# 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. 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. 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. 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.