Macroeconomics and Methodology Christopher Sims

MACROECONOMICS AND METHODOLOGY
CHRISTOPHER SIMS
Journal of Economic Perspectives 1996
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Outline
Advances in sciences: discuss the view of science as data reduction
Evaluation of theories⇒ science-as-data-reduction view vs hypothesis
testing view
The role of statistical inference across disciplines
Rhetoric of economics
Real Business Cycle School
Progress in Quantitative Macroeconomics
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Science as data reduction
Advances in natural sciences are discoveries of ways to compress
data with minimal loss of information.
Example: Tycho Brahe accumulated large amounts of reliable data on the movements of
the planets. Kepler observed that they are all elliptical orbits with the sun at a focus.
Economics: same aim but less successful ⇒ Whatever theory
economist use to characterize data on the economy, the actual data
contain substantial variation that is not captured in the theory.
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Critique to the hypothesis testing view
Common view of economists: formulate testable hypothesis and
confront them with data.
⇒ True hypothesis survive the test, false ones would be eliminated.
Critique 1: Hypothesis testing view is dependent on the idea that there
are true and false theories.
⇒ The degree to which theories succeed in reducing data can be a continuum.
⇒There are better theories than others. “Worse” theories are not necessarily false and do
not have to be rejected.
Critique 2: A naive hypothesis testing approach might accept a theory
as “true” but the theory can be so complex that do not allow important
data reduction.
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The role of statistical inference across disciplines
Formal statistical inference is not important when:
the data are so abundant that allow the available theories to be clearly
ranked ⇒ Experimental natural sciences.
there is no need to choose among competing - observationally
equivalent- theories.
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The role of statistical inference across disciplines
Economics:
Then if:
Little experimentation possible. Particularly true for macroeconomics.
Exist competing theories - observationally equivalent- to some questions.
Important policy questions demand opinions from economic experts.
If data do not make the choice of the theory obvious
and predictions of policy effects depend on the choice of the theory
⇒ experts can only discuss their conclusions only using notions of probability.
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The rhetoric of Economics
Concern: Too much attention is given to the development of rhetorical
devices in economic argument and to encourage rhetorical skill among
economist. (McCloskey (1983) methodological essay).
⇒ Many economist are willing to use arguments they know are flawed
without explaining the flaws or cite evidence they know could be shown
to be misleading, for sake of rhetorical effectiveness.
Rhetoric should remain secondary.
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General equilibrium modeling
Macroeconomics is a branch of economics that makes drastic
simplifications for the sake of studying a phenomena (the business cycle,
economic growth) that inherently requires analysis of general
equilibrium.
It is natural and promising that macroeconomists, as computational
power expands, are exploring and using dynamic, stochastic, general
equilibrium (DSGE) models.
Still, the models are too stylized and too remote from fitting the data to
provide reliable guide to policy.
DSGE modeling has delivered little empirical payoff so far.
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Real Business Cycle School (Kydland and Prescott)
According to K&P:
⇒ macroeconomists are said to have available a “well-tested” theory, or
“standard” theory.
⇒ They do computational “experiments”.
⇒ These experiments usually results in “established theory becoming
stronger” but occasionally discover an extension of the existing theory that is
useful, and thereby “established theory” is “improved”.
These analogies with established physical sciences are strained.
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Real Business Cycle School (Kydland and Prescott)
K&P say that the neoclassical stochastic growth model is a “well
tested” theory but they do not discuss its limitations.
⇒ Even within the researches using this theory there is no illusion that it
is uncontroversial.
RBC research has ignored most of the known facts about the
business cycle in assessing the match between DSGE models and the
facts
⇒ K&P do not document the way theory does and does not match
the data.
We may still be interested in a poorly fitting theory if:
⇒ it offers a dramatic data compression.
⇒ it is a type of theory that promisses to fit better with further work.
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Real Business Cycle School (Kydland and Prescott)
What K&P call “computational experiments” are computations, not
experiments.
k&P argue that is reasonable to look at the theoretical probability
distribution implied by a model for a set of statistics and to compare this
to the statistics computed from actual data
⇒ How to compare a distribution from the stochastic model to a
statistic of the actual data? Little guidance.
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Real Business Cycle School (Kydland and Prescott)
K&P definition of what constitutes a well tested theory does not suffices
when we need to consider which of two or more models/theories
with different policy implications is more reliable.
⇒ K&P argument: Neoclassical stochastic growth model constitutes a
well tested theory because this framework produce “normal-looking
fluctuations” or “similar to those actually observed”.
⇒ When we have 2 or more models it is not convincing to say that we
should be more confident in the one whose simulated data is more
“normal-looking” ⇒ Need to engage in statistical inference.
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Real Business Cycle School (Kydland and Prescott)
K&P claim that “searching within some parametric class of economies
for the one that best fits a set of aggregate time series makes litlle sense,
because it isn’t likely to answer an interesting question”.
It does make sense.
Example:
⇒ “How much of the US postwar economy would have fluctuated if technology shocks had
been the only source of fluctuations?
Approach:
⇒ construct a parametric class of DSGE models in which the parameter indexed the
contribution of technology shocks to fluctuations.
⇒ Examine behavior of model fit as a function of this parameter.
- The model fit is insensitive to the parameter (model weakely identified)
or
- Some sources of impulse response could be ruled out as unlikely, because they implied a
poor fit.
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Progress in quantitative macroeconomics
Streams of research:
Work based on DSGE models using formal methods of probability-based
inference:
- McGrattan, Rogerson and Wright (1993): estimate a standard real
bussiness cycle model using maximum likelihood.
- Leeper and Sims (1994): RBC + fluctuating relative price of
consumption and capital goods + monetary and fiscal sectors.
Policy modeling in the tradition of simultaneous equations modeling.
Work based on weakely identified time series models to isolate the
effects of monetary policy.
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