pyblp.Integration¶

class
pyblp.
Integration
(specification, size, seed=None)¶ Configuration for building integration nodes and weights.
 Parameters
specification (str) –
How to build nodes and weights. One of the following:
'monte_carlo'
 Draw from a pseudorandom standard multivariate normal distribution. Integration weights are1 / size
.'product'
 Generate nodes and weights according to the levelsize
GaussHermite product rule.'nested_product'
 Generate nodes and weights according to the levelsize
nested GaussHermite product rule. Weights can be negative.'grid'
 Generate a sparse grid of nodes and weights according to the levelsize
GaussHermite quadrature rule. Weights can be negative.'nested_grid'
 Generate a sparse grid of nodes and weights according to the levelsize
nested GaussHermite quadrature rule. Weights can be negative.
Best practice for low dimensions is probably to use
'product'
to a relatively high degree of polynomial accuracy. In higher dimensions,'grid'
appears to scale the best. For more information, see Judd and Skrainka (2011) and Conlon and Gortmaker (2019).Sparse grids are constructed in analogously to the Matlab function nwspgr created by Florian Heiss and Viktor Winschel. For more information, see Heiss and Winschel (2008).
size (int) – The number of draws if
specification
is'monte_carlo'
, and the level of the quadrature rule otherwise.seed (int, optional) – Passed to
numpy.random.RandomState
whenspecification
is'monte_carlo'
to seed the random number generator before building nodes. By default, a seed is not passed to the random number generator.
Examples