# lae¶

Computes a sequence of marginal densities for a continuous state space Markov chain $$X_t$$ where the transition probabilities can be represented as densities. The estimate of the marginal density of $$X_t$$ is

$\frac{1}{n} \sum_{i=0}^n p(X_{t-1}^i, y)$

This is a density in $$y$$.

## References¶

https://lectures.quantecon.org/py/stationary_densities.html

class quantecon.lae.LAE(p, X)[source]

Bases: object

An instance is a representation of a look ahead estimator associated with a given stochastic kernel p and a vector of observations X.

Parameters: p : function The stochastic kernel. A function p(x, y) that is vectorized in both x and y X : array_like(float) A vector containing observations

Examples

>>> psi = LAE(p, X)
>>> y = np.linspace(0, 1, 100)
>>> psi(y)  # Evaluate look ahead estimate at grid of points y

Attributes: p, X : see Parameters

Methods

 __call__(y) A vectorized function that returns the value of the look ahead estimate at the values in the array y.