code: def test_start_params():
      # Test that the behavior is correct for multiple exogenous and / or
      # autoregressive components
  
      # Parameters
      nobs = int(1e4)
      beta = np.r_[10, -2]
      phi = np.r_[0.5, 0.1]
  
      # Generate data
      np.random.seed(1234)
      exog = np.c_[np.ones(nobs), np.arange(nobs) * 1.0]
      eps = np.random.normal(size=nobs)
      endog = np.zeros(nobs + 2)
      for t in range(1, nobs):
          endog[t + 1] = (
              phi[0] * endog[t] + phi[1] * endog[t - 1] + eps[t]
          )
      endog = endog[2:]
      endog += np.dot(exog, beta)
  
      # Now just test that the starting parameters are approximately what they
      # ought to be (could make this arbitrarily precise by increasing nobs,
      # but that would slow down the test for no real gain)
      mod = UnobservedComponents(endog, exog=exog, autoregressive=2)
      assert_allclose(
          mod.start_params, [1.0, 0.5, 0.1, 10, -2], atol=1e-1
      )
  
imports: import warnings
  import numpy
  from numpy.testing import assert_equal, assert_allclose, assert_raises
  import pandas
  import pytest
  from statsmodels.datasets import macrodata
  from statsmodels.tools.sm_exceptions import SpecificationWarning
  from statsmodels.tsa.statespace import structural
  from statsmodels.tsa.statespace.structural import UnobservedComponents
  from statsmodels.tsa.statespace.tests.results import results_structural
label_desc: Use image node_id 23 for calling a global function with example usage: test_start_params() without return types