# pyblp.build_differentiation_instruments¶

pyblp.build_differentiation_instruments(formulation, product_data, version='local', interact=False)

Construct excluded differentiation instruments.

Differentiation instruments in the spirit of Gandhi and Houde (2017) are

(1)$Z^\text{Diff}(X) = [Z^\text{Diff,Other}(X), Z^\text{Diff,Rival}(X)],$

in which $$X$$ is a matrix of product characteristics, $$Z^\text{Diff,Other}(X)$$ is a second matrix that consists of sums over functions of differences between non-rival goods, and $$Z^\text{Diff,Rival}(X)$$ is a third matrix that consists of sums over rival goods. Without optional interaction terms, all three matrices have the same dimensions.

Note

To construct simpler, firm-agnostic instruments that are sums over functions of differences between all different goods, specify a constant column of firm IDs and keep only the first half of the instrument columns.

Let $$x_{jt\ell}$$ be characteristic $$\ell$$ in $$X$$ for product $$j$$ in market $$t$$, which is produced by firm $$f$$. That is, $$j \in J_{ft}$$. Then in the “local” version of $$Z^\text{Diff}(X)$$,

(2)$\begin{split}Z_{jt\ell}^\text{Local,Other}(X) = \sum_{k \in J_{ft} \setminus \{j\}} 1(|d_{jkt\ell}| < \text{SD}_\ell), \\ Z_{jt\ell}^\text{Local,Rival}(X) = \sum_{k \notin J_{ft}} 1(|d_{jkt\ell}| < \text{SD}_\ell),\end{split}$

where $$d_{jkt\ell} = x_{kt\ell} - x_{jt\ell}$$ is the difference between products $$j$$ and $$k$$ in terms of characteristic $$\ell$$, $$\text{SD}_\ell$$ is the standard deviation of these pairwise differences computed across all markets, and $$1(|d_{jkt\ell}| < \text{SD}_\ell)$$ indicates that products $$j$$ and $$k$$ are close to each other in terms of characteristic $$\ell$$.

The intuition behind this “local” version is that demand for products is often most influenced by a small number of other goods that are very similar. For the “quadratic” version of $$Z^\text{Diff}(X)$$, which uses a more continuous measure of the distance between goods,

(3)$\begin{split}Z_{jtk}^\text{Quad,Other}(X) = \sum_{k \in J_{ft} \setminus\{j\}} d_{jkt\ell}^2, \\ Z_{jtk}^\text{Quad,Rival}(X) = \sum_{k \notin J_{ft}} d_{jkt\ell}^2.\end{split}$

With interaction terms, which reflect covariances between different characteristics, the summands for the “local” versions are $$1(|d_{jkt\ell}| < \text{SD}_\ell) \times d_{jkt\ell'}$$ for all characteristics $$\ell'$$, and the summands for the “quadratic” versions are $$d_{jkt\ell} \times d_{jkt\ell'}$$ for all $$\ell' \geq \ell$$.

Note

Usually, any supply or demand shifters are added to these excluded instruments, depending on whether they are meant to be used for demand- or supply-side estimation.

Parameters
• formulation (Formulation) – Formulation configuration for $$X$$, the matrix of product characteristics used to build excluded instruments. Variable names should correspond to fields in product_data.

• product_data (structured array-like) –

Each row corresponds to a product. Markets can have differing numbers of products. The following fields are required:

• market_ids : (object) - IDs that associate products with markets.

• firm_ids : (object) - IDs that associate products with firms.

Along with market_ids and firm_ids, the names of any additional fields can be used as variables in formulation.

• version (str, optional) –

The version of differentiation instruments to construct:

• 'local' (default) - Construct the instruments in (2) that consider only the characteristics of “close” products in each market.

• 'quadratic' - Construct the more continuous instruments in (3) that consider all products in each market.

• interact (bool, optional) – Whether to include interaction terms between different product characteristics, which can help capture covariances between product characteristics.

Returns

Excluded differentiation instruments, $$Z^\text{Diff}(X)$$.

Return type

ndarray

Examples