HARK.simulation

Functions for generating simulated data and shocks.

Functions

drawBernoulli(N[, p, seed]) Generates arrays of booleans drawn from a simple Bernoulli distribution.
drawDiscrete(N[, P, X, exact_match, seed]) Simulates N draws from a discrete distribution with probabilities P and outcomes X.
drawLognormal(N[, mu, sigma, seed]) Generate arrays of mean one lognormal draws.
drawMeanOneLognormal(N[, sigma, seed]) Generate arrays of mean one lognormal draws.
drawNormal(N[, mu, sigma, seed]) Generate arrays of normal draws.
drawUniform(N[, bot, top, seed]) Generate arrays of uniform draws.
drawWeibull(N[, scale, shape, seed]) Generate arrays of Weibull draws.
main()
HARK.simulation.drawBernoulli(N, p=0.5, seed=0)

Generates arrays of booleans drawn from a simple Bernoulli distribution. The input p can be a float or a list-like of floats; its length T determines the number of entries in the output. The t-th entry of the output is an array of N booleans which are True with probability p[t] and False otherwise.

Returns:
draws : np.array or [np.array]

T-length list of arrays of Bernoulli draws each of size N, or a single array of size N (if sigma is a scalar).

HARK.simulation.drawDiscrete(N, P=[1.0], X=[0.0], exact_match=False, seed=0)

Simulates N draws from a discrete distribution with probabilities P and outcomes X.

Parameters:
P : np.array

A list of probabilities of outcomes.

X : np.array

A list of discrete outcomes.

N : int

Number of draws to simulate.

exact_match : boolean

Whether the draws should “exactly” match the discrete distribution (as closely as possible given finite draws). When True, returned draws are a random permutation of the N-length list that best fits the discrete distribution. When False (default), each draw is independent from the others and the result could deviate from the input.

seed : int

Seed for random number generator.

Returns:
draws : np.array

An array draws from the discrete distribution; each element is a value in X.

HARK.simulation.drawLognormal(N, mu=0.0, sigma=1.0, seed=0)

Generate arrays of mean one lognormal draws. The sigma input can be a number or list-like. If a number, output is a length N array of draws from the lognormal distribution with standard deviation sigma. If a list, output is a length T list whose t-th entry is a length N array of draws from the lognormal with standard deviation sigma[t].

Parameters:
N : int

Number of draws in each row.

mu : float or [float]

One or more means. Number of elements T in mu determines number of rows of output.

sigma : float or [float]

One or more standard deviations. Number of elements T in sigma determines number of rows of output.

seed : int

Seed for random number generator.

HARK.simulation.drawMeanOneLognormal(N, sigma=1.0, seed=0)

Generate arrays of mean one lognormal draws. The sigma input can be a number or list-like. If a number, output is a length N array of draws from the lognormal distribution with standard deviation sigma. If a list, output is a length T list whose t-th entry is a length N array of draws from the lognormal with standard deviation sigma[t].

Parameters:
N : int

Number of draws in each row.

sigma : float or [float]

One or more standard deviations. Number of elements T in sigma determines number of rows of output.

seed : int

Seed for random number generator.

HARK.simulation.drawNormal(N, mu=0.0, sigma=1.0, seed=0)

Generate arrays of normal draws. The mu and sigma inputs can be numbers or list-likes. If a number, output is a length N array of draws from the normal distribution with mean mu and standard deviation sigma. If a list, output is a length T list whose t-th entry is a length N array with draws from the normal distribution with mean mu[t] and standard deviation sigma[t].

Parameters:
N : int

Number of draws in each row.

mu : float or [float]

One or more means. Number of elements T in mu determines number of rows of output.

sigma : float or [float]

One or more standard deviations. Number of elements T in sigma determines number of rows of output.

seed : int

Seed for random number generator.

Returns:
draws : np.array or [np.array]

T-length list of arrays of normal draws each of size N, or a single array of size N (if sigma is a scalar).

HARK.simulation.drawUniform(N, bot=0.0, top=1.0, seed=0)

Generate arrays of uniform draws. The bot and top inputs can be numbers or list-likes. If a number, output is a length N array of draws from the uniform distribution on [bot,top]. If a list, output is a length T list whose t-th entry is a length N array with draws from the uniform distribution on [bot[t],top[t]].

Parameters:
N : int

Number of draws in each row.

bot : float or [float]

One or more bottom values. Number of elements T in mu determines number of rows of output.

top : float or [float]

One or more top values. Number of elements T in top determines number of rows of output.

seed : int

Seed for random number generator.

Returns:
draws : np.array or [np.array]

T-length list of arrays of uniform draws each of size N, or a single array of size N (if sigma is a scalar).

HARK.simulation.drawWeibull(N, scale=1.0, shape=1.0, seed=0)

Generate arrays of Weibull draws. The scale and shape inputs can be numbers or list-likes. If a number, output is a length N array of draws from the Weibull distribution with the given scale and shape. If a list, output is a length T list whose t-th entry is a length N array with draws from the Weibull distribution with scale scale[t] and shape shape[t].

Note: When shape=1, the Weibull distribution is simply the exponential dist.

Mean: scale*Gamma(1 + 1/shape)

Parameters:
N : int

Number of draws in each row.

scale : float or [float]

One or more scales. Number of elements T in scale determines number of rows of output.

shape : float or [float]

One or more shape parameters. Number of elements T in scale determines number of rows of output.

seed : int

Seed for random number generator.