Thanks to everyone in advance for their time!
I am trying to run a TVP-VAR (time varying papramenter) for a panel in the statespace mlemodels in statsmodel. I get an error while trying to fit the model. My understanding is that mostly is regarding the dimensions for state covariance matrix. I suspect will get the same error later when I will deal with column shape.
The error is :
ValueError: Invalid dimensions for state covariance matrix matrix: requires 702 rows, got 3
How could I solve that ? The type error is showing is as below, highlighted in bold both the error and the Traceback :
preliminary = tvppanelvarmodel.fit(maxiter=1000)
Traceback (most recent call last):
File "/var/folders/m6/68zljfsj2t9_dzgpwwslj29r0000gp/T/ipykernel_6232/3038987883.py", line 1, in <module>
preliminary = tvppanelvarmodel.fit(maxiter=1000)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 704, in fit
mlefit = super(MLEModel, self).fit(start_params, method=method,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/base/model.py", line 563, in fit
xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/base/optimizer.py", line 241, in _fit
xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/base/optimizer.py", line 651, in _fit_lbfgs
retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/lbfgsb.py", line 197, in fmin_l_bfgs_b
res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/lbfgsb.py", line 306, in _minimize_lbfgsb
sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/optimize.py", line 261, in _prepare_scalar_function
sf = ScalarFunction(fun, x0, args, grad, hess,
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py", line 140, in __init__
self._update_fun()
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py", line 233, in _update_fun
self._update_fun_impl()
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py", line 137, in update_fun
self.f = fun_wrapped(self.x)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/scipy/optimize/_differentiable_functions.py", line 134, in fun_wrapped
return fun(np.copy(x), *args)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/base/model.py", line 531, in f
return -self.loglike(params, *args) / nobs
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 933, in loglike
self.update(params, transformed=True, includes_fixed=True,
File "/var/folders/m6/68zljfsj2t9_dzgpwwslj29r0000gp/T/ipykernel_6232/3786466608.py", line 104, in update
self['state_cov'] = np.diag([params[2]**2, params[3]**2, params[4]**2]) # W
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 239, in __setitem__
return self.ssm.__setitem__(key, value)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/representation.py", line 420, in __setitem__
setattr(self, key, value)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/representation.py", line 54, in __set__
value = self._set_matrix(obj, value, shape)
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/representation.py", line 68, in _set_matrix
validate_matrix_shape(
File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/statsmodels/tsa/statespace/tools.py", line 1474, in validate_matrix_shape
raise ValueError('Invalid dimensions for %s matrix: requires %d'
ValueError: Invalid dimensions for state covariance matrix matrix: requires 702 rows, got 3
When I defined the class initially, I did the following
class TVPVAR(sm.tsa.statespace.MLEModel):
def __init__(self, y):
# Create a matrix with [y_t' : y_{t-1}'] for t = 2, ..., T
augmented = sm.tsa.lagmat(y, 1, trim='both', original='in', use_pandas=True)
# Separate into y_t and z_t = [1 : y_{t-1}']
p = y.shape[1]
y_t = augmented.iloc[:, :p]
z_t = sm.add_constant(augmented.iloc[:, p:])
nobs = y.shape[0]
T=y.shape[0]
# Recall that the length of the state vector is p * (p + 1)
k_states = p * (p + 1)
super(TVPVAR,self).__init__(y_t, exog=None, k_states=k_states,k_posdef=k_states)
self.k_y = p
self.k_states = p * (p + 1)
self.nobs = T
self['design'] = np.zeros((self.k_y, self.k_states, 1))
self['transition'] = np.eye(k_states) # G
self['selection'] = np.eye(k_states) # R=1
def update_variances(self, obs_cov, state_cov_diag):
self['obs_cov'] = obs_cov
self['state_cov'] = np.diag(state_cov_diag) # W
init = initialization.Initialization(self.k_states)
init.set((0, 2), 'diffuse')
init.set((2, 4), 'stationary')
self.ssm.initialize(init)
OTHER CODE
def update(self, params, **kwargs):
params = super().update(params, **kwargs)
self['transition', 2,2] = params[0]
self['transition', 3,2] = params[1]
self['state_cov'] = np.diag([params[2]**2, params[3]**2, params[4]**2]) # W
How can I define the dimensions for state covariance matrix and the vector shape? Thanks your inputs.
