statsmodels中TVP-VAR模型拟合报错：method与float无法比较

问题场景 #

在statsmodels的statespace mlemodels框架下运行面板TVP-VAR模型，执行拟合时触发类型错误，怀疑与初始参数定义相关。

报错信息及回溯 #

preliminary = tvppanelvarmodel.fit(maxiter=1000)
Traceback (most recent call last):

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 57, in _wrapfunc
    return bound(*args, **kwds)

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/_methods.py", line 159, in _clip
    return _clip_dep_invoke_with_casting(

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/_methods.py", line 113, in _clip_dep_invoke_with_casting
    return ufunc(*args, out=out, **kwargs)

**TypeError: '>=' not supported between instances of 'method' and 'float'**


During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "/var/folders/m6/68zljfsj2t9_dzgpwwslj29r0000gp/T/ipykernel_11675/3038987883.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 298, in _minimize_lbfgsb
    x0 = np.clip(x0, new_bounds[0], new_bounds[1])

  File "<__array_function__ internals>", line 180, in clip

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 2152, in clip
    return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 66, in _wrapfunc
    return _wrapit(obj, method, *args, **kwds)

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 43, in _wrapit
    result = getattr(asarray(obj), method)(*args, **kwds)

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/_methods.py", line 159, in _clip
    return _clip_dep_invoke_with_casting(

  File "/opt/anaconda3/envs/spyder-env/lib/python3.10/site-packages/numpy/core/_methods.py", line 113, in _clip_dep_invoke_with_casting
    return ufunc(*args, out=out, **kwargs)

TypeError: '>=' not supported between instances of 'method' and 'float'

当前初始参数定义 #

使用Nelder-Mead优化器（method='nm'），初始参数方法未添加return语句：

def start_params(self):
       
        start_params =  [.1, .1, 100, 100, 100] 

解决步骤 #

1. 修复初始参数方法：start_params方法必须返回参数数组，否则statsmodels会将方法本身当作参数传入优化器，触发类型错误。修改代码如下：

   def start_params(self):
           start_params =  [.1, .1, 100, 100, 100] 
           return start_params
   

2. 验证参数维度：确保返回的参数列表长度与模型待估计的参数数量一致，避免后续出现维度不匹配问题。
3. 重新执行拟合：再次运行拟合代码即可正常启动优化流程。

内容的提问来源于stack exchange，提问作者David K