Frame timeseries_breusch_pagan_test

---

timeseries_breusch_pagan_test(self, residuals, factors)

Breusch-Pagan statistics test

Parameters:

residuals : unicode

Name of the column that contains residual values

factors : list

Name of the column(s) that contain factors

Returns:

: dict

Performs the Breusch-Pagan test for heteroskedasticity.

Examples

Consider the following frame:

>>> frame.inspect()
[#]  AT             V              AP             RH             PE
==============================================================================
[0]  8.34000015259  40.7700004578  1010.84002686  90.0100021362  480.480010986
[1]  23.6399993896  58.4900016785  1011.40002441  74.1999969482         445.75
[2]  29.7399997711  56.9000015259  1007.15002441  41.9099998474  438.760009766
[3]  19.0699996948  49.6899986267   1007.2199707  76.7900009155  453.089996338
[4]  11.8000001907  40.6599998474  1017.13000488  97.1999969482  464.429992676
[5]   13.970000267  39.1599998474  1016.04998779  84.5999984741  470.959991455
[6]  22.1000003815  71.2900009155  1008.20001221  75.3799972534  442.350006104
[7]   14.470000267  41.7599983215  1021.97998047  78.4100036621          464.0
[8]          31.25  69.5100021362        1010.25  36.8300018311  428.769989014
[9]  6.76999998093  38.1800003052  1017.79998779  81.1299972534  484.299987793

Calculate the Bruesh-Pagan test statistic where the “AT” column contains residual values and the other columns are factors:

>>> result = frame.timeseries_breusch_pagan_test("AT",["V","AP","RH","PE"])
[===Job Progress===]

The result contains the test statistic and p-value:

>>> result["test_stat"]
22.674159327676357

>>> result["p_value"]
0.00014708935047758054