Fiscal Stimulus In Expectations-Driven Liquidity Traps

I study liquidity traps in a model where agents have heterogeneous expectations and finite planning horizons. Backward-looking agents base their expectations on past observations, while forward-looking agents have fully rational expectations. Liquidity traps that are fully or partly driven by expectations can arise due to pessimism of backward-looking agents. Only when planning horizons are finite, these liquidity traps can be of longer duration without ending up in a deflationary spiral. I further find that fiscal stimulus in the form of an increase in government spending or a cut in consumption taxes can be very effective in mitigating the liquidity trap. A feedback mechanism of heterogeneous expectations causes fiscal multipliers to be the largest when the majority of agents is backward-looking but there also is a considerable fraction of agents that are forward-looking. Labor tax cuts are always deflationary and are not an effective tool in a liquidity trap

The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations

We propose a simple model of expectation formation with three distinct deviations from fully rational expectations. In particular, forecasters’ expectations are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecaster as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. Moreover, in an over-identified econometric specification, we find that the restriction on coefficients implied by our model is always close to being satisfied and in most cases not rejected. We also stress the point that using the past consensus forecast to form expectations is a rather smart thing to do if cognitive limitations and biases cause any attempt to build an own rational forecast to fail

The Stabilizing Effects of Publishing Strategic Central Bank Projections

Expectations are among the main driving forces for economic dynamics. Therefore, managing expectations has become a primary objective for monetary policy seeking to stabilize the business cycle. In this paper, we study whether central banks can manage private-sector expectations by means of publishing one-period ahead inflation projections in a New Keynesian learning-to-forecast experiment. Subjects in the experiment observe these projections along with the historic development of the economy and subsequently submit their own one-period ahead inflation forecasts. In this context, we find that the central bank can significantly manage private-sector expectations and that this management strongly supports monetary policy in stabilizing the economy. Moreover, published central bank inflation projections drastically reduce the probability of a deflationary spiral after strong negative shocks to the economy

Forecast revisions in the presence of news: a lab investigation

We conduct a laboratory experiment in a fully-fledged macroeconomic model where participants receive information about future government spending shocks and are tasked with repeatedly forecasting output over a given horizon. By eliciting several-period-head predictions, we investigate forecast reaction to news and revision. The lab forecasts are consistent with stylized facts on reaction to news established in the survey literature. We find that subjects steadily learn the magnitude of the effect of the shocks on output, albeit not to full extent. We further find little support for fully backward-looking expectations. We rationalize the experimental data in the context of a Bayesian updating model, which provides a better description of the behaviors in longer-horizon environments and among more attentive and experienced subjects

Generalizing Heuristic Switching Models

The growing literature in behavioral finance and macroeconomics that uses dynamic discrete choice models has overwhelmingly assumed that individual choices are made on the basis of a logit framework. While this assumption allows for analytical tractability, it comes with a number of restrictions with regards to the economic environments it can represent. These restrictions are lifted if a probit framework is used instead. In this paper we compare the two approaches and show that, due to its ability to allow for correlations between the random part of different choice alternatives as well as random taste variation, the probit-based model can better fit actual choice data from an existing laboratory experiment, especially if there are more choice alternatives. On the other hand, for the case of two choice alternatives without random taste variation, the probit-based and logit-based models result in very similar dynamics. But even in that case, we find that important qualitative differences arise - in terms of an additional region of chaos - in the cobweb model of the seminal work of Brock and Hommes (1997). Our work highlights the usefulness of using the probit framework for extensions of existing theoretical models and as a way to better fit dynamic experimental or real world choice data