---
title: "BLPestimatoR - Package for Demand Estimation"
author: "Daniel Brunner"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{BLPestimatoR - Package for Demand Estimation}
%\VignetteEngine{knitr::knitr}
%\VignetteEncoding{UTF-8}
references:
- id: BLP1995
title: Automobile Prices in Market Equilibrium
author:
- family: Berry
given: Steven
- family: Levinsohn
given: James
- family: Pakes
given: Ariel
container-title: Econometrica
type: article-journal
issued:
year: 1995
- id: Brunner2017
title: Reliable Estimation of Random Coefficient Logit Demand Models
author:
- family: Brunner
given: Daniel
- family: Heiss
given: Florian
- family: Romahn
given: Andre
- family: Weiser
given: Constantin
container-title: DICE Discussion Paper No 267
type: article-journal
issued:
year: 2017
- id: KM2014
title: 'Estimation of Random-Coefficient Demand Models: Two Empiricists Perspective'
author:
- family: Knittel
given: Christopher
- family: Metaxoglou
given: Konstantinos
container-title: The Review of Economics and Statistics
type: article-journal
issued:
year: 2014
- id: Nevo2001
title: A Practitioner's Guide to Estimation of Random-Coefficients Logit Models of Demand
author:
- family: Nevo
given: Aviv
container-title: Journal of Economics \& Management Strategy
type: article-journal
issued:
year: 2001
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# Intro
`BLPestimatoR` provides an efficient estimation algorithm to perform the demand estimation described in @BLP1995. The routine uses analytic gradients and offers a large number of optimization routines and implemented integration methods as discussed in @Brunner2017.
This extended documentation demonstrates the steps of a typical demand estimation with the package:
- prepare the data with `BLP_data` (includes the specification of a model and providing integration draws for observed or unobserved heterogeneity)
- estimate the parameters with `estimate_BLP`
- showing the results with `summary`
- view the own- and crosspriceelasticities with `get_elasticities`
- perform a hypothetical merger analysis
For this purpose the well-known training datasets for the cereal market [@Nevo2001] and the car market [@BLP1995] are included in the package. Loading the package is therefore the very first step of the demand estimation:
```{r}
library(BLPestimatoR)
```
# Data
## Model
Since version 0.1.6 the model is provided in R’s formula syntax and consists of five parts. The variable
to be explained is given by observed market shares. Explanatory variables are grouped into four (possibly
overlapping) categories separated by `|`:
- linear variables
- exogenous variables
- random coefficients
- instruments
The first part of this documentation starts with the cereal data example from @Nevo2001. Nevo's model can be translated into the following formula syntax:
```{r}
nevos_model <- as.formula("share ~ price + productdummy |
0+ productdummy |
price + sugar + mushy |
0+ IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10 +
IV11 + IV12 + IV13 + IV14 + IV15 + IV16 + IV17 + IV18 + IV19 + IV20")
```
The model is directly related to consumer $i$'s indirect utility from purchasing cereal $j$ in market $t$:
$$u_{ijt}=\sum_{m=1}^M x^{(m)}_{jt} \beta_{i,m}+\xi_{jt}+\epsilon_{ijt} \;\; \text{with}$$
$$\beta_{i,m}= \bar{\beta}_m + \sum_{r=1}^R \gamma_{m,r} d_{i,r} + \sigma_m \nu_{i,m}$$
and
- M = 4 random coefficients (`price`, `sugar`, `mushy` and an intercept)
- R = 4 demographics (`income`, `incomesq`, `age`, `child`)
- and the set of non-linear parameters to estimate:
$$\theta_2 =
\begin{pmatrix}
\sigma_1 & \gamma_{1,1} & \cdots & \gamma_{1,R} \\
\sigma_2 & \gamma_{2,1} & \cdots & \gamma_{2,R} \\
\vdots & \vdots & \ddots & \vdots \\
\sigma_M & \gamma_{M,1} & \cdots & \gamma_{M,R}
\end{pmatrix}$$
## Dataframe
Product related variables are collected in the dataframe `productData` with the following requirements:
- missings are not allowed
- character variables are automatically transformed to a set of dummy variables
- a variable that describes market affiliation (`market_identifier`)
A variable that uniquely identifies a product in a market (`product_identifier`) is optional, but enhances clarity (interpreting elasticities, for example, is much easier). `market_identifier` and `product_identifier` together uniquely identify an observation, which is used by the function `update_BLP_data` to update any variable in the data (in this case `product_identifier` is mandatory).
In the cereal example, this gives the following dataframe:
```{r}
head(productData_cereal)
```
## Integration Draws
The arguments related to the numerical integration problem are of particular importance when providing
own integration draws and weights, which is most relevant for observed heterogeneity
(for unobserved heterogeneity, the straightforward approach is the use of automatic integration).
In the cereal data, both, observed and unobserved heterogeneity, is used for the random coefficients.
Starting with observed heterogeneity, user provided draws are collected in a list. Each list entry must be
named according to the name of a demographic. Each entry contains the following variables:
- a variable `market_identifier` that matches each line to a market (same variable name as in `productData`)
- integration draws for each market
In the cereal example, observed heterogeneity is provided as follows (list names correspond to the demographics):
```{r}
demographicData_cereal$income[1:4, 1:5]
demographicData_cereal$incomesq[1:4, 1:5]
demographicData_cereal$age[1:4, 1:5]
demographicData_cereal$child[1:4, 1:5]
```
If demographic input (`demographicData`) is missing, the estimation routine considers only coefficients for unobserved heterogeneity. This can be done by already implemented integration methods via `integration_method` as shown in the estimation section. In Nevo's cereal example however, a specific set of 20 draws is
given. For this situation, draws are also provided as a list (list names correspond to the formula's random coefficients and each list entry has a variable `market_identifier`):
```{r}
originalDraws_cereal$constant[1:4, 1:5]
# renaming constants:
names(originalDraws_cereal)[1] <- "(Intercept)"
originalDraws_cereal$price[1:4, 1:5]
originalDraws_cereal$sugar[1:4, 1:5]
originalDraws_cereal$mushy[1:4, 1:5]
```
As demonstrated above, list entries for draws of constants **must** be named `(Intercept)`. Other names of list entries must match the random coefficients specified in the formula.
## Calling BLP_data
Calling `BLP_data` structures and prepares the data for estimation and creates the data object:
```{r }
productData_cereal$startingGuessesDelta <- c(log(w_guesses_cereal)) # include orig. draws in the product data
cereal_data <- BLP_data(
model = nevos_model,
market_identifier = "cdid",
par_delta = "startingGuessesDelta",
product_identifier = "product_id",
productData = productData_cereal,
demographic_draws = demographicData_cereal,
blp_inner_tol = 1e-6, blp_inner_maxit = 5000,
integration_draws = originalDraws_cereal,
integration_weights = rep(1 / 20, 20)
)
```
The arguments in greater detail:
- `model` provides the utility model as explained above
- `market_identifier` gives the name of the variable in `productData` that matches
each observation to a market
- `product_identifier` gives the name of the variable in `productData` that matches
each observation to a product (must be unique in a market)
- `productData` is given as a dataframe and `demographicData` as a list as described above
- `par_delta` gives the name of the variable in `productData` for mean utilities
- `blp_inner_tol` , `blp_inner_maxit`: arguments related to be BLP algorithm include the convergence threshold and the maximum number of iterations in the contraction mapping
- if integration draws are provided manually, `integration_draws` and
`integration_weights` need to be specified
- for automatic integration the user specifies `integration_method`, for example
`integration_method= "MLHS"`, and the accuracy of the integration method by
`integration_accuracy` (for stochastic integration methods this equals the number
of draws)
If you decide to update your data later, you can use the function `update_BLP_data`.
# Estimation
## Starting guesses
The provided set of starting guesses `par_theta2` is matched with formula input and demographic data:
- rownames of `par_theta2` must match with the random coefficients specified in the formula (note: constants **must** be named `(Intercept)` )
- colnames of `par_theta2` must match with list entry names of `demographicData` and a column for unobserved heterogeneity (**must** be named `unobs_sd)
- `NA`s in `par_theta2` indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero.
These requirements are demonstrated with a set of exemplary starting guesses:
```{r}
# before:
theta_guesses_cereal
theta_guesses_cereal[theta_guesses_cereal == 0] <- NA
colnames(theta_guesses_cereal) <- c("unobs_sd", "income", "incomesq", "age", "child")
rownames(theta_guesses_cereal) <- c("(Intercept)", "price", "sugar", "mushy")
# correctly named:
theta_guesses_cereal
```
## Calling estimateBLP
The following code performs the demand estimation:
```{r}
cereal_est <- estimateBLP(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
solver_method = "BFGS", solver_maxit = 1000, solver_reltol = 1e-6,
standardError = "heteroskedastic",
extremumCheck = FALSE,
printLevel = 1
)
summary(cereal_est)
```
The arguments in greater detail:
- `par_theta2` gives initial values for non-linear parameters to be optimized
over. Correct naming of columns and rows is important to allow correct matching.
- `solver_method`, `solver_maxit` , `solver_reltol`: solver related arguments that specify the R internal optimization (`optim` function). Additional arguments can be passed to optim via `...`
- `standardError` can be specified as `homoskedastic`, `heteroskedastic` or
`cluster`. The latter requires the variable `group_structure` in `productData` giving the related
cluster.
- if `extremumCheck` is `TRUE`, numerical derivatives at the solver optimum are used
to check, if a local minimum was found
- `printLevel` controls for the amount of information that is provided during the
estimation
Many of these arguments have default values. In the following setting you see a
minimum of necessary arguments with an automatic generation of integration
draws and just unobserved heterogeneity. The summary output informs you about the most important default values.
```{r}
cereal_data2 <- BLP_data(
model = nevos_model,
market_identifier = "cdid",
product_identifier = "product_id",
productData = productData_cereal,
integration_method = "MLHS",
integration_accuracy = 20, integration_seed = 213
)
cereal_est2 <- estimateBLP(blp_data = cereal_data2, printLevel = 1)
summary(cereal_est2)
```
# Postestimation
## Standard Errors
Standard errors can be computed with three options that control for the unobserved characteristic $\xi$, which consists of $N$ elements. $\Omega$ denotes the variance covariance matrix of $\xi$.
- option `homoskedastic` requires the standard deviation $\sigma_i$ for each $\xi_i \;\forall
i\in 1,\cdots,N$ to be identical:
$$\Omega = \begin{pmatrix} \sigma & 0 &
\dots & 0\\ 0 & \sigma & & 0\\ \vdots & & \ddots & 0\\
0 & 0 & 0 & \sigma \\ \end{pmatrix}$$
- option `heteroskedastic` allows for individual standard deviations $\sigma_i$ for each
$\xi_i$ :
$$\Omega = \begin{pmatrix} \sigma_1 & 0 &
\dots & 0\\
0 & \sigma_2 & & 0\\
\vdots & & \ddots & 0\\
0 & 0 & 0 & \sigma_N \\ \end{pmatrix}$$
- option `cluster` allows for cluster individual variance covariance matrices in each of $M$ cluster groups. For this option the argument `group_structure` needs to be specified in the function `BLP_data` to determine the cluster group. This gives the block-diagonal form with $\Sigma_m$ as the variance covariance matrix for all $\xi_i$ in cluster $m$:
$$\Omega = \begin{pmatrix} \Sigma_1 & 0 &
\dots & 0\\
0 & \Sigma_2 & & 0\\
\vdots & & \ddots & 0\\
0 & 0 & 0 & \Sigma_M \\ \end{pmatrix}$$
## Elasticities
The following code demonstrates the calculation of elasticities for the estimation object `cereal_est`.
```{r}
# extract parameters from output
theta1_price <- cereal_est$theta_lin["price", ]
theta2 <- matrix(NA, nrow = 4, ncol = 5)
colnames(theta2) <- c("unobs_sd", "income", "incomesq", "age", "child")
rownames(theta2) <- c("(Intercept)", "price", "sugar", "mushy")
for (i in 1:13) {
theta2[cereal_est$indices[i, 1], cereal_est$indices[i, 2]] <- cereal_est$theta_rc[i]
}
delta_data <- data.frame(
"product_id" = cereal_data$parameters$product_id,
"cdid" = cereal_data$parameters$market_id_char_in,
"startingGuessesDelta" = cereal_est$delta
)
# always use update_BLP_data() to update data object to maintain consistent data
cereal_data <- update_BLP_data(
data_update = delta_data,
blp_data = cereal_data
)
shareObj <- getShareInfo(
blp_data = cereal_data,
par_theta2 = theta2,
printLevel = 1
)
get_elasticities(
blp_data = cereal_data,
share_info = shareObj,
theta_lin = theta1_price,
variable = "price",
products = c("cereal_1", "cereal_4"),
market = "market_2"
)
```
The value of the elasticity matrix in row $j$ and column $i$ for a variable $x$, gives the effect of a change in product $i$'s characteristic $x$ on the share of product $j$.
# Modular Examples
Further analysis like incorporating a supply side or performing a merger simulation often requires access to building blocks of the BLP algorithm. The following wrappers insure correct data inputs and access the internal functions of the algorithm.
In the following, you find an example of the contraction mapping and an evaluation of the GMM function at the starting guess:
```{r}
delta_eval <- getDelta_wrap(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
printLevel = 4
)
productData_cereal$startingGuessesDelta[1:6]
delta_eval$delta[1:6]
delta_eval$counter
gmm <- gmm_obj_wrap(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
printLevel = 2
)
gmm$local_min
```
Printed distances in the contraction mapping are maximum absolute distances between the current vector of mean utilities and the previous one.
For any $\theta_2$, you can compute predicted shares:
```{r}
shareObj <- getShareInfo(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
printLevel = 4
)
shareObj$shares[1:6]
```
The object contains a list of outputs that are useful for further economic analysis. For example, the list element `sij` contains share probabilities for every individual and needs to be given to calculate elasticities.
The gradient contains two important building blocks as explained in the appendix of @Nevo2001:
- $\frac{\partial s_{ijt}}{\partial \theta_2}$ , i.e. the derivative of individual $i$'s share of product $j$ in market $t$ with respect to non-linear parameters
- $\frac{\partial s_{ijt}}{\partial \delta}$ , i.e. the derivative of individual $i$'s share of product $j$ in market $t$ with respect to mean utilities
Both are used to compute the jacobian and are easy to obtain with the package as the following example demonstrates:
```{r}
# market 2:
derivatives1 <- dstdtheta_wrap(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
market = "market_2"
)
derivatives2 <- dstddelta_wrap(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
market = "market_2"
)
jac_mkt2 <- -solve(derivatives2) %*% derivatives1
jac_mkt2[1:5, 1:4]
# all markets
jacobian_nevo <- getJacobian_wrap(
blp_data = cereal_data,
par_theta2 = theta_guesses_cereal,
printLevel = 2
)
jacobian_nevo[25:29, 1:4] # compare to jac_mkt2
```
# Another Example: Merger Analysis with BLP's car data
Analyzing a hypothetical merger is demonstrated by the car data of @BLP1995. In this case, the preparation of product data comprises the computation of instruments as a function of product characteristics of competitors' products (for details, check @BLP1995). This example is based on data and documentation of @KM2014.
```{r}
# add owner matix to productData
own_pre <- dummies_cars
colnames(own_pre) <- paste0("company", 1:26)
productData_cars <- cbind(productData_cars, own_pre)
# construct instruments
nobs <- nrow(productData_cars)
X <- data.frame(
productData_cars$const, productData_cars$hpwt,
productData_cars$air, productData_cars$mpg, productData_cars$space
)
sum_other <- matrix(NA, nobs, ncol(X))
sum_rival <- matrix(NA, nobs, ncol(X))
sum_total <- matrix(NA, nobs, ncol(X))
for (i in 1:nobs) {
other_ind <- productData_cars$firmid == productData_cars$firmid[i] &
productData_cars$cdid == productData_cars$cdid[i] &
productData_cars$id != productData_cars$id[i]
rival_ind <- productData_cars$firmid != productData_cars$firmid[i] &
productData_cars$cdid == productData_cars$cdid[i]
total_ind <- productData_cars$cdid == productData_cars$cdid[i]
sum_other[i, ] <- colSums(X[other_ind == 1, ])
sum_rival[i, ] <- colSums(X[rival_ind == 1, ])
sum_total[i, ] <- colSums(X[total_ind == 1, ])
}
colnames(sum_other) <- paste0("IV", 1:5)
colnames(sum_rival) <- paste0("IV", 6:10)
productData_cars <- cbind(productData_cars, sum_other, sum_rival)
head(productData_cars)
# To show similarities between implementations of other authors,
# the variable "const" is used, although constants are considered by default.
blps_model <- as.formula("share ~ 0 + const + price + hpwt + air + mpg + space |
0 + const + hpwt + air + mpg + space |
0 + price + const + hpwt + air + mpg |
0 + IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7 + IV8 + IV9 + IV10")
car_data <- BLP_data(
model = blps_model,
market_identifier = "cdid",
product_identifier = "id",
additional_variables = paste0("company", 1:26), # check reordering works
productData = productData_cars,
blp_inner_tol = 1e-9,
blp_inner_maxit = 5000,
integration_method = "MLHS",
integration_accuracy = 50, integration_seed = 48
)
```
In the next step, starting guesses for random coefficients are generated from a standard normal distribution. The estimation of the model works like before.
```{r}
set.seed(121)
theta_guesses <- matrix(rnorm(5))
rownames(theta_guesses) <- c("price", "const", "hpwt", "air", "mpg")
colnames(theta_guesses) <- "unobs_sd"
car_est <- estimateBLP(
blp_data = car_data,
par_theta2 = theta_guesses,
solver_method = "BFGS", solver_maxit = 1000, solver_reltol = 1e-6,
extremumCheck = FALSE, printLevel = 0
)
summary(car_est)
```
Next, all parameters that are required by the subsequent merger analysis are extracted. Note that all extracted data is based on the estimation object `car_est` or the data object `car_data` to maintain data consistency (for example, the order of data in `product_data_cars` might differ from `car_data`). Moreover, mean utilities are updated in `car_data` by the values in the estimation object `car_est`.
```{r}
## Pre-Merger data
own_pre <- as.matrix(car_data$data$additional_data[, paste0("company", 1:26)])
delta_pre <- car_est$delta
theta1_price <- car_est$theta_lin["price", ]
theta2_price <- car_est$theta_rc["unobs_sd*price"]
theta2_all <- matrix(car_est$theta_rc)
rownames(theta2_all) <- c("price", "const", "hpwt", "air", "mpg")
colnames(theta2_all) <- "unobs_sd"
## update mean utility in data ( always use update_BLP_data() to update data object to maintain consistent data )
delta_data <- data.frame(
"id" = car_data$parameters$product_id,
"cdid" = car_data$parameters$market_id,
"delta" = delta_pre
)
car_data_updated <- update_BLP_data(
data_update = delta_data,
blp_data = car_data
)
```
In the next step, an estimate for marginal costs $mc$ **before** the merger is computed. The following is based on the FOC of a Bertrand equilibrium with prices $p$ before the merger:
$$ p^{pre} - \widehat{mc} = \Omega^{pre}(p^{pre})^{-1} \hat{s}(p^{pre}) $$
$\Omega^{pre}(p^{pre})^{-1}$ is defined marketwise as the inverse of
$$ \Omega^{pre}(p^{pre}) =
\pmatrix{
-\frac{\partial s_{1}}{\partial p_{1}} (p^{pre}) \cdot D_{1,1} & -\frac{\partial s_{2}}{\partial p_{1}} (p^{pre}) \cdot D_{1,2} & \cdots & -\frac{\partial s_{j}}{\partial p_{1}} (p^{pre}) \cdot D_{1,j} & \cdots & -\frac{\partial s_{J}}{\partial p_{1}} (p^{pre}) \cdot D_{1,J}\\
-\frac{\partial s_{1}}{\partial p_{2}} (p^{pre}) \cdot D_{2,1} & -\frac{\partial s_{2}}{\partial p_{2}} (p^{pre}) \cdot D_{2,2} & \cdots & -\frac{\partial s_{j}}{\partial p_{2}} (p^{pre}) \cdot D_{2,j} & \cdots & -\frac{\partial s_{J}}{\partial p_{2}} (p^{pre}) \cdot D_{2,J}\\
\vdots & \vdots & \ddots & \vdots & \ddots & \vdots\\
-\frac{\partial s_{1}}{\partial p_{k}} (p^{pre}) \cdot D_{k,1} & -\frac{\partial s_{2}}{\partial p_{k}} (p^{pre}) \cdot D_{k,2} & \cdots & -\frac{\partial s_{j}}{\partial p_{k}} (p^{pre}) \cdot D_{k,j} & \cdots &
-\frac{\partial s_{J}}{\partial p_{k}} (p^{pre}) \cdot D_{k,J}\\
\vdots & \vdots & \ddots & \vdots & \ddots & \vdots\\
-\frac{\partial s_{1}}{\partial p_{J}} (p^{pre}) \cdot D_{J,1} & -\frac{\partial s_{2}}{\partial p_{J}} (p^{pre}) \cdot D_{J,2} & \cdots & -\frac{\partial s_{j}}{\partial p_{J}} (p^{pre}) \cdot D_{J,j}& \cdots & -\frac{\partial s_{J}}{\partial p_{J}} (p^{pre}) \cdot D_{J,J}\\
}
$$
with
$$D_{k,j} =
\begin{cases}
1 & \text{if products k and j are produced by the same firm} \\
0 & \text{otherwise} \\
\end{cases}$$
Partial derivatives $\frac{\partial s_j}{\partial p_k}$ can be calculated based on the elasticity $\eta_{jk} = \frac{\partial s_j }{\partial p_k }\frac{ p_k}{ s_j}$, so
$$ \frac{\partial s_j}{\partial p_k} = \eta_{jk} \cdot \frac{ s_j}{ p_k} $$
In the following code chunk, these objects in a market `i` are labeled as follows:
- `own_prod_pre_i` ($D_{k,j}$)
- `elasticities_i` ($\eta_{jk}$)
- `derivatives_i` ($\eta_{jk} \cdot \frac{ s_j}{ p_k}$)
- `-solve(t(derivatives_i) * own_prod_pre_i)` ($\Omega^{pre}(p^{pre})^{-1}$)
- `shareObj$shares` ($\hat{s}(p^{pre})$).
```{r}
## calculate sij
shareObj <- getShareInfo(
blp_data = car_data_updated,
par_theta2 = theta2_all,
printLevel = 0
)
## computation of marginal costs
market_id <- car_data$parameters$market_id
nmkt <- length(unique(market_id))
markups <- numeric(length(market_id))
sh <- shareObj$shares
prices_pre <- car_data$data$X_rand[, "price"]
for (i in 1:nmkt) {
mkt_ind <- market_id == i
share_i <- sh[ mkt_ind ]
price_pre_i <- prices_pre[ mkt_ind ]
scalar_i <- matrix(1 / share_i) %*% matrix(price_pre_i, nrow = 1)
elasticities_i <- get_elasticities(
blp_data = car_data_updated,
share_info = shareObj,
theta_lin = theta1_price,
variable = "price",
market = i,
printLevel = 0
)
derivatives_i <- elasticities_i / scalar_i # partial derivatives of shares wrt price
own_pre_i <- own_pre[ mkt_ind, ]
own_prod_pre_i <- own_pre_i %*% t(own_pre_i) # if element (i,j) equals 1, that means that prod i and j are produced by same firm
markups[mkt_ind] <- c(-solve(t(derivatives_i) * own_prod_pre_i) %*% share_i)
}
marg_cost <- prices_pre - markups
```
The ownership matrix is adjusted to implement a hypothetical merger between Chrysler and GM:
```{r}
# Merger between company 16 and 19 (i.e. GM and Chrysler)
prices_post <- numeric(2217)
own_post <- cbind(
own_pre[, 1:15],
own_pre[, 16] + own_pre[, 19],
own_pre[, 17:18],
own_pre[, 20:26]
)
```
To analyze the effect on prices the FOC of the new equilibrium must be solved:
$$ p^{post} - \widehat{mc} = \Omega^{post}(p^{post})^{-1} \hat{s}(p^{post}) $$
The solution of this set of non-linear equations is obtained by the function `foc_bertrand_mkt` and the package `nleqslv`:
```{r, eval = FALSE}
foc_bertrand_mkt <- function(par, own_prod, blp_data, mkt, marg_cost, theta_lin, theta_rc) {
# argument par: candidate for post merger prices
# arguments own_prod, blp_data, mkt, marg_cost, theta_lin, theta_rc: see previous code blocks
# post merger updates: update the BLP_data object for market i
tmp <- data.frame(
"id" = blp_data$parameters$product_id,
"cdid" = blp_data$parameters$market_id,
"delta" = blp_data$data$delta,
"price" = blp_data$data$X_rand[, "price"]
)
market_ind <- blp_data$parameters$market_id == mkt
delta_old <- blp_data$data$delta
prices_pre <- blp_data$data$X_rand[, "price"]
tmp$price[ market_ind ] <- par
tmp$delta[ market_ind ] <- delta_old[market_ind] - prices_pre[market_ind] * theta_lin + par * theta_lin
new_blp_data <- update_BLP_data(
blp_data = blp_data,
data_update = tmp
)
ShareObj <- getShareInfo(
blp_data = new_blp_data,
par_theta2 = theta_rc,
printLevel = 0
)
implied_shares <- as.matrix(ShareObj$shares[market_ind])
elasticities_post_mkt <- get_elasticities(
blp_data = new_blp_data,
share_info = ShareObj,
theta_lin = theta_lin,
variable = "price",
market = mkt,
printLevel = 0
)
scalar_mkt <- matrix(1 / implied_shares) %*% matrix(par, nrow = 1)
derivatives_mkt <- elasticities_post_mkt / scalar_mkt
markups_post <- c(-solve(t(derivatives_mkt) * own_prod) %*% implied_shares)
differences <- par - marg_cost[market_ind] - markups_post
return(differences)
}
```
Finally, the function is used to compute the new equilibrium:
```{r,eval=FALSE}
library(nleqslv) # to solve non linear first order conditions
for (i in 1:nmkt) {
mkt_ind <- market_id == i
own_post_i <- own_post[ mkt_ind, ]
own_prod_post_i <- own_post_i %*% t(own_post_i)
price_pre_i <- prices_pre[ mkt_ind ]
solution <- nleqslv(
x = price_pre_i, foc_bertrand_mkt, # startingguesses: price_pre_i
own_prod = own_prod_post_i,
blp_data = car_data_updated,
mkt = i,
marg_cost = marg_cost,
theta_lin = theta1_price,
theta_rc = theta2_all
)
prices_post[ market_id == i ] <- solution$x
}
```
# References