Price puzzle

The price puzzle is a phenomenon in monetary economics observed within structural vector autoregression (SVAR) models. It refers to the counterintuitive result where a contractionary monetary policy shock—typically modeled as an increase in short-term interest rates—is followed by an increase, rather than a decrease, in the price level. This anomaly challenges conventional macroeconomic theories that predict a decline in prices as monetary tightening reduces aggregate demand.

Historical Context

The term "price puzzle" was first introduced by Lawrence Christiano in 1992, who observed this anomaly in SVAR models analyzing U.S. monetary policy. Early studies found that when using short-term interest rates, such as the federal funds rate, as the primary indicator of monetary policy, SVAR models often produced results inconsistent with theoretical expectations. This sparked a series of investigations into the limitations of these models and the underlying causes of the puzzle.

Why the Price Puzzle Occurs in SVAR Models?

The price puzzle is largely attributed to the structure and assumptions of SVAR models, which often fail to fully capture the information set available to central banks or the forward-looking nature of monetary policy. Key issues include:

• Omitted Variables: Traditional SVAR models sometimes exclude variables that capture supply-side shocks, such as commodity prices, leading to biased results.

• Backward-Looking Dynamics: Many SVAR models assume delayed reactions to policy shocks, which neglect the anticipatory behavior of economic agents and policymakers.

• Limited Monetary Policy Indicators: Using simple interest rate rules without considering broader monetary aggregates can result in incomplete representations of policy impacts, exacerbating the price puzzle.

Efforts to Resolve the Price Puzzle

Augmented Information Sets

One approach to resolving the price puzzle involves expanding the information set in SVAR models. For instance, including variables like commodity prices or Federal Reserve forecasts (e.g., Greenbook data) can provide additional context for policy decisions, reducing the puzzle's prevalence.

Divisia Monetary Aggregates

The study of Divisia monetary aggregates as superior policy indicators has its roots in the work of Keating et al. and Belongia and Ireland, who emphasized the importance of incorporating broad monetary aggregates into economic models to better understand monetary policy effects. Their research demonstrated that Divisia aggregates outperform traditional simple-sum measures, such as M1 and M2, by resolving anomalies like the price puzzle and establishing a more stable relationship between money supply and macroeconomic variables.

Building on this foundation, Chen and Valcarcel expanded the application of Divisia aggregates, highlighting their ability to account for the liquidity services of monetary components and provide richer insights into monetary policy transmission. Their inclusion of Divisia aggregates in SVAR models has consistently mitigated the price puzzle in both historical and modern samples, reinforcing their value as robust tools in monetary economics.

Rational Expectations Augmented SVAR (RE-SVAR)

The RE-SVAR methodology developed by Chen and Valcarcel incorporates forward-looking rational expectations directly into the SVAR framework. This method addresses the puzzle by aligning the model's assumptions with the forward-looking nature of monetary policy. By comparing interest rate rules with money growth rules, RE-SVAR demonstrates that models using Divisia aggregates are more robust in capturing monetary shocks without generating puzzling price responses.