© Eric Zivot 2012

Nobel Prize Lecture in Economics

The Study of Causal Relationships in the Macroeconomy: The Contributions of

Christopher Sims

Eric ZivotRobert Richards Chaired Professor of Economics

Christopher Sims Receives Nobel Prize

• Award given “for their empirical research on cause and effect in the macroeconomy”

• Sargent and Sims developed methods for identifying and quantifying the effects of changes in policy

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Christopher Sims at Work

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Christopher Sims – Trombone Player!

• http://youtu.be/fC6ENPLq7uM

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Short Biography

• Harvard, PhD 1968• Harvard, Assistant Professor 1968-70 • Minnesota, Associate Professor 1970-1974• Minnesota, Professor 1974-1990• Yale, Henry Ford II Professor 1990-1999

– One of my PhD advisors!!!

• Princeton, Harold H. Helm '20 Professor of Economics and Banking 1999 - present

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Sims’s Main Contributions• Statistical tests for causal relations

– “Money, Income, and Causality”, AER 1972

• Vector autoregression (VAR) modeling framework– “Macroeconomics and Reality”, ECTA, 1980– “Comparison of Interwar and Postwar Business

Cycles”, AER, 1980– “Are Forecasting Models Usable for Policy Analysis?”,

Fed Quarterly Review, 1986.

• Use of Bayesian statistical methodology– “Forecasting and conditional projection using realistic

prior distributions”. Econometric Reviews, 1984– “Bayesian Methods for Dynamic Multivariate Models",

(with Tao Zha), IER, 1998© Eric Zivot 2012

“Money, Income and Causality”Background

• Keynesian-Monetarist Debate about the causes of business cycles– Keynesian view: business cycles are primarily demand-driven– Monetarist view: business cycles are primarily the result of

monetary policy mistakes

• Monetarist’s argued that growth in the stock of money was tightly related to growth in income and they argued that this relationship was causal - fluctuations in money growth causing fluctuations in income.

• A statistically estimated equation with income explained by current and past money growth implied that most of the business cycle could be eliminated by simply making money supply growth constant.

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“Money, Income, and Causality”Contributions

• Examine the substantive question: Is there statistical evidence that changes in the money supply is causal in the money-income relationship?

• Develop a direct statistical test for the existence of unidirectional causality.– Complements the causality tests of Clive Granger,

who was also awarded the Nobel Prize for his work on the statistical analysis causality.

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Causality Testing: Framework

• If the monetarists were right in claiming that the strong correlations of money growth with income primarily reflected a causal influence of monetary policy errors on income, future money growth should not contribute to explaining current income, once the influence of current and past money growth on income had been accounted for.

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Causality Testing: Results

• The monetarist regressions passed the test. Future money growth did not help predict current income

• Problem: Framework only considers two variables– What about interest rates, prices, unemployment,

exchange rates, etc?– Neglecting these variables could cloud the money-

income causality results

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“Macroeconomics and Reality”

• Circa 1980, the state of the art in macroeconometrics was to use large-scale Keynesian-type structural models.

• These models often involved scores or even hundreds of equations, essentially a S=D equation for every important market, identities to make sure things add up correctly, etc.

• But in order to estimate the parameters of these models, the structural parameters as they are known, you had to overcome the identification problem.

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“Macroeconomics and Reality”

• The identification problem essentially asks if its possible to estimate the structural parameters at all.

• The answer, in general, is no. For example, if every variable in the model appears in every equation, then it won't be possible to estimate the structural model due to simultaneity bias.

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Identification of Structural Models

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“Macroeconomics and Reality”

• In large models, exclusions are numerous, and many researchers simply assumed whatever exclusion restrictions were needed to achieve identification, and then went on to estimate the model.

• Sims argued the assumptions that researchers were imposing to achieve identification had no theoretical basis. They were ad hoc and difficult to defend - especially when expectations are in the model

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Sims’s VAR Modeling Framework

• Step1: Construct statistical forecasting model (VAR) to separate expected from unexpected effects

• Step 2: Identify causal links and extract fundamental shocks (VAR → SVAR)

• Step 3: Trace effects of fundamental shocks on economy over time – Impulse response analysis

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Step 1: Construct VAR

• Forecasting model for a collection of macro variables without ad hoc identifying restrictions

• Each variable is forecast using lagged values of itself and all other variables– Variable = Forecast (expected) + error (unexpected)

• Short-term forecasting accuracy is typically quite good

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Step 2: Identify Causal Links

• Identify causal links and extract fundamental shocks using theory-based and non-ad hoc assumptions– Some variables react to others with a lag (e.g., M only

responds to Y with a lag) – Long-run neutrality arguments (e.g., M has no long-

run impact on Y)

• Process transforms errors in VAR equations into fundamental shocks

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Step 3: Impulse Response Analysis

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Q: How do macro variables respond to unexpected fundamental shocks ?

The Monetarist Debate Viewed Through SVARs

• Sims and others were able to show using SVAR's that influences of monetary policy were detectable in the data.

• But at the same time, they showed that most movements in both money stock and interest rates represented systematic reactions of monetary authorities to the state of the economy.

• Only a small part of macroeconomic fluctuations could be attributed to erratic monetary policy.

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Bayesian Methodology

• The textbook frequentist view distinguishes non-random, but unknown, “parameters” from random quantities that repeatedly vary, or could conceivably repeatedly vary.

• The Bayesian view treats everything that is not known as random, until it is observed, after which it becomes non-random

• Think of coin flipping experiment

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Bayesian Methodology• A Bayesian approach comfortably

accommodates uncertain prior information.• In a large model, it allows introducing sensible

restrictions on the values of unknown parameters, without pretending that these restrictions are without uncertainty.

• That is, it allows introducing probability distributions for model parameters, then allowing the data to update or sharpen those distributions.

• It thereby avoids the need to imply unrealistic precision in the probability distributions for model predictions. © Eric Zivot 2012

Concluding Remarks

• Sims changed the way macroeconomists and econometricians model the economy

• After Sims, the profession moved away from using large scale Keynesian-style structural macro-models and adopted the SVAR approach

• IRF analysis from SVARs provide the “stylized facts” from which modern theory-based models are calibrated and evaluated

• Bayesian methods have become widely accepted

© Eric Zivot 2012