attracts a nonnegligible market share, j=0. Therefore,

80. Functional Form
(Assume one market for now so drop “’t.”) Uij=U(wi,pj,xj,fj|)= xj'β – αpj + fj + εij = δj + εij
Econsumers i[εij] = 0, δj is E[Utility].
Will assume logit form to make integration unnecessary. The expectation has a closed form.

81. Heterogeneity
Assumptions so far imply IIA. Cross price elasticities depend only on market shares.
Individual heterogeneity: Random parameters
Uij=U(wi,pj,xj,fj|i)= xj'βi – αpj + fj + εij
βik = βk + σkvik.
The mixed model only imposes IIA for a particular consumer, but not for the market as a whole.

82. Endogenous Prices: Demand side
Uij=U(wi,pj,xj,fj|)= xj'βi – αpj + fj + εij
fj is unobserved
Utility responds to the unobserved fj
Price pj is partly determined by features fj.
In a choice model based on observables, price is correlated with the unobservables that determine the observed choices.

83. Endogenous Price: Supply Side
There are a small number of competitors in this market
Price is determined by firms that maximize profits given the features of its products and its competitors.
mcj = g(observed cost characteristics c, unobserved cost characteristics h)
At equilibrium, for a profit maximizing firm that produces one product, sj + (pj-mcj)sj/pj = 0
Market share depends on unobserved cost characteristics as well as unobserved demand characteristics, and price is correlated with both.

84. Instrumental Variables (ξ and ω are our h and f.)

85. Econometrics: Essential Components

86. Econometrics

87. GMM Estimation Strategy - 1

88. GMM Estimation Strategy - 2

89. BLP Iteration

90. ABLP Iteration our ft.  is our (β,)
No superscript is our (M); superscript 0 is our (M-1).

91. Side Results

92. ABLP Iterative Estimator

93. BLP Design Data

94. Exogenous price and nonrandom parameters

95. IV Estimation

96. Full Model

97. Some Elasticities

107. Latent Class Mixed Logit

111. Appendix: Maximum Simulated Likelihood

112. Monte Carlo Integration

113. Monte Carlo Integration

114. Example: Monte Carlo Integral

115. Simulated Log Likelihood for a Mixed Probit Model

116. Generating Random Draws

117. Drawing Uniform Random Numbers

118. Quasi-Monte Carlo Integration Based on Halton Sequences
For example, using base p=5, the integer r=37 has b0 = 2, b1 = 2, and b2 = 1; (37=1x52 + 2x51 + 2x50). Then
H(37|5) = 2  5-3 =

119. Halton Sequences vs. Random Draws
Requires far fewer draws – for one dimension, about 1/10. Accelerates estimation by a factor of 5 to 10.

120. Estimating the RPL Model
Estimation: 1 2it = 2 + Δzi + Γvi,t Uncorrelated: Γ is diagonal Autocorrelated: vi,t = Rvi,t-1 + ui,t (1) Estimate “structural parameters” (2) Estimate individual specific utility parameters (3) Estimate elasticities, etc.

121. Classical Estimation Platform: The Likelihood
Expected value over all possible realizations of i. I.e., over all possible samples.

123. Simulation Based Estimation
Probability = P[data |(1,2,Δ,Γ,R,vi,t)]
Need to integrate out the unobserved random term
E{P[data | (1,2,Δ,Γ,R,vi,t)]}
= P[…|vi,t]f(vi,t)dvi,t
Integration is done by simulation
Draw values of v and compute  then probabilities
Average many draws
Maximize the sum of the logs of the averages
(See Train[Cambridge, 2003] on simulation methods.)

124. Programs differ on the models fitted, the algorithms, the paradigm, and the extensions provided to the simplest RPM, i = +wi.
WinBUGS:
MCMC
User specifies the model – constructs the Gibbs Sampler/Metropolis Hastings
MLWin:
Linear and some nonlinear – logit, Poisson, etc.
Uses MCMC for MLE (noninformative priors)
SAS: Proc Mixed.
Classical
Uses primarily a kind of GLS/GMM (method of moments algorithm for loglinear models)
Stata: Classical
Several loglinear models – GLAMM. Mixing done by quadrature.
Maximum simulated likelihood for multinomial choice (Arne Hole, user provided)
LIMDEP/NLOGIT
Mixing done by Monte Carlo integration – maximum simulated likelihood
Numerous linear, nonlinear, loglinear models
Ken Train’s Gauss Code, miscellaneous freelance R and Matlab code
Monte Carlo integration
Mixed Logit (mixed multinomial logit) model only (but free!)
Biogeme
Multinomial choice models
Many experimental models (developer’s hobby)