# pyblp.Simulation¶

class pyblp.Simulation(product_formulations, product_data, beta, sigma=None, pi=None, gamma=None, rho=None, agent_formulation=None, agent_data=None, integration=None, xi=None, omega=None, xi_variance=1, omega_variance=1, correlation=0.9, distributions=None, epsilon_scale=1.0, costs_type='linear', seed=None)

Simulation of data in BLP-type models.

Any data left unspecified are simulated during initialization. Simulated prices and shares can be replaced by Simulation.replace_endogenous() with equilibrium values that are consistent with true parameters. Less commonly, simulated exogenous variables can be replaced instead by Simulation.replace_exogenous(). To choose your own prices, refer to the first note in Simulation.replace_endogenous(). Simulations are typically used for two purposes:

1. Solving for equilibrium prices and shares under more complicated counterfactuals than is possible with ProblemResults.compute_prices() and ProblemResults.compute_shares(). For example, this class can be initialized with estimated parameters, structural errors, and marginal costs from a ProblemResults(), but with changed data (fewer products, new products, different characteristics, etc.) and Simulation.replace_endogenous() can be used to compute the corresponding prices and shares.

2. Simulation of BLP-type models from scratch. For example, a model with fixed true parameters can be simulated many times, converted into problems with SimulationResults.to_problem(), and solved with Problem.solve() to evaluate in a Monte Carlo study how well the true parameters can be recovered.

If data for variables (used to formulate product characteristics in $$X_1$$, $$X_2$$, and $$X_3$$, as well as agent demographics, $$d$$, and endogenous prices and market shares $$p$$ and $$s$$) are not provided, the values for each unspecified variable are drawn independently from the standard uniform distribution. In each market $$t$$, market shares are divided by the number of products in the market $$J_t$$. Typically, Simulation.replace_endogenous() is used to replace prices and shares with equilibrium values that are consistent with true parameters.

If data for unobserved demand-and supply-side product characteristics, $$\xi$$ and $$\omega$$, are not provided, they are by default drawn from a mean-zero bivariate normal distribution.

After variables are loaded or simulated, any unspecified integration nodes and weights, $$\nu$$ and $$w$$, are constructed according to a specified Integration configuration.

Parameters
• product_formulations (Formulation or sequence of Formulation) –

Formulation configuration or a sequence of up to three Formulation configurations for the matrix of demand-side linear product characteristics, $$X_1$$, for the matrix of demand-side nonlinear product characteristics, $$X_2$$, and for the matrix of supply-side characteristics, $$X_3$$, respectively. If the formulation for $$X_2$$ is not specified or is None, the logit (or nested logit) model will be simulated.

The shares variable should not be included in the formulations for $$X_1$$ or $$X_2$$. If shares is included in the formulation for $$X_3$$, care should be taken when solving for equilibrium prices in, for example, Simulation.replace_endogenous(), since this routine assumes that marginal costs remain constant.

The prices variable should not be included in the formulation for $$X_3$$, but it should be included in the formulation for $$X_1$$ or $$X_2$$ (or both). Variables that cannot be loaded from product_data will be drawn from independent standard uniform distributions. Unlike in Problem, fixed effect absorption is not supported during simulation.

Warning

Characteristics that involve prices, $$p$$, or shares, $$s$$, should always be formulated with the prices and shares variables, respectively. If another name is used, Simulation will not understand that the characteristic is endogenous. For example, to include a $$p^2$$ characteristic, include I(prices**2) in a formula instead of manually constructing and including a prices_squared variable.

• product_data (structured array-like) –

Each row corresponds to a product. Markets can have differing numbers of products. The convenience function build_id_data() can be used to construct the following required ID data:

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

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

Custom ownership matrices can be specified as well:

• ownership : (numeric, optional) - Custom stacked $$J_t \times J_t$$ ownership or product holding matrices, $$\mathscr{H}$$, for each market $$t$$, which can be built with build_ownership(). By default, standard ownership matrices are built only when they are needed to reduce memory usage. If specified, there should be as many columns as there are products in the market with the most products. Rightmost columns in markets with fewer products will be ignored.

Note

The ownership field can either be a matrix or can be broken up into multiple one-dimensional fields with column index suffixes that start at zero. For example, if there are three products in each market, a ownership field with three columns can be replaced by three one-dimensional fields: ownership0, ownership1, and ownership2.

To use certain types of micro moments, product IDs must be specified:

To simulate a nested logit or random coefficients nested logit (RCNL) model, nesting groups must be specified:

• nesting_ids (object, optional) - IDs that associate products with nesting groups. When these IDs are specified, rho must be specified as well.

Along with market_ids, firm_ids, product_ids, and nesting_ids, the names of any additional fields can typically be used as variables in product_formulations. However, there are a few variable names such as 'X1', which are reserved for use by Products.

• beta (array-like) – Vector of demand-side linear parameters, $$\beta$$. Elements correspond to columns in $$X_1$$, which is formulated by product_formulations.

• sigma (array-like, optional) – Lower-triangular Cholesky root of the covariance matrix for unobserved taste heterogeneity, $$\Sigma$$. Rows and columns correspond to columns in $$X_2$$, which is formulated by product_formulations. If $$X_2$$ is not formulated, this should not be specified, since the logit model will be simulated.

• pi (array-like, optional) – Parameters that measure how agent tastes vary with demographics, $$\Pi$$. Rows correspond to the same product characteristics as in sigma. Columns correspond to columns in $$d$$, which is formulated by agent_formulation. If $$d$$ is not formulated, this should not be specified.

• gamma (array-like, optional) – Vector of supply-side linear parameters, $$\gamma$$. Elements correspond to columns in $$X_3$$, which is formulated by product_formulations. If $$X_3$$ is not formulated, this should not be specified.

• rho (array-like, optional) – Parameters that measure within nesting group correlation, $$\rho$$. If this is a scalar, it corresponds to all groups defined by the nesting_ids field of product_data. If this is a vector, it must have $$H$$ elements, one for each nesting group. Elements correspond to group IDs in the sorted order of Simulation.unique_nesting_ids. If nesting IDs are not specified, this should not be specified either.

• agent_formulation (Formulation, optional) – Formulation configuration for the matrix of observed agent characteristics called demographics, $$d$$, which will only be included in the model if this formulation is specified. Any variables that cannot be loaded from agent_data will be drawn from independent standard uniform distributions.

• agent_data (structured array-like, optional) –

Each row corresponds to an agent. Markets can have differing numbers of agents. Since simulated agents are only used if there are demand-side nonlinear product characteristics, agent data should only be specified if $$X_2$$ is formulated in product_formulations. If agent data are specified, market IDs are required:

• market_ids : (object, optional) - IDs that associate agents with markets. The set of distinct IDs should be the same as the set in product_data. If integration is specified, there must be at least as many rows in each market as the number of nodes and weights that are built for the market.

If integration is not specified, the following fields are required:

• weights : (numeric, optional) - Integration weights, $$w$$, for integration over agent choice probabilities.

• nodes : (numeric, optional) - Unobserved agent characteristics called integration nodes, $$\nu$$. If there are more than $$K_2$$ columns (the number of demand-side nonlinear product characteristics), only the first $$K_2$$ will be used. If any columns of sigma are fixed at zero, only the first few columns of these nodes will be used.

The convenience function build_integration() can be useful when constructing custom nodes and weights.

Note

If nodes has multiple columns, it can be specified as a matrix or broken up into multiple one-dimensional fields with column index suffixes that start at zero. For example, if there are three columns of nodes, a nodes field with three columns can be replaced by three one-dimensional fields: nodes0, nodes1, and nodes2.

Along with market_ids, the names of any additional fields can typically be used as variables in agent_formulation. The exception is the name 'demographics', which is reserved for use by Agents.

• integration (Integration, optional) –

Integration configuration for how to build nodes and weights for integration over agent choice probabilities, which will replace any nodes and weights fields in agent_data. This configuration is required if nodes and weights in agent_data are not specified. It should not be specified if $$X_2$$ is not formulated in product_formulations.

If this configuration is specified, $$K_2$$ columns of nodes (the number of demand-side nonlinear product characteristics) will be built. However, if sigma is left unspecified or is specified with columns fixed at zero, fewer columns will be used.

• xi (array-like, optional) – Unobserved demand-side product characteristics, $$\xi$$. By default, if $$X_3$$ is formulated, each pair of unobserved characteristics in this vector and $$\omega$$ is drawn from a mean-zero bivariate normal distribution. This must be specified if $$X_3$$ is not formulated or if omega is specified.

• omega (array-like, optional) – Unobserved supply-side product characteristics, $$\omega$$. By default, if $$X_3$$ is formulated, each pair of unobserved characteristics in this vector and $$\xi$$ is drawn from a mean-zero bivariate normal distribution. This must be specified if $$X_3$$ is formulated and xi is specified. It is ignored if $$X_3$$ is not formulated.

• xi_variance (float, optional) – Variance of $$\xi$$. The default value is 1.0. This is ignored if xi or omega is specified.

• omega_variance (float, optional) – Variance of $$\omega$$. The default value is 1.0. This is ignored if xi or omega is specified.

• correlation (float, optional) – Correlation between $$\xi$$ and $$\omega$$. The default value is 0.9. This is ignored if xi or omega is specified.

• distributions (sequence of str, optional) –

Random coefficient distributions. By default, random coefficients in (3) are assumed to be normally distributed. Non-default distributions can be specified with a list of the following supported strings:

• 'normal' (default) - The random coefficient is assumed to be normal.

• 'lognormal' - The random coefficient is assumed to be lognormal. The coefficient’s column in (3) is exponentiated before being pre-multiplied by $$X_2$$.

The list should have as many strings as there are columns in $$X_2$$. Each string determines the distribution of the random coefficient on the corresponding product characteristic in $$X_2$$.

A typical example of a lognormal coefficient is one on prices. Implementing this typically involves having a I(-prices) in the formulation for $$X_2$$, and instead of including prices in $$X_1$$, including a 1 in the agent_formulation. Then the corresponding coefficient in $$\Pi$$ will serve as the mean parameter for the lognormal random coefficient on negative prices, $$-p_{jt}$$.

• epsilon_scale (float, optional) –

Factor by which the Type I Extreme Value idiosyncratic preference term, $$\epsilon_{ijt}$$, is scaled. By default, $$\epsilon_{ijt}$$ is not scaled. The typical use of this parameter is to approximate the pure characteristics model of Berry and Pakes (2007) by choosing a value smaller than 1.0. As this scaling factor approaches zero, the model approaches the pure characteristics model in which there is no idiosyncratic preference term.

For more information about choosing this parameter and estimating models where it is smaller than 1.0, refer to the same argument in Problem.solve(). In some situations, it may be easier to solve simulations with small epsilon scaling factors by using Simulation.replace_exogenous() rather than Simulation.replace_endogenous().

• costs_type (str, optional) –

Specification of the marginal cost function $$\tilde{c} = f(c)$$ in (9). The following specifications are supported:

• 'linear' (default) - Linear specification: $$\tilde{c} = c$$.

• 'log' - Log-linear specification: $$\tilde{c} = \log c$$.

• seed (int, optional) – Passed to numpy.random.RandomState to seed the random number generator before data are simulated. By default, a seed is not passed to the random number generator.

product_formulations

Formulation configurations for $$X_1$$, $$X_2$$, and $$X_3$$, respectively.

Type

tuple

agent_formulation

Formulation configuration for $$d$$.

Type

tuple

product_data

Synthetic product data that were loaded or simulated during initialization. Typically, Simulation.replace_endogenous() is used replace prices and shares with equilibrium values that are consistent with true parameters. The data_to_dict() function can be used to convert this into a more usable data type.

Type

recarray

agent_data

Synthetic agent data that were loaded or simulated during initialization. The data_to_dict() function can be used to convert this into a more usable data type.

Type

recarray

integration

Integration configuration for how any nodes and weights were built during initialization.

Type

Integration

products

Product data structured as Products, which consists of data taken from Simulation.product_data along with matrices build according to Simulation.product_formulations. The data_to_dict() function can be used to convert this into a more usable data type.

Type

Products

agents

Agent data structured as Agents, which consists of data taken from Simulation.agent_data or built by Simulation.integration along with any demographics formulated by Simulation.agent_formulation. The data_to_dict() function can be used to convert this into a more usable data type.

Type

Agents

unique_market_ids

Unique market IDs in product and agent data.

Type

ndarray

unique_firm_ids

Unique firm IDs in product data.

Type

ndarray

unique_product_ids

Unique product IDs in product data.

Type

ndarray

unique_nesting_ids

Unique nesting IDs in product data.

Type

ndarray

beta

Demand-side linear parameters, $$\beta$$.

Type

ndarray

sigma

Cholesky root of the covariance matrix for unobserved taste heterogeneity, $$\Sigma$$.

Type

ndarray

gamma

Supply-side linear parameters, $$\gamma$$.

Type

ndarray

pi

Parameters that measures how agent tastes vary with demographics, $$\Pi$$.

Type

ndarray

rho

Parameters that measure within nesting group correlation, $$\rho$$.

Type

ndarray

xi

Unobserved demand-side product characteristics, $$\xi$$.

Type

ndarray

omega

Unobserved supply-side product characteristics, $$\omega$$.

Type

ndarray

distributions

Random coefficient distributions.

Type

list of str

epsilon_scale

Factor by which the Type I Extreme Value idiosyncratic preference term, $$\epsilon_{ijt}$$, is scaled.

Type

float

costs_type

Functional form of the marginal cost function $$\tilde{c} = f(c)$$.

Type

str

T

Number of markets, $$T$$.

Type

int

N

Number of products across all markets, $$N$$.

Type

int

F

Number of firms across all markets, $$F$$.

Type

int

I

Number of agents across all markets, $$I$$.

Type

int

K1

Number of demand-side linear product characteristics, $$K_1$$.

Type

int

K2

Number of demand-side nonlinear product characteristics, $$K_2$$.

Type

int

K3

Number of supply-side characteristics, $$K_3$$.

Type

int

D

Number of demographic variables, $$D$$.

Type

int

MD

Number of demand-side instruments, $$M_D$$, which is always zero because instruments are added or constructed in SimulationResults.to_problem().

Type

int

MS

Number of supply-side instruments, $$M_S$$, which is always zero because instruments are added or constructed in SimulationResults.to_problem().

Type

int

ED

Number of absorbed dimensions of demand-side fixed effects, $$E_D$$, which is always zero because simulations do not support fixed effect absorption.

Type

int

ES

Number of absorbed dimensions of supply-side fixed effects, $$E_S$$, which is always zero because simulations do not support fixed effect absorption.

Type

int

H

Number of nesting groups, $$H$$.

Type

int

Examples

Methods

 replace_endogenous([costs, prices, …]) Replace simulated prices and market shares with equilibrium values that are consistent with true parameters. replace_exogenous(X1_name[, X3_name, delta, …]) Replace exogenous product characteristics with values that are consistent with true parameters.