Bayesian Econometrics Software

4.6 Latent-class linear model with panel data

Mathematical representation

y_{i t | c} = x_{i t}^{'} β_{c} + 𝜀_{i t | c}, 𝜀_{i t | c} \sim N (0, \frac{1}{τ_{c}}), c = 0, 1, \dots, C - 1

(4.7)

the model is estimated using observations from $N$ groups, each group observed for $T_{i}$ periods (balanced or unbalanced panels); the total number of observations is $\sum_{i = 1}^{N} T_{i}$
the model involves $C$ classes (counting starts at zero) and each group, $i$ , is restricted to belong to the same class for all $T_{i}$ observations
$y_{i t}$ is the value of the dependent variable for group $i$ , observed in period $t$
$x_{i t}$ is a $K \times 1$ vector that stores the values of the $K$ independent variables for group $i$ , observed in period $t$
$β_{c}$ is a $K \times 1$ vector of parameters for class $c$
$τ_{c}$ is the precision of the error term for class $c$ : $σ_{𝜀, c}^{2} = \frac{1}{τ_{c}}$
each group, i, belongs to class c with prior probability (before seeing the data yit,xit t=1Ti) πi,c. BayES supports two types of models:
1. unconditional prior class membership probabilities, in which case:
  $π_{i, c} = π_{c} \forall i, c$
  With this speciﬁcation $π \equiv {[\begin{matrix} π_{0} & π_{1} & \dots & π_{C - 1} \end{matrix}]}^{'}$ is a vector of parameters to be estimated, with $π_{c} > 0$ $\forall c$ and $\sum_{c = 0}^{C - 1} π_{c} = 1$ .
2. conditional prior class membership probabilities, in which case:
  
  $π_{i, c} = \frac{e^{z_{i}^{'} δ_{c}}}{\sum_{ℓ = 0}^{C - 1} e^{z_{i}^{'} δ_{ℓ}}} \forall i, c$
  
  where:
  - $z_{i}$ is an $L \times 1$ vector that stores the values of the $L$ determinants of class-membership for group $i$ ; these variables vary by group only (not within group)
  - $δ \equiv {[\begin{matrix} δ_{1}^{'} & δ_{2}^{'} & \dots δ_{C - 1}^{'} \end{matrix}]}^{'}$ is an $L \cdot (C - 1) \times 1$ vector of parameters to be estimated
  In this speciﬁcation class-membership probabilities are determined by a multinomial Logit model, where, for identiﬁcation purposes, $δ_{0}$ is normalized to an $L \times 1$ vector of zeros.

Priors


Parameter	Probability density function	Default hyperparameters

Common to both model types
$β_{c}$	$p (β_{c}) = \frac{\| P_{c} \|^{1 ∕ 2}}{{(2 π)}^{K ∕ 2}} exp \{- \frac{1}{2} {(β_{c} - m_{c})}^{'} P_{c} (β_{c} - m_{c})\}$	$m_{c} = 0_{K}$ , $P_{c} = 0.001 \cdot I_{K}$
$τ_{c}$	$p (τ_{c}) = \frac{b_{τ_{c}}^{a_{τ_{c}}}}{Γ (a_{τ_{c}})} τ_{c}^{a_{τ_{c}} - 1} e^{- τ_{c} b_{τ_{c}}}$	$a_{τ_{c}} = 0.001$ , $b_{τ_{c}} = 0.001$
Model with unconditional class-membership probabilities
$π$	$p (π) = \frac{1}{B (a)} \prod_{c = 0}^{C - 1} π_{c}^{a_{c} - 1}$	$a_{0} = a_{1} = \dots = a_{C - 1} = 1$
Model with conditional class-membership probabilities
$δ$	$p (δ) = \frac{\| P_{δ} \|^{1 ∕ 2}}{{(2 π)}^{\frac{L (C - 1)}{2}}} exp \{- \frac{1}{2} {(δ - m_{δ})}^{'} P_{δ} (δ - m_{δ})\}$	$m_{δ} = 0_{L (C - 1)}$ , $P_{δ} = 0.001 \cdot I_{L (C - 1)}$

Syntax

[

<model name> =

]

lm_lc( y ~ x1 x2 … xK

[

|

z1 z2

\dots

]

[

, <options>

]

);

where:

y is the dependent variable name, as it appears in the dataset used for estimation
x1 x2 $\dots$ xK is a list of the $K$ independent variable names, as they appear in the dataset used for estimation; when a constant term is to be included in the model, this must be requested explicitly
z1 z2 $\dots$ zL is a list of the $L$ variable names that enter the speciﬁcation of the class-membership probabilities (determinants of class membership), as they appear in the dataset used for estimation; when a constant term is to be included in the model, this must be requested explicitly; this list is optional and when provided the conditional latent-class model is estimated; if not provided the unconditional model is estimated; the values of the variables in this list must be constant within each group

If the dataset used for estimation has not been previously declared as a panel dataset or if this structure has been removed (by a call to the set_cs() function) then the model estimated is the one documented in the preceding section. Diﬀerent time observations from the same group in that model are allowed to belong to diﬀerent classes.

The optional arguments for the latent-class linear model with panel data are:⁷

Gibbs parameters

"chains"	number of chains to run in parallel (positive integer); the default value is 1
"burnin"	number of burn-in draws per chain (positive integer); the default value is 10000
"draws"	number of retained draws per chain (positive integer); the default value is 20000
"thin"	value of the thinning parameter (positive integer); the default value is 1
"seed"	value of the seed for the random-number generator (positive integer); the default value is 42
Model specification

"classes"	speciﬁcation of the number of classes to be used in the model (positive integer); the default value is 2
Hyperparameters

Common to both model types
"m"	mean vector of the prior for each $β_{c}$ ( $K \times 1$ vector); the default value is $0_{K}$
"P"	precision matrix of the prior for each $β_{c}$ ( $K \times K$ symmetric and positive-deﬁnite matrix); the default value is $0.001 \cdot I_{K}$
"mj"	mean vector of the prior for $β_{j}$ , $j = 0, 1, \dots, C - 1$ ( $K \times 1$ vector); this mean overwrites the generic mean ("m") for class $j$ only
"Pj"	precision matrix of the prior for $β_{j}$ , $j = 0, 1, \dots, C - 1$ ( $K \times K$ symmetric and positive-deﬁnite matrix); this precision matrix overwrites the generic precision matrix ("P") for class $j$ only
"a_tau"	shape parameter of the prior for each $τ_{c}$ (positive number); the default value is $0.001$
"b_tau"	rate parameter of the prior for each $τ_{c}$ (positive number); the default value is $0.001$
"a_tauj"	shape parameter of the prior for $τ_{j}$ , $j = 0, 1, \dots, C - 1$ (positive number); this shape parameter overwrites the generic shape parameter ("a_tau") for class $j$ only
"b_tauj"	rate parameter of the prior for $τ_{j}$ , $j = 0, 1, \dots, C - 1$ (positive number); this rate parameter overwrites the generic rate parameter ("b_tau") for class $j$ only
Model with unconditional class-membership probabilities
"a"	vector of concentration parameters for the Dirichlet prior on $π$ ( $C \times 1$ vector with positive entries); the default value is a $C \times 1$ vector of ones
Model with conditional class-membership probabilities
"m_delta"	mean vector of the prior for $δ$ ( $L (C - 1) \times 1$ vector); the default value is $0_{L (C - 1)}$
"P_delta"	precision matrix of the prior for $δ$ ( $L (C - 1) \times 1$ symmetric and positive-deﬁnite matrix); the default value is $0.001 \cdot I_{L (C - 1)}$
Dataset and log-marginal likelihood

"dataset"	the id value of the dataset that will be used for estimation; the default value is the ﬁrst dataset in memory (in alphabetical order)
"logML_CJ"	boolean indicating whether the Chib (1995)/Chib & Jeliazkov (2001) approximation to the log-marginal likelihood should be calculated (true $\|$ false); the default value is false

Reported Parameters


Common to both model types

$β_{c}$	variable_name	vector of parameters associated with the independent variables for class $c$

$τ_{c}$	tau	precision parameter of the error term for class $c$ , $𝜀_{i t \| c}$

$σ_{𝜀, c}$	sigma_e	standard deviation of the error term for class $c$ : $σ_{𝜀, c} = 1 ∕ τ_{c}^{1 ∕ 2}$

Model with unconditional class-membership probabilities

$π_{c}$	pi	prior class-membership probability for class $c$

Model with conditional class-membership probabilities

$δ_{c}$	variable_name	vector of parameters associated with the determinants of class membership for class $c$ ; for identiﬁcation purposes, these parameters for class 0 are normalized to zero

Stored values and post-estimation analysis
If a left-hand-side id value is provided when a latent-class linear model with panel data is created, then the following results are saved in the model item and are accessible via the ‘.’ operator:

Samples	a matrix containing the draws from the posterior of $β_{c}$ and $τ_{c}$ for $c = 0, 1, \dots, C - 1$ , and, depending on the estimated model, $π$ or $δ$ .
cj$x1, $\dots$ ,cj$xK	vectors containing the draws from the posterior of the parameters associated with variables x1, $\dots$ ,xK, for $j = 0, 1, \dots, C - 1$ (the names of these vectors are the names of the variables that were included in the right-hand side of the model, prepended by ‘c’, the class index and a dollar sign; in this way ‘cj’ can be used to distinguish among parameters across diﬀerent classes)
cj$tau	vectors containing the draws from the posterior of each $τ_{c}$ , for $j = 0, 1, \dots, C - 1$ (‘tau’ is prepended by ‘c’, the class index and the dollar sign; in this way ‘cj’ can be used to distinguish among precision parameters in diﬀerent classes)
pi_j	vectors containing the draws from the posterior of each $π_{c}$ , for $j = 0, 1, \dots, C - 1$ (these vectors are available only after the estimation of the model with unconditional class-membership probabilities)
pi_j$z1, $\dots$ , pi_j$zL	vectors containing the draws from the posterior of the parameters associated with variables z1, $\dots$ ,zL, for $j = 1, \dots, C - 1$ ⁸ (the names of these vectors are the names of the variables that were included the z list, in the right-hand side of the model, prepended by ‘pi_j’ and the dollar sign; in this way ‘pi_j’ can be used to distinguish among parameters associated with variables with diﬀerent roles in the model, for example the same variable appearing in both x and z lists, as well as among parameters associated with a variable in the z list, but corresponding to diﬀerent classes; these vectors are available only after the estimation of the model with conditional class-membership probabilities)
logML	the Lewis & Raftery (1997) approximation of the log-marginal likelihood
logML_CJ	the Chib (1995)/Chib & Jeliazkov (2001) approximation to the log-marginal likelihood; this is available only if the model was estimated with the "logML_CJ"=true option
pi_i	$N \times C$ matrix that stores the expected values of the posterior class-membership probabilities for each group and for each of the $C$ classes
nchains	the number of chains that were used to estimate the model
nburnin	the number of burn-in draws per chain that were used when estimating the model
ndraws	the total number of retained draws from the posterior ( $=$ chains $\cdot$ draws)
nthin	value of the thinning parameter that was used when estimating the model
nseed	value of the seed for the random-number generator that was used when estimating the model
nclasses	number of classes used during the estimation of the model

Additionally, the following functions are available for post-estimation analysis (see section B.14):

diagnostics()
test()
pmp()
store()
mfx()

The latent-class linear model with panel data uses the store() function to associate the estimates of the posterior class-membership probabilities (pi_i) with speciﬁc observations and store their values in the dataset used for estimation. The generic syntax for a statement involving the store() function after estimation of a latent-class linear model with panel data is:

store( pi_i, <new variable name prefix>

[

, "model"=<model name>

]

);

This statement will generate $C$ additional variables in the dataset used for estimation of the model, with names constructed by appending the class index ( $0, 1, \dots, C - 1$ ) to the preﬁx provided as the second argument to store().

The latent-class linear model with panel data and conditional class-membership probabilities uses the mfx() function to calculate and report the marginal eﬀects of the variables in the z list on the prior class-membership probabilities that come from the multinomial-Logit part of the model. The generic syntax for a statement involving the mfx() function after estimation of a latent-class linear model with panel data and conditional class-membership probabilities is:

mfx(

[

"type"=1

]

[

, "point"=<point of calculation>

]

[

, "model"=<model name>

]

);

See the general documentation of the mfx() function (section B.14) for details on the optional arguments.

Examples

Example 1

myData = import("$BayESHOME/Datasets/dataset2.csv");
myData.constant = 1;

set_pd( year, id, "dataset" = myData);
lm_lc(y ~ constant x1 x2);

Example 2

myData = import("$BayESHOME/Datasets/dataset2.csv");
myData.constant = 1;

set_pd( year, id, "dataset" = myData);
myModel = lm_lc(y ~ constant x1 x2,
    "m0"=[2;0.6;0.3], "P" = 10*eye(3,3),
    "burnin"=10000, "draws"=30000, "thin"=2, "chains"=2, "classes"=2,
    "logML_CJ" = true, "dataset"=myData);

diagnostics("model"=myModel);

plot([myModel.c0$x1, myModel.c1$x1],
    "title"="∖beta2 for the two classes");
plot([myModel.pi_0, myModel.pi_1],
    "title"="Prior class

-

membership probabilities");

⁷Optional arguments are always given in option-value pairs (eg. "chains"=3).

⁸Indexing starts at $1$ because the parameters of the multinomial-Logit part of the model associated with class $0$ are normalized to zero for identiﬁcation purposes.

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