pyblp.Products¶
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class
pyblp.Products¶ Product data structured as a record array.
Attributes in addition to the ones below are the variables underlying \(X_1\), \(X_2\), and \(X_3\).
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market_ids¶ IDs that associate products with markets.
- Type
ndarray
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firm_ids¶ IDs that associate products with firms.
- Type
ndarray
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demand_ids¶ IDs used to create demand-side fixed effects.
- Type
ndarray
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supply_ids¶ IDs used to create supply-side fixed effects.
- Type
ndarray
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nesting_ids¶ IDs that associate products with nesting groups.
- Type
ndarray
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product_ids¶ IDs that identify products within markets.
- Type
ndarray
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clustering_ids¶ IDs used to compute clustered standard errors.
- Type
ndarray
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ownership¶ Stacked \(J_t \times J_t\) ownership or product holding matrices, \(\mathscr{H}\), for each market \(t\).
- Type
ndarray
Market shares, \(s\).
- Type
ndarray
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prices¶ Product prices, \(p\).
- Type
ndarray
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ZD¶ Full set of demand-side instruments, \(Z_D\), which typically consists of excluded demand-side instruments and \(X_1^\text{ex}\). If there are any demand-side fixed effects, these instruments will be residualized with respect to these fixed effects.
- Type
ndarray
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ZS¶ Full set of supply-side instruments, \(Z_S\), which typically consists of excluded supply-side instruments and \(X_3^\text{ex}\). If there are any supply-side fixed effects, these instruments will be residualized with respect to these fixed effects.
- Type
ndarray
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ZC¶ Covariance instruments, \(Z_C\), as in MacKay and Miller (2023).
- Type
ndarray
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X1¶ Demand-side linear product characteristics, \(X_1\). If there are any demand-side fixed effects, these characteristics will be residualized with respect to these fixed effects.
- Type
ndarray
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X2¶ Demand-side nonlinear product characteristics, \(X_2\).
- Type
ndarray
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X3¶ Supply-side product characteristics, \(X_3\). If there are any supply-side fixed effects, these characteristics will be residualized with respect to these fixed effects.
- Type
ndarray
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