In aggregate unadjusted data, measured Solow residuals exhibit large seasonal variations. Total Factor Productivity grows rapidly in the fourth quarter at an annual rate of 16 percent and regresses sharply in the first quarter at an annual rate of –24 percent. This paper considers two potential explanations for the measured seasonal variation in the Solow residual: labor hoarding and increasing returns to scale. Using a specification that allows for no exogenous seasonal variation in technology and a single seasonal demand shift in the fourth quarter, we ask the following question: How much of the total seasonal variation in the measured Solow residual can be explained by Christmas? The answer to this question is surprising. With increasing returns and time varying labor effort, Christmas is sufficient to explain the seasonal variation in the Solow residual, consumption, average productivity, and output in all four quarters. Our analysis of seasonally unadjusted data uncovers important roles for labor hoarding and increasing returns which are difficult to identify in adjusted data.
We provide new evidence that models of the monetary transmission mechanism should be consistent with at least the following facts. After a contractionary monetary policy shock, the aggregate price level responds very little, aggregate output falls, interest rates initially rise, real wages decline by a modest amount, and profits fall. We compare the ability of sticky price and limited participation models with frictionless labor markets to account for these facts. The key failing of the sticky price model lies in its counterfactual implications for profits. The limited participation model can account for all the above facts, but only if one is willing to assume a high labor supply elasticity (2 percent) and a high markup (40 percent). The shortcomings of both models reflect the absence of labor market frictions, such as wage contracts or factor hoarding, which dampen movements in the marginal cost of production after a monetary policy shock.
We consider a dynamic, stochastic equilibrium business cycle model which is augmented to reflect seasonal shifts in preferences, technology, and government purchases. Our estimated parameterization implies implausibly large seasonal variation in the state of technology: rising at an annual rate of 24% in the fourth quarter and falling at an annual rate of 28% in the first quarter. Furthermore, our findings indicate that variation in the state of technology of this magnitude is required if the model is to explain the main features of the seasonal cycle.
Monetary policy research using time series methods has been criticized for using more information than the Federal Reserve had available in setting policy. To quantify the role of this criticism, we propose a method to estimate a VAR with real-time data while accounting for the latent nature of many economic variables, such as output. Our estimated monetary policy shocks are closely correlated with a typically estimated measure. The impulse response functions are broadly similar across the methods. Our evidence suggests that the use of revised data in VAR analyses of monetary policy shocks may not be a serious limitation.