def genMarkov(markovFilePath, verb='INFO', nSimulation=int(5.e+4)):
logger.setStreamVerb(verb=verb)
logger.info('')
#os.system('cat ' + markovFilePath)
logger.debug('markovFilePath is %s' % markovFilePath)
# Read paramter file
beta = float(getParameter(markovFilePath, 'beta', 'bar-separated'))
muG = float(getParameter(markovFilePath, 'muG', 'bar-separated'))
sigmaG = float(getParameter(markovFilePath, 'sigmaG', 'bar-separated'))
p = float(getParameter(markovFilePath, 'p', 'bar-separated'))
dy = float(getParameter(markovFilePath, 'dy', 'bar-separated'))
nAgent = int(getParameter(markovFilePath, 'nAgent', 'bar-separated'))
theta = float(getParameter(markovFilePath, 'theta', 'bar-separated'))
periodsPerYear = int(
getParameter(markovFilePath, 'periodsPerYear', 'bar-separated'))
# Output read parameters
logger.debug('')
logger.debug('read the following yearly parameters: ')
logger.debug('beta = %s' % beta)
logger.debug('muG = %s' % muG)
logger.debug('sigmaG = %s' % sigmaG)
logger.debug('p = %s' % p)
logger.debug('dy = %s' % dy)
logger.debug('nAgent = %s' % nAgent)
logger.debug('theta = %s' % theta)
logger.debug('periodsPerYear = %s' % periodsPerYear)
# perform consitency check on paramters:
assert periodsPerYear >= 1
assert beta >= 0.8 and beta <= 1.0
assert sigmaG >= 0.
assert muG <= 1.0
# computing scaled parameters
muG_sc = (1. + muG)**(1. / periodsPerYear)
sigmaG_sc = sigmaG / np.sqrt(periodsPerYear)
beta_sc = beta**(1. / periodsPerYear)
# scaled parameters:
logger.info('')
logger.info('computed the following scaled 1/%s parameters: ' %
periodsPerYear)
logger.info('beta_sc = %s' % beta_sc)
logger.info('muG_sc = %s' % (muG_sc - 1.))
logger.info('sigmaG_sc = %s' % sigmaG_sc)
logger.info('')
# Build trans matrix
TransMatrix = np.array([[p, 1. - p], [1. - p, p]])
ShockMatrix = np.array([[muG_sc - sigmaG_sc], [muG_sc + sigmaG_sc]])
mkov = MkovM(ShockMatrix, TransMatrix)
logger.debug(ShockMatrix)
logger.debug(TransMatrix)
#mkov.simulation()
logger.debug(mkov)
if nAgent > 1:
# Add unemployment shocks
#TransMatrixUnem = np.triu(np.ones( (nAgent, nAgent)), 1 ) * ( ( 1. - p) / ( nAgent - 1.) ) + np.tril(np.ones( (nAgent, nAgent)), -1 ) * ( ( 1. - p) / ( nAgent - 1.) ) + np.eye(nAgent) * p
ShockMatrixUnem = np.triu(np.ones(
(nAgent, nAgent)
), 1) * dy / (nAgent - 1) + np.tril(np.ones(
(nAgent, nAgent)), -1) * dy / (nAgent - 1) + np.eye(nAgent) * (-dy)
# Print unemployment shock matrix
logger.info('ShockMatrixUnem \n %s' % ShockMatrixUnem)
# Combine aggregate uncertainty with idiosyncratic unemployment uncertainty
fullShockMatrix = np.hstack(
(np.repeat(ShockMatrix, nAgent,
axis=0), np.tile(ShockMatrixUnem, (2, 1))))
#fullTransMatrix = np.kron(TransMatrix, TransMatrixUnem)
# ugly ugly ugly
fullShockMatrix = fullShockMatrix[:nAgent + 1]
fullShockMatrix[nAgent][ShockMatrix.shape[1]:] = np.zeros((1, nAgent))
# more ugly ugly ugly
pers = TransMatrix[0, 0]
fullTransMatrix = np.vstack(
(np.hstack((np.eye(nAgent) * pers, np.ones(
(nAgent, 1)) * (1. - pers))),
np.append(((1. - pers) / nAgent) * np.ones(nAgent), pers)))
else:
fullShockMatrix = ShockMatrix
fullTransMatrix = TransMatrix
np.set_printoptions(precision=7, linewidth=300)
logger.info('fullShock\n %s' % fullShockMatrix)
logger.info('fullTrans\n %s' % fullTransMatrix)
###################
# Compute PD
# nD_t+1 = nYbar_t+1 - n(1-theta)E_tYbar_t+1
# divide through with Ybar_t+1
# nd_t+1 = n - n(1-theta) E_t[g_t+1]/ g_t+1
g = fullShockMatrix[:, 0]
Etg = np.dot(fullTransMatrix, g)
PD = 1. - (1. - theta) * np.reshape(Etg, (len(Etg), 1)) / g
PB = np.reshape(Etg, (len(Etg), 1)) / g
##################
try:
np.savetxt(os.path.join(os.getcwd(), 'output', 'shockMatrix.in'),
fullShockMatrix,
fmt='%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'transMatrix.in'),
fullTransMatrix,
fmt='%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'p_a.in'),
PD,
fmt='%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'p_b.in'),
PB,
fmt='%15.10f')
except IOError:
logger.critical('cannot write to output/.')
logger.info('')
#Estimate moments for full markov chain.
fullMChain = MkovM(fullShockMatrix, fullTransMatrix)
varSim, Theta = fullMChain.simulation(nSimulation)
# rename columns
varSim = varSim.rename(columns={'0': 'agIncGrowth'})
for agent in range(1, nAgent):
varSim = varSim.rename(columns={str(agent): 'dy_agent' + str(agent)})
qPers = Theta[1, 1]
yearlyStockSeries = pandas.DataFrame(varSim[::4])
model = scikits.statsmodels.tsa.api.VAR(yearlyStockSeries)
results = model.fit(1)
Theta = results.params[1:, :]
aPers = Theta[1, 1]
logger.info('quarterly pers %s, annual pers %s, diagnol pers %s' %
(qPers, aPers, TransMatrix[0, 0]))
np.set_string_function(None)
varSim['simInc'] = float(nAgent)
varSim['incGrIndAg0'] = float(1.)
varSim['incShareAg0'] = (1. + varSim['dy_agent1']) / nAgent
for row in varSim.rows():
if row > 0:
varSim['simInc'][row] = varSim['simInc'][
row - 1] * varSim['agIncGrowth'][row]
varSim['incGrIndAg0'][row] = (
varSim['incShareAg0'][row] /
varSim['incShareAg0'][row - 1]) * varSim['agIncGrowth'][row]
varSim['simIndIncAg0'] = varSim['incShareAg0'] * varSim['simInc']
x = varSim.ix[:, ['0', 'incShareAg0', 'incGrIndAg0']]
logger.info(x[:100])
## logger.info('VAR for growth, incshareAg0, incGrIndAg0')
## model = scikits.statsmodels.tsa.api.VAR(x)
## result = model.fit(1)
## logger.info(result.summary())
logger.info('incShareAg0')
incShareAg0 = x['incShareAg0']
logger.info(incShareAg0.describe())
logger.info('skewness %s' % scipy.stats.kurtosis(incShareAg0))
logger.info('kurtosis %s' % scipy.stats.skew(incShareAg0))
logger.info('\nincGrIndAg0')
incGrIndAg0 = x['incGrIndAg0']
logger.info(incGrIndAg0.describe())
logger.info('skewness %s' % scipy.stats.skew(incGrIndAg0))
logger.info('kurtosis %s' % (scipy.stats.kurtosis(incGrIndAg0)))
#scikits.statsmodels.tsa.ar_model.AR(np.asarray(varSim['simIndIncAg0']))
#varSim = varSim[:100]
#N = len(varSim)
#rng = pandas.DateRange('1/1/1900', periods = N, timeRule = 'Q@JAN')
#ts = pandas.Series(varSim['1'], index = rng)
return fullShockMatrix, fullTransMatrix, beta_sc, g, Etg, PD, PB, incGrIndAg0
def lucasOneAgent(shockMatrix, transMatrix, beta, g, Etg, PD, PB, markovFilePath, deterministic = False):
'''
markovFilePath: path to parameters.in file
determistic: boolean indicating whether to compute special determistic or stochastic case.
One agent economy. Therefore we have:
C_t = Y_t
C_{t+1} = Y_{t+1}
In the normalied world:
c_t = 1
c_{t+1} = 1
'''
logFile = os.path.join(os.getcwd(), 'output/logs/lucasOneAgent.log')
lucasLogger = wrapLogger(loggerName = 'lucasOneAgentLog', streamVerb = verb, logFile = logFile)
gamma = float(getParameter(markovFilePath, 'gamma', 'bar-separated'))
psi = float(getParameter(markovFilePath, 'psi', 'bar-separated'))
cBar = float(getParameter(markovFilePath, 'cBar', 'bar-separated'))
mkov = MkovM(shockMatrix, transMatrix)
lucasLogger.debug('\n')
lucasLogger.info('log file written to %s\n' % logFile)
#np.set_printoptions(precision = 7, linewidth = 300)
if deterministic:
shockMatrix = np.array([[g]])
transMatrix = np.array([[1]])
states = len(shockMatrix)
c = np.ones(states)
rho = 1. / psi
gammaSeq = [ gamma ] * len(PD)
if psi < 0: # CRRA case
lucasLogger.debug('Using CRRA Euler Equations')
# infinitely lived stock (lucas tree)
qS_init = np.ones(states)
def compute_qS(qS):
euler_qS = eulerCRRA(g, beta, gamma, transMatrix, PD + qS) - qS
return euler_qS
qS = scipy.optimize.newton_krylov(compute_qS, qS_init)
# Price remaining assets
for state in range(len(shockMatrix)):
qD = eulerCRRA(g, beta, gamma, transMatrix, payoff = PD)
qB = eulerCRRA(g, beta, gamma, transMatrix, payoff = PB)
elif psi >= 0: # Epstein Zin case
lucasLogger.debug('Using Epstein Zin Euler Equations')
# guess initial value function
V_init = g
lucasLogger.debug('Initial V: %s ' % V_init)
V = V_init
lucasLogger.info('starting backward recursion: ')
dif = 1.
counter = 0
while dif > 1.e-10:
newV = EpsteinZin(c - cBar, g, V, beta, gamma, psi, transMatrix)
dif = np.sum(np.abs(newV - V))
V = newV.copy()
lucasLogger.debug('iteration: %d dif: %g V: %s' % ( counter, dif, V))
counter += 1
lucasLogger.info('converged after %d iterations: ' % counter)
lucasLogger.info('converged value function: %s' % V)
# infinitely lived stock
qS_init = np.ones(states)
def compute_qS(qS):
euler_qS = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PD + qS) - qS
return euler_qS
qS = scipy.optimize.newton_krylov(compute_qS, qS_init)
# Price remaining assets
for state in range(len(shockMatrix)):
qD = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PD)
qB = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PB)
# Generate output:
# stock
lucasLogger.info('')
lucasLogger.info('qS: %s' % qS)
EtPS = inner1d(transMatrix, ( PD + qS ) * g)
lucasLogger.info('EtPS: %s ' % EtPS)
EtRS = EtPS / qS
lucasLogger.info('EtRS: %s' % EtRS)
if not deterministic:
ERS = np.dot(mkov.getlmbda(), EtRS)
lucasLogger.info('ERS: %s' % ERS)
# bond
lucasLogger.info('')
lucasLogger.info('qB: %s' % qB)
# EtPB = np.dot(transMatrix, PB * g)
EtPB = inner1d(transMatrix, PB * g)
lucasLogger.info('EtPB: %s' % EtPB)
EtRB = EtPB / qB
lucasLogger.info('EtRB: %s' % EtRB)
if not deterministic:
ERB = np.dot(mkov.getlmbda(), EtRB)
lucasLogger.info('ERB: %s' % ERB)
# dividend asset
lucasLogger.info('')
lucasLogger.info('qD: %s' % qD)
EtPD = np.dot(transMatrix, PD * g)
lucasLogger.info('EtPD: %s ' % EtPD)
EtRD = EtPS / qD
lucasLogger.info('RtD: %s' % EtRD)
if not deterministic:
ERD = np.dot(mkov.getlmbda(), EtRD)
lucasLogger.info('ERD: %s' % ERD)
# risk premium
c_rp = EtRS - EtRB
lucasLogger.info('\ncond risk prem: %s' % c_rp)
if not deterministic:
rp = np.dot(mkov.getlmbda(), c_rp)
lucasLogger.info('unc risk premium: %s' % rp)
lucasLogger.debug('\ndone with riskpremium computation')
# Write prices to file
savePath = os.path.join(os.getcwd(), 'output')
lucasLogger.debug('writing qS/qB to path %slucasOneagent_qS|lucasOneagent_qS' % savePath)
np.savetxt(os.path.join(savePath, 'lucasOneAgent_qS.in'), qS, fmt = '%15.10f')
np.savetxt(os.path.join(savePath, 'lucasOneAgent_qB.in'), qB, fmt = '%15.10f')
if psi > 0:
np.savetxt(os.path.join(savePath, 'lucasOneAgent_V.in'), V, fmt = '%15.10f')
def lucasOneAgent(shockMatrix, transMatrix, beta, g, Etg, PD, PB, markovFilePath, deterministic = False):
'''
markovFilePath: path to parameters.in file
determistic: boolean indicating whether to compute special determistic or stochastic case.
One agent economy. Therefore we have:
C_t = Y_t
C_{t+1} = Y_{t+1}
In the normalied world:
c_t = 1
c_{t+1} = 1
'''
logFile = os.path.join(os.getcwd(), 'output/logs/lucasOneAgent.log')
lucasLogger = wrapLogger(loggerName = 'lucasOneAgentLog', streamVerb = verb, logFile = logFile)
gamma = float(getParameter(markovFilePath, 'gamma', 'bar-separated'))
psi = float(getParameter(markovFilePath, 'psi', 'bar-separated'))
cBar = float(getParameter(markovFilePath, 'cBar', 'bar-separated'))
mkov = MkovM(shockMatrix, transMatrix)
lucasLogger.debug('\n')
lucasLogger.info('log file written to %s\n' % logFile)
#np.set_printoptions(precision = 7, linewidth = 300)
if deterministic:
shockMatrix = np.array([[g]])
transMatrix = np.array([[1]])
states = len(shockMatrix)
c = np.ones(states)
rho = 1. / psi
gammaSeq = [ gamma ] * len(PD)
if psi < 0: # CRRA case
lucasLogger.debug('Using CRRA Euler Equations')
# infinitely lived stock (lucas tree)
qS_init = np.ones(states)
def compute_qS(qS):
euler_qS = eulerCRRA(g, beta, gamma, transMatrix, PD + qS) - qS
return euler_qS
qS = scipy.optimize.newton_krylov(compute_qS, qS_init)
# Price remaining assets
for state in range(len(shockMatrix)):
qD = eulerCRRA(g, beta, gamma, transMatrix, payoff = PD)
qB = eulerCRRA(g, beta, gamma, transMatrix, payoff = PB)
elif psi >= 0: # Epstein Zin case
lucasLogger.debug('Using Epstein Zin Euler Equations')
# guess initial value function
V_init = g
lucasLogger.debug('Initial V: %s ' % V_init)
V = V_init
lucasLogger.info('starting backward recursion: ')
dif = 1.
counter = 0
while dif > 1.e-10:
newV = EpsteinZin(c - cBar, g, V, beta, gamma, psi, transMatrix)
dif = np.sum(np.abs(newV - V))
V = newV.copy()
lucasLogger.debug('iteration: %d dif: %g V: %s' % ( counter, dif, V))
counter += 1
lucasLogger.info('converged after %d iterations: ' % counter)
lucasLogger.info('converged value function: %s' % V)
# infinitely lived stock
qS_init = np.ones(states)
def compute_qS(qS):
euler_qS = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PD + qS) - qS
return euler_qS
qS = scipy.optimize.newton_krylov(compute_qS, qS_init)
# Price remaining assets
for state in range(len(shockMatrix)):
qD = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PD)
qB = eulerEpsteinZin(g, V, beta, gamma, psi, transMatrix, payoff = PB)
# Generate output:
# stock
lucasLogger.info('')
lucasLogger.info('qS: %s' % qS)
EtPS = inner1d(transMatrix, ( PD + qS ) * g)
lucasLogger.info('EtPS: %s ' % EtPS)
EtRS = EtPS / qS
lucasLogger.info('EtRS: %s' % EtRS)
if not deterministic:
ERS = np.dot(mkov.getlmbda(), EtRS)
lucasLogger.info('ERS: %s' % ERS)
# bond
lucasLogger.info('')
lucasLogger.info('qB: %s' % qB)
# EtPB = np.dot(transMatrix, PB * g)
EtPB = inner1d(transMatrix, PB * g)
lucasLogger.info('EtPB: %s' % EtPB)
EtRB = EtPB / qB
lucasLogger.info('EtRB: %s' % EtRB)
if not deterministic:
ERB = np.dot(mkov.getlmbda(), EtRB)
lucasLogger.info('ERB: %s' % ERB)
# dividend asset
lucasLogger.info('')
lucasLogger.info('qD: %s' % qD)
EtPD = np.dot(transMatrix, PD * g)
lucasLogger.info('EtPD: %s ' % EtPD)
EtRD = EtPS / qD
lucasLogger.info('RtD: %s' % EtRD)
if not deterministic:
ERD = np.dot(mkov.getlmbda(), EtRD)
lucasLogger.info('ERD: %s' % ERD)
# risk premium
c_rp = EtRS - EtRB
lucasLogger.info('\ncond risk prem: %s' % c_rp)
if not deterministic:
rp = np.dot(mkov.getlmbda(), c_rp)
lucasLogger.info('unc risk premium: %s' % rp)
lucasLogger.debug('\ndone with riskpremium computation')
# Write prices to file
savePath = os.path.join(os.getcwd(), 'output')
lucasLogger.debug('writing qS/qB to path %slucasOneagent_qS|lucasOneagent_qS' % savePath)
np.savetxt(os.path.join(savePath, 'lucasOneAgent_qS.in'), qS, fmt = '%15.10f')
np.savetxt(os.path.join(savePath, 'lucasOneAgent_qB.in'), qB, fmt = '%15.10f')
if psi > 0:
np.savetxt(os.path.join(savePath, 'lucasOneAgent_V.in'), V, fmt = '%15.10f')
def genMarkov(markovFilePath, verb = 'INFO', nSimulation = int(5.e+4)):
logger.setStreamVerb(verb = verb)
logger.info('')
#os.system('cat ' + markovFilePath)
logger.debug('markovFilePath is %s' % markovFilePath)
# Read paramter file
beta = float(getParameter(markovFilePath, 'beta', 'bar-separated'))
muG = float(getParameter(markovFilePath, 'muG', 'bar-separated'))
sigmaG = float(getParameter(markovFilePath, 'sigmaG', 'bar-separated'))
p = float(getParameter(markovFilePath, 'p', 'bar-separated'))
dy = float(getParameter(markovFilePath, 'dy', 'bar-separated'))
nAgent = int(getParameter(markovFilePath, 'nAgent', 'bar-separated'))
theta = float(getParameter(markovFilePath, 'theta', 'bar-separated'))
periodsPerYear = int(getParameter(markovFilePath, 'periodsPerYear', 'bar-separated'))
# Output read parameters
logger.debug('')
logger.debug('read the following yearly parameters: ')
logger.debug('beta = %s' % beta)
logger.debug('muG = %s' % muG)
logger.debug('sigmaG = %s' % sigmaG)
logger.debug('p = %s' % p)
logger.debug('dy = %s' % dy)
logger.debug('nAgent = %s' % nAgent)
logger.debug('theta = %s' % theta)
logger.debug('periodsPerYear = %s' % periodsPerYear)
# perform consitency check on paramters:
assert periodsPerYear >= 1
assert beta >= 0.8 and beta <= 1.0
assert sigmaG >= 0.
assert muG <= 1.0
# computing scaled parameters
muG_sc = (1. + muG) ** (1./ periodsPerYear)
sigmaG_sc = sigmaG / np.sqrt(periodsPerYear)
beta_sc = beta ** (1./ periodsPerYear)
# scaled parameters:
logger.info('')
logger.info('computed the following scaled 1/%s parameters: ' % periodsPerYear)
logger.info('beta_sc = %s' % beta_sc)
logger.info('muG_sc = %s' % (muG_sc - 1.))
logger.info('sigmaG_sc = %s' % sigmaG_sc)
logger.info('')
# Build trans matrix
TransMatrix = np.array([[p, 1.-p], [1.-p, p]])
ShockMatrix = np.array([[ muG_sc - sigmaG_sc], [muG_sc + sigmaG_sc]])
mkov = MkovM(ShockMatrix, TransMatrix)
logger.debug(ShockMatrix)
logger.debug(TransMatrix)
#mkov.simulation()
logger.debug(mkov)
if nAgent > 1:
# Add unemployment shocks
#TransMatrixUnem = np.triu(np.ones( (nAgent, nAgent)), 1 ) * ( ( 1. - p) / ( nAgent - 1.) ) + np.tril(np.ones( (nAgent, nAgent)), -1 ) * ( ( 1. - p) / ( nAgent - 1.) ) + np.eye(nAgent) * p
ShockMatrixUnem = np.triu(np.ones( (nAgent, nAgent)), 1 ) * dy / ( nAgent - 1 ) + np.tril(np.ones( (nAgent, nAgent)), -1 ) * dy / (nAgent - 1 ) + np.eye(nAgent) * ( -dy )
# Print unemployment shock matrix
logger.info('ShockMatrixUnem \n %s' % ShockMatrixUnem)
# Combine aggregate uncertainty with idiosyncratic unemployment uncertainty
fullShockMatrix = np.hstack( ( np.repeat(ShockMatrix, nAgent, axis = 0), np.tile(ShockMatrixUnem, (2, 1))) )
#fullTransMatrix = np.kron(TransMatrix, TransMatrixUnem)
# ugly ugly ugly
fullShockMatrix = fullShockMatrix[:nAgent + 1]
fullShockMatrix[nAgent][ShockMatrix.shape[1]:] = np.zeros( (1, nAgent) )
# more ugly ugly ugly
pers = TransMatrix[0,0]
fullTransMatrix = np.vstack( (np.hstack( ( np.eye(nAgent) * pers, np.ones( (nAgent, 1) ) * (1.-pers) ) ), np.append(((1.-pers)/ nAgent) *np.ones(nAgent), pers) ) )
else:
fullShockMatrix = ShockMatrix
fullTransMatrix = TransMatrix
np.set_printoptions(precision = 7, linewidth = 300)
logger.info('fullShock\n %s' % fullShockMatrix )
logger.info('fullTrans\n %s' % fullTransMatrix )
###################
# Compute PD
# nD_t+1 = nYbar_t+1 - n(1-theta)E_tYbar_t+1
# divide through with Ybar_t+1
# nd_t+1 = n - n(1-theta) E_t[g_t+1]/ g_t+1
g = fullShockMatrix[:, 0]
Etg = np.dot(fullTransMatrix, g)
PD = 1. - ( 1. - theta ) * np.reshape(Etg, (len(Etg), 1)) / g
PB = np.reshape(Etg, (len(Etg), 1)) / g
##################
try:
np.savetxt(os.path.join(os.getcwd(), 'output', 'shockMatrix.in'), fullShockMatrix, fmt = '%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'transMatrix.in'), fullTransMatrix, fmt = '%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'p_a.in'), PD, fmt = '%15.10f')
np.savetxt(os.path.join(os.getcwd(), 'output', 'p_b.in'), PB, fmt = '%15.10f')
except IOError:
logger.critical('cannot write to output/.')
logger.info('')
#Estimate moments for full markov chain.
fullMChain = MkovM(fullShockMatrix, fullTransMatrix)
varSim, Theta = fullMChain.simulation(nSimulation)
# rename columns
varSim = varSim.rename(columns = {'0': 'agIncGrowth'})
for agent in range(1, nAgent):
varSim = varSim.rename(columns = {str(agent): 'dy_agent' + str(agent)})
qPers = Theta[1, 1]
yearlyStockSeries = pandas.DataFrame(varSim[::4])
model = scikits.statsmodels.tsa.api.VAR(yearlyStockSeries)
results = model.fit(1)
Theta = results.params[1:,:]
aPers = Theta[1, 1]
logger.info('quarterly pers %s, annual pers %s, diagnol pers %s' % (qPers, aPers, TransMatrix[0, 0]) )
np.set_string_function(None)
varSim['simInc'] = float(nAgent)
varSim['incGrIndAg0'] = float(1.)
varSim['incShareAg0'] = ( 1. + varSim['dy_agent1'] ) / nAgent
for row in varSim.rows():
if row > 0:
varSim['simInc'][row] = varSim['simInc'][row-1] * varSim['agIncGrowth'][row]
varSim['incGrIndAg0'][row] = ( varSim['incShareAg0'][row] / varSim['incShareAg0'][row - 1] ) * varSim['agIncGrowth'][row]
varSim['simIndIncAg0'] = varSim['incShareAg0'] * varSim['simInc']
x = varSim.ix[:,['0','incShareAg0', 'incGrIndAg0']]
logger.info(x[:100])
## logger.info('VAR for growth, incshareAg0, incGrIndAg0')
## model = scikits.statsmodels.tsa.api.VAR(x)
## result = model.fit(1)
## logger.info(result.summary())
logger.info('incShareAg0')
incShareAg0 = x['incShareAg0']
logger.info(incShareAg0.describe())
logger.info('skewness %s' % scipy.stats.kurtosis(incShareAg0))
logger.info('kurtosis %s' % scipy.stats.skew(incShareAg0))
logger.info('\nincGrIndAg0')
incGrIndAg0 = x['incGrIndAg0']
logger.info(incGrIndAg0.describe())
logger.info('skewness %s' % scipy.stats.skew(incGrIndAg0))
logger.info('kurtosis %s' % (scipy.stats.kurtosis(incGrIndAg0)))
#scikits.statsmodels.tsa.ar_model.AR(np.asarray(varSim['simIndIncAg0']))
#varSim = varSim[:100]
#N = len(varSim)
#rng = pandas.DateRange('1/1/1900', periods = N, timeRule = 'Q@JAN')
#ts = pandas.Series(varSim['1'], index = rng)
return fullShockMatrix, fullTransMatrix, beta_sc, g, Etg, PD, PB, incGrIndAg0
