Search model#


Below is the documentation for the Matlab code in “NNE_search” folder at this GitHub repository. The code uses NNE to estimate a consumer search model. You are welcome to modify the code to estimate your own structural model.

Workflow#

The following commands run a Monte Carlo experiment that estimates the search model from a simulated dataset.

>> monte_carlo_data         % simulate a dataset and save it in data.mat
>> nne_gen                  % generate the training examples for NNE and save them in nne_training.mat
>> nne_train                % train a neural net and then apply it on data.mat

Description of each file#

model_seq_search.m#

This function codes a sequential search model.

[yd, yt, order] = model_seq_search(pos, z, consumer_id, theta, curve)

  • Inputs:

    • pos: product ranking positions (which affects search costs)

    • z: other product attributes (e.g., review rating, price)

    • consumer_id: indices of consumers

    • theta: search model parameter vector

    • curve: lookup table between reservation utility and search cost, available from curve_seq_search.csv

  • Outputs:

    • yd: dummies indicating searches

    • yt: dummies indicating purchases

    • order: search order

moments.m#

This function summarizes data into a set of moments.

output = moments(pos, z, consumer_id, yd, yt)

  • Inputs: as described above for model_seq_search.m.

  • Output: a vector collecting the moments.

normalRegressionLayer.m#

This file codes the cross-entropy loss. This custom loss function is needed if we want NNE to output variance estimates in addition to point estimates.

monte_carlo_data.m#

This script creates a dataset for Monte Carlo experiment. It uses model_seq_search.m to simulate the dataset under a “true” search model parameter vector, and then saves the dataset to data.mat.

nne_gen.m#

This script generates the training and validation examples.

  • It loads the product attributes (z and pos) in data.mat.

  • It uses model_seq_search.m to simulate the consumer choices in each training or validation example.

  • It uses moments.m to summarize data in each training or validation example.

  • The training and validation examples are saved to nne_training.mat.

nne_train.m#

This script trains a shallow neural net.

  • It loads the training and validation examples from nne_training.mat (saved by nne_gen.m).

  • It uses normalRegressionLayer.m for the cross-entropy loss.

  • Validation loss is reported. We can use this loss to choose neural net hyperparameters (e.g., the number of hidden nodes).

  • It applies the trained neural net to data.mat.