152 lines
4.8 KiB
Python
152 lines
4.8 KiB
Python
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"""Generate a random process with panel structure
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Created on Sat Dec 17 22:15:27 2011
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Author: Josef Perktold
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Notes
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-----
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* written with unbalanced panels in mind, but not flexible enough yet
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* need more shortcuts and options for balanced panel
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* need to add random intercept or coefficients
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* only one-way (repeated measures) so far
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"""
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import numpy as np
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from . import correlation_structures as cs
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class PanelSample:
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'''data generating process for panel with within correlation
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allows various within correlation structures, but no random intercept yet
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Parameters
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----------
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nobs : int
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total number of observations
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k_vars : int
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number of explanatory variables to create in exog, including constant
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n_groups int
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number of groups in balanced sample
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exog : None or ndarray
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default is None, in which case a exog is created
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within : bool
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If True (default), then the exog vary within a group. If False, then
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only variation across groups is used.
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TODO: this option needs more work
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corr_structure : ndarray or ??
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Default is np.eye.
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corr_args : tuple
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arguments for the corr_structure
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scale : float
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scale of noise, standard deviation of normal distribution
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seed : None or int
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If seed is given, then this is used to create the random numbers for
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the sample.
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Notes
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-----
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The behavior for panel robust covariance estimators seems to differ by
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a large amount by whether exog have mostly within group or across group
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variation. I do not understand why this should be the case from the theory,
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and this would warrant more investigation.
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This is just used in one example so far and needs more usage to see what
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will be useful to add.
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'''
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def __init__(self, nobs, k_vars, n_groups, exog=None, within=True,
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corr_structure=np.eye, corr_args=(), scale=1, seed=None):
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nobs_i = nobs//n_groups
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nobs = nobs_i * n_groups #make balanced
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self.nobs = nobs
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self.nobs_i = nobs_i
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self.n_groups = n_groups
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self.k_vars = k_vars
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self.corr_structure = corr_structure
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self.groups = np.repeat(np.arange(n_groups), nobs_i)
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self.group_indices = np.arange(n_groups+1) * nobs_i #check +1
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if exog is None:
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if within:
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#t = np.tile(np.linspace(-1,1,nobs_i), n_groups)
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t = np.tile(np.linspace(0, 2, nobs_i), n_groups)
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#rs2 = np.random.RandomState(9876)
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#t = 1 + 0.3 * rs2.randn(nobs_i * n_groups)
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#mix within and across variation
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#t += np.repeat(np.linspace(-1,1,nobs_i), n_groups)
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else:
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#no within group variation,
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t = np.repeat(np.linspace(-1,1,nobs_i), n_groups)
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exog = t[:,None]**np.arange(k_vars)
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self.exog = exog
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#self.y_true = exog.sum(1) #all coefficients equal 1,
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#moved to make random coefficients
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#initialize
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self.y_true = None
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self.beta = None
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if seed is None:
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seed = np.random.randint(0, 999999)
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self.seed = seed
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self.random_state = np.random.RandomState(seed)
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#this makes overwriting difficult, move to method?
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self.std = scale * np.ones(nobs_i)
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corr = self.corr_structure(nobs_i, *corr_args)
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self.cov = cs.corr2cov(corr, self.std)
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self.group_means = np.zeros(n_groups)
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def get_y_true(self):
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if self.beta is None:
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self.y_true = self.exog.sum(1)
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else:
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self.y_true = np.dot(self.exog, self.beta)
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def generate_panel(self):
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'''
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generate endog for a random panel dataset with within correlation
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'''
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random = self.random_state
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if self.y_true is None:
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self.get_y_true()
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nobs_i = self.nobs_i
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n_groups = self.n_groups
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use_balanced = True
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if use_balanced: #much faster for balanced case
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noise = self.random_state.multivariate_normal(np.zeros(nobs_i),
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self.cov,
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size=n_groups).ravel()
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#need to add self.group_means
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noise += np.repeat(self.group_means, nobs_i)
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else:
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noise = np.empty(self.nobs, np.float64)
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noise.fill(np.nan)
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for ii in range(self.n_groups):
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#print ii,
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idx, idxupp = self.group_indices[ii:ii+2]
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#print idx, idxupp
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mean_i = self.group_means[ii]
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noise[idx:idxupp] = self.random_state.multivariate_normal(
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mean_i * np.ones(self.nobs_i), self.cov)
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endog = self.y_true + noise
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return endog
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