Consumer search

This application uses full-information NNE to estimate a sequential search model with unobserved consumer heterogeneity. The Matlab code is available in this GitHub repository.

More details of this application can be found in our paper referenced on the Full-information NNE page.

Note

Scaffold page. This page mirrors the structure of the NNE consumer search code documentation. Fill in the example workflow and the file-by-file descriptions below with the contents of the search-model repository (replace the placeholder GitHub link above with the real one).

An example to use the code

Run the scripts in order. This example is a Monte Carlo experiment that uses full-information NNE to estimate the search model parameters from a simulated dataset.

>> monte_carlo_data        % simulate a dataset
>> fnne_gen                % generate the training examples (simulate datasets across theta draws)
>> fnne_train              % train the CNN and apply it to the data

Description of each file

model_seq_search.m

Describe the sequential search model with unobserved consumer heterogeneity (inputs, outputs).

fnne_gen.m

Describe how training examples \(\{\boldsymbol{\theta}^{(\ell)}, \mathcal{D}^{(\ell)}\}\) are generated.

fnne_train.m

Describe how the two-part CNN architecture is trained and applied to the data.