hamilton_filter: Clarify the API

quantecon/filter.py

@@ -5,24 +5,25 @@
 """
 import numpy as np
 
-def hamilton_filter(data, h, *args):
+
+def hamilton_filter(data, h, p=None):
  r"""
  This function applies "Hamilton filter" to the data
- 
+
  http://econweb.ucsd.edu/~jhamilto/hp.pdf
- 
+
  Parameters
  ----------
  data : array or dataframe
  h : integer
  Time horizon that we are likely to predict incorrectly.
  Original paper recommends 2 for annual data, 8 for quarterly data,
  24 for monthly data.
- *args : integer
- If supplied, it is p in the paper. Number of lags in regression. 
+ p : integer (optional)
+ If supplied, it is p in the paper. Number of lags in regression.
  Must be greater than h.
  If not supplied, random walk process is assumed.
- 
+
  Note: For seasonal data, it's desirable for p and h to be integer multiples
  of the number of obsevations in a year.
  e.g. For quarterly data, h = 8 and p = 4 are recommended.
@@ -38,8 +39,7 @@ def hamilton_filter(data, h, *args):
  # sample size
  T = len(y)
 
- if len(args) == 1: # if p is supplied
- p = args[0]
+ if p is not None: # if p is supplied
  # construct X matrix of lags
  X = np.ones((T-p-h+1, p+1))
  for j in range(1, p+1):
@@ -51,12 +51,7 @@ def hamilton_filter(data, h, *args):
  trend = np.append(np.zeros(p+h-1)+np.nan, X@b)
  # cyclical component
  cycle = y - trend
-
- elif len(args) == 0: # if p is not supplied (random walk)
+ else: # if p is not supplied (random walk)
  cycle = np.append(np.zeros(h)+np.nan, y[h:T] - y[0:T-h])
  trend = y - cycle
-
  return cycle, trend
-
-
-