Fabio Milani

Expectation Shocks and Learning as Drivers of the Business Cycle

Psychological factors, market sentiments, and shifts in beliefs are believed by many to play a nontrivial role in inducing and amplifying economic fluctuations. Yet, these forces are rarely considered in macroeconomic models. This paper provides an attempt to evaluate the empirical role of expectational shocks on business cycle fluctuations. The paper relaxes the conventional assumption of rational expectations to exploit observed data on survey and market expectations in the estimation of a benchmark New Keynesian model. The observed expectations are modeled as formed from a near-rational expectation formation mechanism, which assumes that economic agents use a linear perceived law of motion for economic variables that has the same structural form as the model solution under rational expectations and that they need to learn model coefficients over time. In addition to the typical structural demand, supply, and policy disturbances, the model incorporates expectation shocks, which affect the formation of expectations by the private sector. Both the best-fitting learning process and the expectations shocks are identified from the expectations data and from the interaction between expectations and realized data. The expectations shocks capture waves of optimism and pessimism that lead agents to form forecasts that deviate from those implied by their learning model and by the state of the economy. The empirical results uncover a crucial role for these novel expectations shocks as a major driving force of the U.S. business cycle. Expectation shocks regarding future real activity are the main source of economic fluctuations, since they can account for roughly half of business cycle fluctuations.

Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy

Factor-augmented VARs (FAVARs) have combined standard VARs with factor analysis to exploit large data sets in the study of monetary policy. FAVARs enjoy a number of advantages over VARs: they allow a better identification of the monetary policy shock; they avoid the use of a single variable to proxy theoretical constructs; they allow researchers to compute impulse responses for hundreds of variables. Their shortcoming, however, is that the factors are not identified and lack an economic interpretation.This paper seeks to provide an interpretation to the factors. We propose a novel Structural Factor-Augmented VAR (SFAVAR) model, where the factors have a clear meaning: Real Activity factor, Inflation factor, Financial Market factor, Credit factor, Expectations factor, and so forth. The paper employs a Bayesian approach to jointly estimate the factors and the dynamic model. This framework is then used to study the effects of monetary policy on a wide range of macroeconomic variables.

Adaptive Learning and Inflation Persistence

What generates persistence in inflation? Is inflation persistence structural? This paper investigates learning as a potential source of persistence in inflation. The paper focuses on the price-setting problem of firms and presents a model that nests structural sources of persistence (indexation) and learning. Indexation is typically necessary under rational expectations to match the inertia in the data and to improve the fit of estimated New Keynesian Phillips curves. The empirical results show that when learning replaces the assumption of fully rational expectations, structural sources of persistence in inflation, such as indexation, become unsupported by the data. The results suggest learning behavior as the main source of persistence in inflation. This finding has implications for the optimal monetary policy. The paper also shows how ones results can heavily depend on the assumed learning speed. The estimated persistence and the model fit, in fact, vary across the whole range of constant gain values. The paper derives the best-fitting constant gains in the sample and shows that the learning speed has substantially changed over time.

Monetary Policy with a Wider Information Set: A Bayesian Model Averaging Approach

Monetary policy has been usually analyzed in the context of small macroeconomic models where central banks are allowed to exploit a limited amount of information. Under these frameworks, researchers typically derive the optimality of aggressive monetary rules, contrasting with the observed policy conservatism and interest rate smoothing. This paper allows the central bank to exploit a wider information set, while taking into account the associated model uncertainty, by employing Bayesian Model Averaging with Markov Chain Model Composition (MC³). In this enriched environment, we derive the optimality of smoother and more cautious policy rates, together with clear gains in macroeconomic efficiency.

The Misspecification of Expectations in New Keynesian Models: A DSGE-VAR Approach

This paper tests the ability of popular New Keynesian models, which are traditionally used to study monetary policy and business cycles, to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. In the empirical analysis, we exploit direct data on expectations from surveys. To explain the joint evolution of realized variables and expectations, we adopt a DSGE-VAR approach, which allows us to estimate all models in the continuum between the extremes of an unrestricted VAR, on one side, and a DSGE model in which the cross-equation restrictions are dogmatically imposed, on the other side. Moreover, the DSGE-VAR approach allows us to assess the extent, as well as the main sources, of misspecification in the model. The paper’s results illustrate the failure of New Keynesian models under the rational expectations hypothesis to account for the dynamic interactions between observed macroeconomic expectations and macroeconomic realizations. Confirming previous studies, DSGE restrictions prove valuable when the New Keynesian model is exempted from matching observed expectations. But when the model is required to match data on expectations, it can do so only by moving away, and hence substantially rejecting, DSGE restrictions. Finally, we investigate alternative models of expectations formation, including examples of extrapolative and heterogeneous expectations, and show that they can go some way toward reconciling the New Keynesian model with the data. Intermediate DSGE-VAR models, which avail themselves of DSGE prior restrictions, return to fit the data better than the unrestricted VAR. Hence, the results overall point to misspecification in the expectations formation side of the DSGE model, more than in the structural microfounded equations.

Econometric Issues in DSGE Models

The paper by An and Schorfheide reviews an important body of literature that takes the empirical implications of Dynamic Stochastic General Equilibrium (DSGE) models seriously. Often, in the past, ...

Public option and private profits: what do markets expect?

BackgroundThe debate on US healthcare reform has largely focused on the introduction of a public health plan option. While supporters stress various beneficial effects that would arise from increased competition in the health insurance market, opponents often contend that a public plan would drive insurers out of the market and potentially lead to the ‘collapse’ of the private health insurance industry.ObjectivesTo contribute to the US healthcare reform debate by inferring, from financial market data, the effect that the public option is likely to have on the private health insurance market.MethodsThe study utilized daily data on the price of a security that was traded in a prediction market from June 2009 and whose pay-off was tied to the event that a federal government-run healthcare plan — the ‘public option’ - would be approved by 31 December 2009 (100 daily observations). These data were combined with data on stock returns of health insurance companies (1500 observations from 100 trading days and 15 companies) to evaluate the expected effect of the public option on private health insurers. The impact on hospital companies (1000 observations) was also estimated.ResultsThe results suggested that daily stock returns of health insurance companies significantly responded to the changing probability regarding the public option. A 10% increase in the probability that the public option would pass, on average, reduced the stock returns of health insurance companies by 1.28% (p < 0.001). Hospital company stock returns were also affected (0.9% reduction; p < 0.001).ConclusionsThe results reveal the market expectation of a negative effect of the public option on the value of health insurance companies. The magnitude of the effect suggests a downward adjustment in the expected profits of health insurers of around 13%, but it does not support more calamitous scenarios.