BLPestimatoR provides an efficient estimation algorithm to perform the demand estimation described in Berry, Levinsohn, and Pakes (1995). The routine uses analytic gradients and offers a large number of optimization routines and implemented integration methods as discussed in Brunner et al. (2017).
This extended documentation demonstrates the steps of a typical demand estimation with the package:
BLP_data (includes the specification of a model and providing integration draws for observed or unobserved heterogeneity)estimate_BLPsummaryget_elasticitiesThe use of the algorithm’s building blocks is explained as well. The well-known training data for the cereal market from Nevo (2001) is used for demonstration purposes.
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 |:
Nevo’s model can be translated into the following formula syntax:
library(BLPestimatoR)
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
price, sugar, mushy and an intercept)income, incomesq, age, child)\[\theta_2 = \begin{pmatrix} \sigma_1 & \gamma_{1,1} & \cdots & \gamma_{1,R} \\ \sigma_2 & \gamma_{2,1} & \cdots & \gamma_{2,R} \\ \vdots & \vdots & \ddots & \vdots \\ \sigma_M & \gamma_{M,1} & \cdots & \gamma_{M,R} \end{pmatrix}\]
Product related variables are collected in the dataframe productData with the following requirements:
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:
head(productData)
#> price const sugar mushy share cdid IV1 IV2
#> 1 0.07208794 1 2 1 0.012417212 market_1 -0.2159728 0.04057341
#> 2 0.11417849 1 18 1 0.007809387 market_1 -0.2452393 0.05474226
#> 3 0.13239066 1 4 1 0.012994511 market_1 -0.1764587 0.04659597
#> 4 0.13034408 1 3 0 0.005769961 market_1 -0.1214013 0.04876037
#> 5 0.15482331 1 12 0 0.017934141 market_1 -0.1326114 0.03962835
#> 6 0.13704921 1 14 0 0.026601892 market_1 -0.1534998 0.04298842
#> IV3 IV4 IV5 IV6 IV7 IV8
#> 1 -3.247948 -0.523937695 -0.23246005 0.0068326605 3.1397395 -0.57478633
#> 2 -19.832461 -0.180519694 0.01468859 0.0007988026 0.2876539 0.03293960
#> 3 -2.878531 -0.284219004 -0.21553691 -0.0318693281 2.8862741 -0.74976495
#> 4 -2.059918 -0.328412257 -0.22206995 -0.0314740402 4.4531096 0.25567529
#> 5 -6.137598 -0.138625095 -0.18936521 -0.0437471023 -3.5546508 0.13882114
#> 6 -8.417332 0.007829087 -0.13850121 -0.0210582272 -2.7594799 0.05020052
#> IV9 IV10 IV11 IV12 IV13 IV14
#> 1 0.2062202 0.1774656 2.1163580 -0.15470824 -0.0057964065 0.01453802
#> 2 0.1051208 -0.2875618 -7.3740909 -0.57641176 0.0129908544 0.07614324
#> 3 -0.4789565 0.2147389 2.1878721 -0.20734643 0.0035092777 0.09178117
#> 4 -0.4729673 0.3560980 2.7045762 0.04074801 -0.0037242656 0.09473168
#> 5 -0.6886784 0.2602726 1.2612419 0.03483558 -0.0005676374 0.10245147
#> 6 -0.2734440 0.1273060 0.3375543 0.02351037 0.0002637777 0.08627983
#> IV15 IV16 IV17 IV18 IV19 IV20
#> 1 0.12624398 0.06734464 0.06842261 0.03480046 0.12634612 0.03548368
#> 2 0.02973565 0.08786672 0.11050060 0.08778380 0.04987192 0.07257905
#> 3 0.16377308 0.11188073 0.10822551 0.08643905 0.12234707 0.10184248
#> 4 0.13527378 0.08809001 0.10176745 0.10177748 0.11074119 0.10433204
#> 5 0.13063951 0.08481820 0.10107461 0.12516923 0.13346381 0.12111110
#> 6 0.07233581 0.02225051 0.10564387 0.11603699 0.09965063 0.10572660
#> product_id productdummy
#> 1 cereal_1 product1
#> 2 cereal_2 product2
#> 3 cereal_3 product3
#> 4 cereal_4 product4
#> 5 cereal_5 product5
#> 6 cereal_6 product6The 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:
market_identifier that matches each line to a market (same variable name as in productData)In the cereal example, observed heterogeneity is provided as follows (list names correspond to the demographics):
demographicData$income[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 0.49512349 0.3787622 0.1050146 -1.48548093
#> 2 market_2 0.05389113 -1.5030833 0.6217917 0.26922901
#> 3 market_3 0.62459788 -0.2242698 -0.2846169 0.67845943
#> 4 market_4 0.94419851 0.4056359 0.5859923 0.01813051
demographicData$incomesq[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 8.3313042 6.121865 1.030803 -25.5836047
#> 2 market_2 0.0966346 -25.849845 10.767233 4.0668173
#> 3 market_3 10.8215619 -4.894545 -5.956954 11.8673879
#> 4 market_4 17.1121550 6.629730 10.075529 -0.5537043
demographicData$age[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 -0.2301090 -2.5326941 -0.006965458 -0.8279460
#> 2 market_2 0.1414545 -0.1813188 -1.279931134 -0.4532526
#> 3 market_3 -0.1813188 -0.5867840 0.208145922 -0.4532526
#> 4 market_4 0.7444506 0.1414545 0.645359728 0.8346017
demographicData$child[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 -0.2308511 0.7691489 -0.2308511 0.7691489
#> 2 market_2 -0.2308511 -0.2308511 0.7691489 -0.2308511
#> 3 market_3 -0.2308511 0.7691489 -0.2308511 -0.2308511
#> 4 market_4 -0.2308511 -0.2308511 -0.2308511 -0.2308511If 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):
originalDraws$constant[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 0.434100553 -0.7266491 -0.6230607 -0.04131699
#> 2 market_2 0.001702598 0.2228245 -1.0287201 0.38312476
#> 3 market_3 -0.529569305 0.7248712 0.2910167 -1.39164253
#> 4 market_4 0.019233489 -1.4250308 -0.2678170 -2.26541236
# renaming constants:
names(originalDraws)[1] <- "(Intercept)"
originalDraws$price[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 -1.5008378 0.1331817 -0.1382405 1.2571357
#> 2 market_2 0.0276477 -0.8414414 -0.9056861 -2.0306179
#> 3 market_3 -0.4768085 -0.9348689 1.4300456 2.6188581
#> 4 market_4 0.5396019 0.1698178 0.7128376 -0.1216309
originalDraws$sugar[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 -1.1510786 -0.5007498 0.7974412 -0.6830540
#> 2 market_2 1.0451218 1.0170277 2.3012550 0.1490338
#> 3 market_3 -1.0187809 0.3107185 -0.9531532 0.8057080
#> 4 market_4 -0.5760582 -0.5072059 -0.7496653 1.1163544
originalDraws$mushy[1:4,1:5]
#> cdid draw_1 draw_2 draw_3 draw_4
#> 1 market_1 0.16101681 0.1297316 -0.79554915 0.2590437
#> 2 market_2 0.23595798 0.1345995 0.07284586 0.9509871
#> 3 market_3 0.02922435 -0.1704289 0.36395246 0.4879702
#> 4 market_4 1.27701102 0.4284194 0.46124654 -1.0110437As 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 structures and prepares the data for estimation and creates the data object:
productData$startingGuessesDelta <- c( log(w_guesses)) # include orig. draws in the product data
nevo_data <- BLP_data(model = nevos_model,
market_identifier="cdid",
par_delta = "startingGuessesDelta",
product_identifier = "product_id",
productData = productData,
demographic_draws = demographicData,
blp_inner_tol = 1e-6, blp_inner_maxit = 5000,
integration_draws = originalDraws,
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 (should 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.
The provided set of starting guesses par_theta2 is matched with formula input and demographic data:
par_theta2 must match with the random coefficients specified in the formula (note: constants must be named (Intercept) )par_theta2 must match with list entry names of demographicData and a column for unobserved heterogeneity (must be named `unobs_sd)NAs in par_theta2 indicate the exclusion from estimation, i.e. the coefficient is assumed to be zero.These requirements are demonstrated with Nevo’s starting guesses:
#before:
theta_guesses
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.3302 5.4819 0.0 0.2037 0.0000
#> [2,] 2.4526 15.8935 -1.2 0.0000 2.6342
#> [3,] 0.0163 -0.2506 0.0 0.0511 0.0000
#> [4,] 0.2441 1.2650 0.0 -0.8091 0.0000
theta_guesses[theta_guesses==0] <- NA
colnames(theta_guesses) <- c("unobs_sd", "income", "incomesq", "age", "child" )
rownames(theta_guesses) <- c("(Intercept)", "price" , "sugar", "mushy")
# correctly named:
theta_guesses
#> unobs_sd income incomesq age child
#> (Intercept) 0.3302 5.4819 NA 0.2037 NA
#> price 2.4526 15.8935 -1.2 NA 2.6342
#> sugar 0.0163 -0.2506 NA 0.0511 NA
#> mushy 0.2441 1.2650 NA -0.8091 NAThe following code performs the demand estimation:
blp_est <- estimateBLP( blp_data=nevo_data,
par_theta2 = theta_guesses,
solver_method = "BFGS", solver_maxit = 1000, solver_reltol = 1e-6,
standardError = "heteroskedastic",
extremumCheck = FALSE ,
printLevel = 1 )
#> blp_data were prepared with the following arguments:
#> BLP_data(model = nevos_model, market_identifier = "cdid", product_identifier = "product_id",
#> par_delta = "startingGuessesDelta", productData = productData,
#> demographic_draws = demographicData, integration_draws = originalDraws,
#> integration_weights = rep(1/20, 20), blp_inner_tol = 1e-06,
#> blp_inner_maxit = 5000)
#> Starting a BLP demand estimation with 2256 observations in 94 markets...
#> [integration::method integration::amountDraws 20 ]
#> [blp::inner_tol 1e-06 blp::inner_maxit 5000 ]
#> [solver::method BFGS solver::maxit 1000 solver::reltol 1e-06 ]
#> gmm objective: 29.3522
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 22451.18
#> gmm objective: 775.9515
#> gmm objective: 71.6209
#> gmm objective: 26.8851
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 330914
#> gmm objective: 12694.84
#> gmm objective: 311.8842
#> gmm objective: 38.2627
#> gmm objective: 26.9935
#> gmm objective: 26.8196
#> gmm objective: 40772.63
#> gmm objective: 1186.144
#> gmm objective: 103.4133
#> gmm objective: 27.9556
#> gmm objective: 26.1524
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 28234.6
#> gmm objective: 756.8709
#> gmm objective: 37.7704
#> gmm objective: 24.7211
#> gmm objective: 5772.739
#> gmm objective: 202.5456
#> gmm objective: 27.2616
#> gmm objective: 23.9898
#> gmm objective: 838.469
#> gmm objective: 32.1783
#> gmm objective: 21.9662
#> gmm objective: 23912.56
#> gmm objective: 244.3514
#> gmm objective: 20.9664
#> gmm objective: 1417.216
#> gmm objective: 56.3625
#> gmm objective: 21.2928
#> gmm objective: 20.6459
#> gmm objective: 24.7031
#> gmm objective: 19.3915
#> gmm objective: 192.3949
#> gmm objective: 18.8625
#> gmm objective: 20.5334
#> gmm objective: 17.7234
#> gmm objective: 16.7846
#> gmm objective: 16.4664
#> gmm objective: 15.9588
#> gmm objective: 15.0231
#> gmm objective: 14.9101
#> gmm objective: 14.8987
#> gmm objective: 14.8961
#> gmm objective: 14.8902
#> gmm objective: 14.8788
#> gmm objective: 14.8479
#> gmm objective: 14.7731
#> gmm objective: 14.5807
#> gmm objective: 14.0873
#> gmm objective: 12.8012
#> gmm objective: 11.9828
#> gmm objective: 9.4672
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 150504.2
#> gmm objective: 6437.658
#> gmm objective: 224.7393
#> gmm objective: 13.0643
#> gmm objective: 9.2023
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 10140.17
#> gmm objective: 554.5767
#> gmm objective: 34.6091
#> gmm objective: 10.0322
#> gmm objective: 9.1844
#> gmm objective: 75730.14
#> gmm objective: 2145.751
#> gmm objective: 45.6103
#> gmm objective: 9.9089
#> gmm objective: 9.0889
#> gmm objective: 111102.8
#> gmm objective: 17852.06
#> gmm objective: 389.125
#> gmm objective: 20.1367
#> gmm objective: 8.7624
#> gmm objective: 766.0638
#> gmm objective: 49.5382
#> gmm objective: 10.0242
#> gmm objective: 8.6598
#> gmm objective: 155.1232
#> gmm objective: 12.3475
#> gmm objective: 8.5964
#> gmm objective: 308.36
#> gmm objective: 10.1499
#> gmm objective: 8.3448
#> gmm objective: 1593.047
#> gmm objective: 18.5902
#> gmm objective: 7.9521
#> gmm objective: 24.6953
#> gmm objective: 7.4967
#> gmm objective: 7.8465
#> gmm objective: 7.4219
#> gmm objective: 7.3411
#> gmm objective: 7.2325
#> gmm objective: 7.0497
#> gmm objective: 6.9818
#> gmm objective: 6.9711
#> gmm objective: 6.9695
#> gmm objective: 6.9696
#> gmm objective: 6.9692
#> gmm objective: 6.9633
#> gmm objective: 6.9462
#> gmm objective: 6.8986
#> gmm objective: 6.8301
#> gmm objective: 6.6517
#> gmm objective: 6.3046
#> gmm objective: 5.7187
#> gmm objective: 5.0274
#> gmm objective: 4.6491
#> gmm objective: 4.5752
#> gmm objective: 54557.62
#> gmm objective: 2335.286
#> gmm objective: 90.1735
#> gmm objective: 7.3992
#> gmm objective: 4.667
#> gmm objective: 4.576
#> gmm objective: 4.575
#> gmm objective: 14656.41
#> gmm objective: 601.8347
#> gmm objective: 32.5354
#> gmm objective: 5.863
#> gmm objective: 4.6245
#> gmm objective: 4.5771
#> gmm objective: 4.5758
#> gmm objective: 4.5759
#> gmm objective: 4.5757
#> gmm objective: 4.5756
#> gmm objective: 4.5755
#> gmm objective: 4.5754
#> gmm objective: 4.5754
#> gmm objective: 4.5754
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5752
#> gmm objective: 4.5752
#> gmm objective: 16969.25
#> gmm objective: 603.3225
#> gmm objective: 23.8438
#> gmm objective: 5.2753
#> gmm objective: 4.5966
#> gmm objective: 4.575
#> gmm objective: 4.5752
#> gmm objective: 4.5755
#> gmm objective: 4.5755
#> gmm objective: 4.5754
#> gmm objective: 4.5754
#> gmm objective: 4.5754
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5753
#> gmm objective: 4.5752
#> gmm objective: 4.5752
#> ------------------------------------------
#> Solver message: Successful convergence
#> ------------------------------------------
#> Final GMM evaluation at optimal parameters:
#> gmm objective: 4.5744
#> theta (RC): 0.55 3.29 -0.01 0.09
#> theta (demogr.): 2.3 577.44 -0.38 0.83 0 -29.63 0 0 1.27 0 0.05 -1.35 0 11.03 0 0
#> inner iterations: 70
#> gradient: -0.0605 0.0017 -0.162 -0.0354 -0.0926 -0.0083 -2.2061 0.2945 -0.1468 0.0797 -1.2189 0.2315 2e-04
#> Using the heteroskedastic asymptotic variance-covariance matrix...
summary(blp_est)
#>
#> Data information:
#>
#> 94 market(s) with 2256 products
#> 25 linear coefficient(s) (24 exogenous coefficients)
#> 13 non-linear parameters related to random coefficients
#> 4 demographic variable(s)
#>
#> Estimation results:
#>
#> Linear Coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -2.5769264 0.8531569 -3.020460 2.523909e-03
#> price -62.1403350 14.5135858 -4.281529 1.856137e-05
#> productdummyproduct10 1.9000897 0.6653478 2.855784 4.293071e-03
#> productdummyproduct11 3.6767952 0.6664428 5.517045 3.447468e-08
#> productdummyproduct12 0.2615994 0.2287542 1.143583 2.527967e-01
#> productdummyproduct13 2.3591835 0.2184213 10.801071 3.402135e-27
#> productdummyproduct14 3.8446786 0.6855662 5.608034 2.046376e-08
#> productdummyproduct15 4.2873240 0.6779154 6.324276 2.544228e-10
#> productdummyproduct16 6.0916947 0.6723137 9.060792 1.295068e-19
#> productdummyproduct17 2.6036321 0.6510521 3.999115 6.357976e-05
#> productdummyproduct18 1.4324858 0.6079004 2.356448 1.845065e-02
#> productdummyproduct19 4.3135147 0.6191372 6.966977 3.238237e-12
#> productdummyproduct2 4.8750516 0.6334282 7.696297 1.400664e-14
#> productdummyproduct20 4.1706989 0.6075700 6.864557 6.669789e-12
#> productdummyproduct21 4.9106550 0.6827987 7.191951 6.387201e-13
#> productdummyproduct22 1.7215961 0.5917418 2.909370 3.621574e-03
#> productdummyproduct23 3.2960677 0.6839398 4.819237 1.441085e-06
#> productdummyproduct24 1.8879979 0.7307294 2.583717 9.774202e-03
#> productdummyproduct3 2.3028803 0.2314068 9.951652 2.480314e-23
#> productdummyproduct4 1.3668373 0.5940217 2.300989 2.139225e-02
#>
#> ...
#>
#> 5 estimates are omitted. They are available in the LinCoefficients generated by summary.
#>
#> Random Coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> unobs_sd*(Intercept) 0.551339802 0.16015070 3.4426313 0.0005760842
#> unobs_sd*price 3.285559242 1.30636137 2.5150462 0.0119016778
#> unobs_sd*sugar -0.005237547 0.01336128 -0.3919943 0.6950624582
#> unobs_sd*mushy 0.091406886 0.18480221 0.4946201 0.6208683092
#> income*(Intercept) 2.303677746 1.19131427 1.9337280 0.0531465808
#> income*price 577.439895043 264.92313045 2.1796507 0.0292833612
#> income*sugar -0.384035468 0.11965304 -3.2095755 0.0013293115
#> income*mushy 0.826450257 0.77955169 1.0601609 0.2890713878
#> incomesq*price -29.627526252 13.80993188 -2.1453782 0.0319226238
#> age*(Intercept) 1.268858974 0.62894244 2.0174485 0.0436487312
#> age*sugar 0.051714014 0.02546546 2.0307513 0.0422802273
#> age*mushy -1.350839886 0.66343728 -2.0361230 0.0417380079
#> child*price 11.025866514 4.11565257 2.6790081 0.0073840611
#>
#> Wald Test
#> 126.7789 on 13 DF, p-value: 9.13936413478121e-21
#>
#> Computational Details:
#> Solver converged with 173 iterations to a minimum at 4.5744 .
#> Local minima check: NA
#> stopping criterion outer loop: 1e-06
#> stopping criterion inner loop: 1e-06
#> Market shares are integrated with provided_by_user and 20 draws.
#> Method for standard errors: heteroskedasticThe 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.
nevo_data2 <- BLP_data(model = nevos_model,
market_identifier="cdid",
product_identifier = "product_id",
productData = productData,
integration_method = "MLHS",
integration_accuracy = 20, integration_seed = 213)
#> Mean utility is initialized with 0 because of missing or invalid par_delta argument.
blp_est2 <- estimateBLP( blp_data=nevo_data2, printLevel = 1 )
#> blp_data were prepared with the following arguments:
#> BLP_data(model = nevos_model, market_identifier = "cdid", product_identifier = "product_id",
#> productData = productData, integration_accuracy = 20, integration_method = "MLHS",
#> integration_seed = 213)
#> Starting a BLP demand estimation with 2256 observations in 94 markets...
#> [integration::method integration::amountDraws 20 ]
#> [blp::inner_tol 1e-09 blp::inner_maxit 10000 ]
#> [solver::method BFGS solver::maxit 10000 solver::reltol 1e-06 ]
#> gmm objective: 189.9432
#> gmm objective: Inf [delta contains NaN's]
#> gmm objective: 38181.19
#> gmm objective: 1730.558
#> gmm objective: 263.4083
#> gmm objective: 192.4396
#> gmm objective: 189.8527
#> gmm objective: 14921.45
#> gmm objective: 794.7065
#> gmm objective: 209.7249
#> gmm objective: 188.7052
#> gmm objective: 299.2339
#> gmm objective: 191.7708
#> gmm objective: 188.5727
#> gmm objective: 188.5122
#> gmm objective: 188.5029
#> gmm objective: 188.5029
#> gmm objective: 201.0545
#> gmm objective: 189.0058
#> gmm objective: 188.5229
#> gmm objective: 188.5037
#> gmm objective: 188.503
#> gmm objective: 188.5029
#> gmm objective: 188.5029
#> ------------------------------------------
#> Solver message: Successful convergence
#> ------------------------------------------
#> Final GMM evaluation at optimal parameters:
#> gmm objective: 188.5029
#> theta (RC): 0.15 -0.3 0 0.14
#> theta (demogr.):
#> inner iterations: 61
#> gradient: 2e-04 0 -0.0204 1e-04
#> Using the heteroskedastic asymptotic variance-covariance matrix...
#> Extremum Check: positive
summary(blp_est2)
#>
#> Data information:
#>
#> 94 market(s) with 2256 products
#> 25 linear coefficient(s) (24 exogenous coefficients)
#> 4 non-linear parameters related to random coefficients
#> 0 demographic variable(s)
#>
#> Estimation results:
#>
#> Linear Coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -1.7799087 0.2397708 -7.423375 1.141731e-13
#> price -30.0963431 1.1024558 -27.299365 4.315817e-164
#> productdummyproduct10 1.8641775 0.2406974 7.744900 9.565674e-15
#> productdummyproduct11 1.7676586 0.2248843 7.860301 3.832126e-15
#> productdummyproduct12 -0.2876883 0.1602658 -1.795070 7.264253e-02
#> productdummyproduct13 2.2491273 0.1504198 14.952338 1.503747e-50
#> productdummyproduct14 1.7844367 0.2282750 7.817049 5.407593e-15
#> productdummyproduct15 2.1642177 0.2255519 9.595210 8.374641e-22
#> productdummyproduct16 3.6877143 0.1808915 20.386330 2.211237e-92
#> productdummyproduct17 0.7941551 0.2367072 3.355010 7.936202e-04
#> productdummyproduct18 0.8806886 0.2350368 3.747024 1.789448e-04
#> productdummyproduct19 1.7652157 0.1863495 9.472608 2.729424e-21
#> productdummyproduct2 2.3645007 0.1771094 13.350512 1.176380e-40
#> productdummyproduct20 2.8136100 0.2308568 12.187687 3.615371e-34
#> productdummyproduct21 2.6849320 0.2328602 11.530232 9.289330e-31
#> productdummyproduct22 0.4781141 0.2306766 2.072659 3.820398e-02
#> productdummyproduct23 1.3807879 0.2257963 6.115192 9.644062e-10
#> productdummyproduct24 2.1776559 0.2358099 9.234795 2.587808e-20
#> productdummyproduct3 1.8011734 0.1491293 12.077934 1.381474e-33
#> productdummyproduct4 0.7697762 0.2296839 3.351459 8.038688e-04
#>
#> ...
#>
#> 5 estimates are omitted. They are available in the LinCoefficients generated by summary.
#>
#> Random Coefficients
#> Estimate Std. Error t value Pr(>|t|)
#> unobs_sd*(Intercept) 0.148175983 1.6693199 0.08876428 0.9292692
#> unobs_sd*price -0.304298641 5.6552127 -0.05380852 0.9570877
#> unobs_sd*sugar 0.004900458 0.1276616 0.03838630 0.9693797
#> unobs_sd*mushy 0.141711614 2.0588204 0.06883146 0.9451238
#>
#> Wald Test
#> 0.0141 on 4 DF, p-value: 0.999975209516464
#>
#> Computational Details:
#> Solver converged with 24 iterations to a minimum at 188.5029 .
#> Local minima check: positive
#> stopping criterion outer loop: 1e-06
#> stopping criterion inner loop: 1e-09
#> Market shares are integrated with MLHS and 20 draws.
#> Method for standard errors: heteroskedasticStandard 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 matrizes 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 \(m\):
\[\Omega = \begin{pmatrix} \Sigma_1 & 0 & \dots & 0\\ 0 & \Sigma_2 & & 0\\ \vdots & & \ddots & 0\\ 0 & 0 & 0 & \Sigma_M \\ \end{pmatrix}\]
The following code demonstrates the calculation of elasticities.
get_elasticities(blp_data=nevo_data,
blp_estimation= blp_est,
variable = "price",
products = c("cereal_1","cereal_4"),
market = "market_2")
#> cereal_1 cereal_4
#> cereal_1 -1.710335908 0.00543326
#> cereal_4 0.006965469 -2.71765805The 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 \(j\).
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:
delta_eval <- getDelta_wrap( blp_data=nevo_data,
par_theta2 = theta_guesses,
printLevel = 4 )
#> ----------------------
#> dist: 0.709015
#> dist: 0.147383
#> dist: 0.0669541
#> dist: 0.0430535
#> dist: 0.0324573
#> dist: 0.0263458
#> dist: 0.02112
#> dist: 0.0169119
#> dist: 0.0135664
#> dist: 0.0109078
#> dist: 0.00878933
#> dist: 0.00709574
#> dist: 0.00573767
#> dist: 0.00464573
#> dist: 0.00376578
#> dist: 0.00305531
#> dist: 0.00248075
#> dist: 0.0020155
#> dist: 0.00163834
#> dist: 0.00133231
#> dist: 0.00108382
#> dist: 0.000881926
#> dist: 0.000717803
#> dist: 0.000584333
#> dist: 0.000475754
#> dist: 0.000387398
#> dist: 0.000315485
#> dist: 0.000256942
#> dist: 0.000209276
#> dist: 0.000170463
#> dist: 0.000138854
#> dist: 0.000113111
#> dist: 9.21432e-005
#> dist: 7.50641e-005
#> dist: 6.11519e-005
#> dist: 4.9819e-005
#> dist: 4.05869e-005
#> dist: 3.30659e-005
#> dist: 2.69389e-005
#> dist: 2.19473e-005
#> dist: 1.78808e-005
#> dist: 1.45678e-005
#> dist: 1.18687e-005
#> dist: 9.66966e-006
#> dist: 7.87811e-006
#> dist: 6.41851e-006
#> dist: 5.22934e-006
#> dist: 4.26049e-006
#> dist: 3.47115e-006
#> dist: 2.82805e-006
#> dist: 2.31279e-006
#> dist: 1.91861e-006
#> dist: 1.59161e-006
#> dist: 1.32035e-006
#> dist: 1.09532e-006
#> dist: 9.96355e-007
productData$startingGuessesDelta[1:6]
#> [1] -3.944367 -2.845205 -3.958199 -4.934153 -2.425356 -4.086816
delta_eval$delta[1:6]
#> cereal_1_market_1 cereal_2_market_1 cereal_3_market_1 cereal_4_market_1
#> -7.069730 -4.357650 -6.056847 -5.887444
#> cereal_5_market_1 cereal_6_market_1
#> -3.501263 -3.079300
delta_eval$counter
#> [1] 56
gmm <- gmm_obj_wrap( blp_data=nevo_data,
par_theta2 = theta_guesses,
printLevel = 2)
#> gmm objective: 29.3523
#> theta (RC): 0.33 2.45 0.02 0.24
#> theta (demogr.): 5.48 15.89 -0.25 1.26 0 -1.2 0 0 0.2 0 0.05 -0.81 0 2.63 0 0
#> inner iterations: 69
#> gradient: 9.8431 0.3168 363.4813 16.3593 10.5995 0.7023 42.4814 -3.4753 13.4895 -2.0292 10.879 1.2831 -0.571
#> [1] 29.3523
gmm$local_min
#> [1] 29.3523Printed distances in the contraction mapping are maximum absolute distance between the current vector of mean utilities and the previous one.
For any \(\theta_2\), you can compute predicted shares:
shares <- getShares_wrap( blp_data=nevo_data,
par_theta2 = theta_guesses, printLevel = 4)
shares[1:6]
#> cereal_1_market_1 cereal_2_market_1 cereal_3_market_1 cereal_4_market_1
#> 0.066138546 0.026031055 0.030931817 0.005596246
#> cereal_5_market_1 cereal_6_market_1
#> 0.039475547 0.007812016The gradient contains two important building blocks as explained in the appendix of Nevo (2001):
\(\frac{\partial s_{ijt}}{\partial \theta_2}\) , 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:
derivatives1 <- dstdtheta_wrap( blp_data=nevo_data,
cm_input = delta_eval,
market = "market_2")
#> Evaluated at par_theta2:
#> [1] 0.3302 2.4526 0.0163 0.2441 5.4819 15.8935 -0.2506 1.2650
#> [9] -1.2000 0.2037 0.0511 -0.8091 2.6342
derivatives2 <- dstddelta_wrap( blp_data=nevo_data,
cm_input = delta_eval,
market = "market_2")
#> Evaluated at par_theta2:
#> [1] 0.3302 2.4526 0.0163 0.2441 5.4819 15.8935 -0.2506 1.2650
#> [9] -1.2000 0.2037 0.0511 -0.8091 2.6342
jacobian_nevo <- getJacobian_wrap(blp_data=nevo_data,
par_theta2 = theta_guesses,
printLevel = 2)
jacobian_nevo[1:5,1:4]
#> unobs_sd*(Intercept) unobs_sd*price unobs_sd*sugar
#> cereal_1_market_1 -0.1029043 0.05148210 -1.689497
#> cereal_2_market_1 -0.1941521 0.03483915 -7.935020
#> cereal_3_market_1 -0.1099606 0.03125065 -1.449870
#> cereal_4_market_1 -0.1341227 0.03963801 -1.451742
#> cereal_5_market_1 -0.2273174 0.04610410 -5.765267
#> unobs_sd*mushy
#> cereal_1_market_1 -0.07753320
#> cereal_2_market_1 -0.06694149
#> cereal_3_market_1 -0.13773737
#> cereal_4_market_1 -0.09275354
#> cereal_5_market_1 0.04301055Berry, Steven, James Levinsohn, and Ariel Pakes. 1995. “Automobile Prices in Market Equilibrium.” Econometrica.
Brunner, Daniel, Florian Heiss, Andre Romahn, and Constantin Weiser. 2017. “Reliable Estimation of Random Coefficient Logit Demand Models.” DICE Discussion Paper No 267.
Nevo, Aviv. 2001. “A Practitioner’s Guide to Estimation of Random-Coefficients Logit Models of Demand.” Journal of Economics & Management Strategy.