Inattention, Uncertainty, and Macroeconomic Dynamics

This dissertation consists of three essays on expectation formation process.The first chapter estimates information stickiness using the common component of professional forecasters' inattention to many economic variables. Information stickiness is hard to measure, but important for monetary policy effectiveness. Professional forecasters in the U.S. Survey of Professional Forecasters and cross-country Consensus Forecasts on average update information every three to four months. More importantly, forecaster inattention is state-dependent, and it is driven by both economic fundamentals and market volatility. Using a dynamic stochastic general equilibrium (DSGE) model with inattentive firms, this chapter characterizes the relationship between inattention and monetary policy effectiveness. Using empirical analyses, this paper confirms that monetary policy is more effective when firms are inattentive. The second chapter discusses the assumption of sticky information model and derives two novel model implications. Both ex-ante forecast disagreement and ex-post forecast accuracy are associated with the level of inattention at individual level. Between ex-ante forecast disagreement and inattention, there is a U-shaped relationship. When an individual forecaster updates information much more or much less frequently relative to the average level, she tends to have larger disagreement. Between ex-post forecast accuracy and inattention, there is a strictly negative relationship. A forecaster who tends to update her forecast more frequently is expected to have more accurate forecast. Using micro-level data, this chapter finds that forecasters are not interchangeable in terms of their forecast disagreement, forecast accuracy, and inattention. Furthermore, their empirical relationships confirm the model implications. This chapter provides an assessment of the IMF's unemployment forecasts, which have not received much scrutiny to date. The focus is on the internal consistency of the IMF's growth and unemployment forecasts, and specifically on seeing whether the relationship between the two is consistent with the relationship in the data, i.e., with Okun's Law. We find that the average performance is good, in the sense that the relationship between growth and unemployment forecasts is fairly comparable to that which prevails in the data: on average, the Okun coefficient in the forecasts mirrors the Okun coefficient in the data. Nevertheless, there is room for improvement, particularly in the year-ahead forecasts and for the group of middle-income countries. We show that a linear combination of Okun-based unemployment forecasts and WEO unemployment forecasts can deliver significant gains in forecast accuracy for developing economies.