Misspecified Expectations among Professional Forecasters

Analyzing panel data from the U.S. Survey of Professional Forecasters (SPF, 1992Q1–2019Q4, 77 forecasters, 1,520 forecaster-quarter observations), Julio Ortiz finds that a “misspecified expectations” model — in which forecasters perceive an AR(2) data-generating process to be an AR(1), causing them to misperceive its underlying persistence — tends to outperform a noisy-information rational benchmark and two leading non-FIRE alternatives (overconfident and diagnostic expectations) when fit to forecast errors and revisions. The models are estimated by maximum likelihood and ranked using forecast-encompassing weights; for the baseline real GDP growth case, misspecified expectations earns the largest encompassing weight (0.539 vs. 0.462 for diagnostic, ~0 for rational and overconfident) and the highest log-likelihood. Across 14 macroeconomic variables, misspecified expectations provides the best fit for most series both in-sample and out-of-sample, though diagnostic expectations fits better for some (e.g., GDP deflator, industrial production, real residential investment) and rational expectations fits the unemployment rate best. The author argues misspecified expectations succeeds in part because its bias enters both the prediction and updating equations, producing overreaction to new information plus overextrapolation across horizons, which makes forecast errors longer-lived; he concludes it can serve as a “suitable approach” / useful benchmark to model professional-forecaster expectation formation, while emphasizing the results are specific to the context of professional forecasting and may not carry over to household or firm expectations.

Spread too thin: The impact of lean inventories

This paper investigates the macroeconomic consequences of widespread just-in-time (JIT) inventory management, documenting a fundamental trade-off: JIT raises firm profitability and reduces micro-level volatility in normal times, but renders the economy significantly more vulnerable to large unanticipated shocks.

The empirical analysis draws on a novel dataset of approximately 200 publicly listed U.S. manufacturing firms for which the author identifies JIT adoption years using narrative records from SEC filings and historical news archives. Firm-level balance sheet data come from Compustat Fundamentals Annual (1980–2018), merged with county-level weather event data from NOAA. Four empirical facts are documented. First, JIT adoption is associated with a 13% decrease in inventory-to-sales ratios and a 9% increase in sales. Second, JIT adopters experience a roughly 7% decline in sales and employment growth volatility. Third, JIT adopters are approximately 25–30% more cyclical than non-adopters: a 1% increase in GDP growth predicts an additional 0.47 percentage point increase in JIT firm sales growth above the non-adopter baseline of roughly 1.6%. Fourth, a weather disaster predicts an additional 3% decline in JIT firm sales and employment relative to non-JIT firms.

Julio L. Ortiz | Macro Paper Warehouse

Misspecified Expectations among Professional Forecasters

Spread too thin: The impact of lean inventories