hamilton_filter: Clarify the API (#634)

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
-
-
-