--- 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