Locations of example data that are included in the package for convenience.
Location of a CSV file containing the fake cereal product data from Nevo (2000). The file includes the same pre-computed excluded instruments used in the original paper.
Location of a CSV file containing the fake cereal agent data. Included in the file are Monte Carlo weights and draws along with demographics, which are used by Nevo (2000) to solve the fake cereal problem.
Location of a CSV file containing the automobile product data extracted by Andrews, Gentzkow, and Shapiro (2017) from the original GAUSS code for Berry, Levinsohn, and Pakes (1999), which is commonly assumed to be the same data used in Berry, Levinsohn, and Pakes (1995).
The file also includes a set of excluded instruments. First, “sums of characteristics” BLP instruments from the original paper were computed with
build_blp_instruments(). As in the original paper, the
mpdvariable was added to the set of excluded instruments for supply. Due to a collinearity problem with these original instruments, each set of excluded instruments was then interacted up to the second degree, standardized, replaced with the minimum set of principal components that explained at least 99% of the variance, and standardized again.
Location of a CSV file containing automobile agent data. Included in the file are 200 Monte Carlo weights and draws for each market, which, unlike in the fake cereal data, are not the same draws used in the original paper.
Also included is an income demographic, which consists of draws from lognormal distributions. The log of income has a common standard deviation,
1.72, and the following market-varying means: