Christopher Sims transformed how macroeconomists think about data, uncertainty, inflation, and the limits of human attention. Across four decades, he kept asking the same question: what is this model assuming away? And then he built the tools to address it.

I have written before about Sims’ critique of large-scale macroeconometric models and about why macroeconomics never had a credibility revolution. Today I want to focus on what Sims built, not what he tore down. His contributions span four areas: vector autoregressions replaced incredible structural models with transparent empirical tools. Bayesian methods brought intellectual honesty to estimation under uncertainty. The fiscal theory of the price level challenged the conventional separation of monetary and fiscal policy. And rational inattention reimagined how economic agents process information. In each case, the pattern was the same: identify what was being swept under the rug, and build a framework that forced it into the open.

The figure below traces this intellectual arc across four decades

I. The Language of Macroeconomic Data

Before Sims, macroeconomists argued about models. After Sims, they argued about facts.

The dominant empirical approach of the 1970s was the large-scale structural model. These models, built at institutions like the Federal Reserve, the Brookings Institution, and the Cowles Commission, contained dozens or hundreds of equations representing consumption, investment, money demand, labor supply, price formation. The ambition was to capture the entire macroeconomic system in simultaneous equations that could be estimated, simulated, and used for policy analysis.

The problem was that each equation required assumptions about which variables appeared and which did not. A variable was declared exogenous because the model required it. A coefficient was set to zero because it simplified estimation. The models were impressive engineering, but their empirical content rested on exclusion restrictions that were difficult to defend on independent grounds.

By the mid-1970s, the limitations of this approach were widely recognized. Robert Lucas’ famous critique (Lucas, 1976) showed that estimated parameters could shift when policy changed, because agents adjust their behavior to new regimes. Lucas and others proposed building models from deeper primitives, preferences, technology, rational expectations, that would remain stable across policy changes.

Sims took a different path. Rather than replacing one set of strong assumptions with another, he asked a more basic question: what can we learn from the data with minimal assumptions?

The answer was the vector autoregression (Sims, 1980). Take a set of macroeconomic variables, say, output, inflation, the interest rate, and the money supply. Instead of assuming a structural model that specifies which variable affects which, let every variable depend on its own past values and the past values of every other variable:

where Y_t ​ is a vector of macroeconomic variables and u_t​ is a vector of reduced-form residuals. No economic theory is imposed. The VAR simply describes how the variables move together over time.

To give these co-movements a causal interpretation, the researcher must decompose the residuals into orthogonal structural shocks. This requires restrictions, but the restrictions are stated explicitly. The simplest approach is a recursive ordering, a Cholesky decomposition that assumes some variables respond to shocks within the period while others do not. Place the interest rate last, and you are assuming that monetary policy responds within the period to all other variables, but output and prices respond only with a lag. This is an assumption, but it is an assumption you can see, debate, and test.

Once you have identified structural shocks, you trace their effects forward through impulse response functions. What happens to output over the next twelve quarters after a monetary policy shock? The impulse response function became the signature output of the VAR literature, and it changed the terms of debate. Instead of arguing about which theoretical assumptions were correct, macroeconomists could point to empirical facts and ask: can your model replicate these?

The toolkit expanded rapidly. Blanchard and Quah (1989) introduced long-run restrictions. Uhlig (2005) proposed sign restrictions. Romer and Romer (2004) used narrative methods. Each approach made different assumptions. Each was explicit about what it required. This is the Sims legacy in empirical macro: not a specific method, but a methodological standard. State your assumptions, defend them, and show that your results survive scrutiny.

II. Taking Uncertainty Seriously

VARs are high-dimensional. A VAR with six variables and eight lags has hundreds of parameters. With typical macroeconomic samples of a few hundred quarterly observations, classical frequentist methods struggle. Sims’ response was to turn to Bayesian methods, and in doing so he became a central figure in bringing Bayesian econometrics into mainstream macroeconomics.

The key insight was that priors are a feature, not a bug. In high-dimensional models, you need to regularize. Frequentist methods do this implicitly through model selection. Bayesian methods do it explicitly through the choice of prior.

The most influential application was the Minnesota prior (Doan, Litterman, and Sims, 1984), developed by Sims with Robert Litterman and Thomas Doan in the early 1980s. The prior embodies a simple belief: each variable in a VAR is likely to behave roughly like a random walk. It shrinks all coefficients toward zero except the first own lag of each variable, which is shrunk toward one. If you have no information about the relationship between industrial production and unemployment at various lags, start from the assumption that each variable mostly follows its own recent trend. The data are free to update this wherever the evidence is strong enough.

The Bayesian VAR became a workhorse forecasting tool. The Federal Reserve, the European Central Bank, the Bank of England, all widely use variants of BVARs for macroeconomic forecasting.

But Sims’ advocacy went beyond forecasting. He argued that uncertainty about the model itself, not just uncertainty about parameters within a model, was a first-order concern for policymakers. Central banks do not know the true model of the economy. A Bayesian framework lets you assign probabilities to different models, update them as evidence accumulates, and make policy decisions that account for this uncertainty. The Smets and Wouters (2007) model, estimated with Bayesian methods and widely used at central banks, is a direct descendant of this insistence.

III. The Fiscal Theory of the Price Level

VARs and Bayesian methods are primarily methodological. The fiscal theory of the price level is different. It is a substantive claim about how inflation works, and one that many macroeconomists still reject.

The standard view is that inflation is fundamentally a monetary phenomenon. Sims proposed an alternative. His argument is that the price level is also determined by the government’s intertemporal budget constraint:

where B_t​ is nominal government debt, P_t is the price level, and τ_{t+s} represents future real primary surpluses. In the standard view, fiscal policy adjusts to satisfy this constraint. In the fiscal theory, when fiscal authorities show no inclination to adjust, the price level does the work instead. If the present value of future surpluses falls short of the real value of debt, inflation rises to reduce debt’s real value until the equation is satisfied.

This means a central bank cannot always independently control inflation. Its power depends on whether fiscal authorities cooperate, on whether fiscal policy is “Ricardian” (adjusting surpluses to satisfy the constraint) or “non-Ricardian” (leaving the price level to do it).

Sims applied this framework to the European Monetary Union in a prescient paper (Sims, 1999) titled “The Precarious Fiscal Foundations of EMU.” He argued that a monetary union without fiscal integration was unstable. A decade later, the Greek fiscal crisis conformed remarkably well to the scenario Sims had warned about.

The theory remains contested. Many macroeconomists, including Sims’ Nobel co-laureate Thomas Sargent, have questioned whether the government budget constraint should be interpreted as an equilibrium condition that determines the price level or simply as a constraint that fiscal authorities must satisfy. But the core insight, that fiscal and monetary policy cannot be analyzed independently, has become part of how macroeconomists think about the world. The global accumulation of government debt since 2020 has only made these questions more urgent.

IV. Rational Inattention

The first three contributions deal with how economists analyze data and think about policy. The fourth deals with how we model people.

Standard models assume agents are fully informed. Rational expectations theory takes this further: agents use all available information optimally. Sims wanted to extend this framework by asking what happens when information processing itself has a cost. People do not track every price in the economy. Firms do not continuously update their pricing decisions. The question was how to model this limited capacity in a disciplined way.

His answer drew on Claude Shannon’s information theory. In his influential 2003 paper (Sims, 2003), Sims proposed treating economic agents as finite-capacity channels. An agent observes a multidimensional world, prices, incomes, interest rates, news, but can only process a limited amount of information per unit of time, measured in Shannon bits. The agent must choose what to pay attention to. The formal constraint is:

where I(C;W) is the mutual information between the agent’s actions and the true state, and κ is the channel’s capacity.

What does this imply? Agents’ responses to external changes are sluggish and noisy, not because of mechanical frictions, but because they rationally choose not to track every fluctuation precisely. Prices update slowly not because of menu costs or Calvo contracts, but because firms have finite capacity to monitor their environment. The degree of inertia is endogenous: when inflation becomes more volatile, agents allocate more capacity to tracking it, and the economy’s response to shocks changes.

The framework also explains why individual behavior is noisy even among agents facing identical fundamentals, and why central bank communication matters. If agents have finite capacity, the way information is presented affects how it is processed.

The rational inattention literature has grown substantially. Maćkowiak and Wiederholt (2009) showed that firms pay more attention to idiosyncratic shocks than to aggregate ones, explaining why aggregate prices are stickier than individual prices. Matějka and McKay (2015) proved that the multinomial logit model emerges as the optimal choice rule under rational inattention with Shannon entropy costs, an unexpected bridge between information theory and discrete choice.

The Unifying Thread

In October 2011, the Royal Swedish Academy of Sciences awarded the Nobel Memorial Prize in Economic Sciences jointly to Christopher Sims and Thomas Sargent. The citation recognized their “empirical research on cause and effect in the macroeconomy.” The pairing was apt. Sargent had pursued rational expectations from within, building structural models with forward-looking agents. Sims had pursued a parallel path from the outside, building empirical tools that could discipline theory without assuming it. They had argued for forty years, productively and with mutual respect.

The deeper unity across Sims’ work is the pattern I described at the beginning. Where large-scale structural models relied on untested restrictions, Sims built VARs that made assumptions transparent. Where classical estimation treated model choice as settled, Sims introduced Bayesian methods that acknowledged uncertainty honestly. Where monetary theory treated inflation as the central bank’s domain alone, Sims showed that fiscal foundations matter. Where standard models assumed perfect information processing, Sims modeled the limits of attention.

Each contribution began with an acknowledgment that something important was being swept under the rug, and proceeded to build a framework that took it seriously. The data come first. Theory must explain the data, not the other way around. Sims was a theorist of the highest caliber, but he insisted that theory earns its place by matching empirical regularities, not by logical elegance alone.

During his freshman year at Harvard, a teaching assistant had told Sims that as a mathematician, he would never be able to change the world. He took the advice seriously. He finished the mathematics degree, then turned to economics. The teaching assistant was right about one thing: pure mathematics, in the abstract, might not change the world. But mathematics in the service of honest empirical inquiry can. Sims died on March 14, 2026. He was 83.

There is one part of the Sims story I have deliberately left out: his early work on causality. In “Money, Income, and Causality” (Sims, 1972), he applied Granger’s framework to test whether money causes income or vice versa, and his results reshaped the monetarist-Keynesian debate. That work, and the broader question of what “causality” means in time series, deserves its own treatment. I will return to it in a future essay.

Some Worth Readings

Sims, C.A. (1972). “Money, Income, and Causality.” American Economic Review, 62(4): 540–552. The paper that tested whether money causes income or the reverse, using Granger’s temporal causality framework. It sided with the monetarists, but the method mattered more than the answer.

Lucas, R.E. (1976). “Econometric Policy Evaluation: A Critique.” Carnegie-Rochester Conference Series on Public Policy, 1: 19–46. The paper that identified a fundamental limitation of large-scale econometric models: if agents adjust expectations when policy changes, the estimated parameters shift too. Sims built on this insight but took a different path forward.

Sims, C.A. (1980). “Macroeconomics and Reality.” Econometrica, 48(1): 1–48. The paper that introduced VARs into macroeconomics and called the identifying assumptions of large-scale models “incredible.” One of the most cited empirical macro papers ever written, with over 13,000 citations.

Blanchard, O.J. & D. Quah (1989). “The Dynamic Effects of Aggregate Demand and Supply Disturbances.” American Economic Review, 79(4): 655–673. Extended the VAR framework by using long-run restrictions to separate supply and demand shocks. A permanent effect on output means it was supply; a transitory effect means it was demand.

Sims, C.A. (1999). “The Precarious Fiscal Foundations of EMU.” De Economist, 147(4): 415–436. Sims warned that a monetary union without fiscal integration was unstable. The eurozone crisis a decade later confirmed the warning.

Sims, C.A. (2003). “Implications of Rational Inattention.” Journal of Monetary Economics, 50(3): 665–690. The paper that imported Shannon’s information theory into economics. Agents have finite capacity to process information, and this alone generates the sluggish, noisy behavior we see in the data.

Romer, C.D. & D.H. Romer (2004). “A New Measure of Monetary Shocks: Derivation and Implications.” American Economic Review, 94(4): 1055–1084. Narrative identification of monetary policy shocks. Read the Fed’s internal forecasts, strip out the predictable component, and what remains is a genuine policy surprise.

Uhlig, H. (2005). “What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure.” Journal of Monetary Economics, 52(2): 381–419. Sign restrictions as an alternative to recursive orderings. Instead of assuming a causal ordering, impose only that a contractionary shock does not raise output or lower rates on impact.

Smets, F. & R. Wouters (2007). “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review, 97(3): 586–606. The benchmark Bayesian DSGE model used at central banks worldwide. A direct descendant of Sims’ insistence on taking both theory and uncertainty seriously.

Maćkowiak, B. & M. Wiederholt (2009). “Optimal Sticky Prices under Rational Inattention.” American Economic Review, 99(3): 769–803. Showed that firms rationally pay more attention to their own costs than to aggregate conditions. This explains why individual prices move a lot but the aggregate price level is sticky.

Matějka, F. & A. McKay (2015). “Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model.” American Economic Review, 105(1): 272–298. Proved that the multinomial logit, one of the most used models in applied economics, emerges naturally from rational inattention with Shannon entropy costs. An unexpected bridge between information theory and discrete choice.