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Historical Statistics and Monetary Policy

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Historical Statistics and Monetary Policy – PowerPoint PPT presentation

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Title: Historical Statistics and Monetary Policy


1
Historical Statistics and Monetary Policy
  • Pierre L. Siklos
  • Wilfrid Laurier University and
  • Viessmann Research Centre

2
What are we trying to analyze, and why?
  • High Frequencies gt business cycle (short-run
  • Noise in data is expected. Quality may matter
    less than econometric technique used to interpret
    data
  • WHY?
  • Focus is more on the role of expectations and
    markets and the pure reaction to information

3
What are we trying to analyze, and why?
  • Middle Frequencies ? business cycle
  • Quarterly or monthly data will do and their
    regimes may come into play. Quality may be
    important but consistency of definitions for
    variables becomes relatively more important
  • WHY?
  • Regimes are less frequent but policy
    reactions/changes are possibly more frequent

4
What are we trying to analyze, and why?
  • Low Frequencies lt business cycle (long-run)
  • Annual data are OK but Quality of data becomes
    paramount because measurement of variables can
    change significantly across time
  • WHY?
  • Rare events so we need a long span of data,
    statistical technique requires a long span of
    data, we want to know about changes in regimes

5
How Should Historical Series be Put Together?
  • Is consistency more important than span of data?
    Is there a Trade-off?
  • May be easier for some time series than for
    others (e.g., GDP VS money supply)
  • Is there a danger that increasing span of data
    makes it easier to rewrite history?
  • Construction may be by back casting VS from the
    ground up (e.g., price data)

6
What Can We Learn, what are the pitfalls of
analyzing monetary policy with historical Data?
  • Example 1 Siklos (1993) 2 slides
  • Example 2 Siklos-Burdekin (1998) 2slides
  • Example 3 Romer 1 slide
  • Example 4 Monetary Policy Rules and the Output
    Gap (Burdekin and Siklos 2004)
  • Example 5 Siklos (2004?)

7
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8
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9
The "Phillips" Curve by Type of Income Measure
10
The "Phillips" Curve by Type of Unemployment
Measure
11
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15
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16
What Is the State of Play in the Use of
Historical Statistics? Selected Examples
17
Future Research?
  • Part I Methodology How Historical Data are
    Used or Misused? A Meta-Analysis?
  • Part II Econometric considerations How to test
    theories while remaining sensitive to data
    construction issues
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