Monopolistic Competition and New Products: A Conjectural Equilibrium Approach

In this paper we generalize the heterogeneous risk adverse agents model of diffusion of new products in a multi-firm, heterogeneous and interacting agents environment. We use a model of choice under uncertainty based on Bayesian theory. We discuss the possibility of product failures, the set of equilibria, their stability and some welfare properties.Product diffusion, Risk aversion, Lock-in, Monopolistic competition, Multiple equilibria

Quality Risk Aversion, Conjectures, and New Product Diffusion

In this paper we provide a generalization of the standard models of the diffusion of a new product. Consumers are heterogeneous and risk averse, and the firm is uncertain about the demand curve: both learn from past observations. The attitude towards risk has important effects with regard to the diffusion pattern. In our model, downward-biased signals to consumers can prevent the success of the product, even if its objective quality is high: a “lock-in” result. We show in addition that the standard logistic pattern can be derived from the model. Finally, we discuss the asymptotic behavior of the learning dynamics, with regard to the multiplicity and the stability of equilibria, and to their welfare properties.Heterogeneity, Multiple equilibria, Lock-in, Product diffusion, Risk aversion.

An Economist´s guide to the Kalman filter

Almost since its appearance, the Kalman Filter (KF) has been successfully used in control engineering. Unfortunately, most of its important results have been published in engineering journals with language, notation and style proper of engineers. In this paper, we want to present the KF in an attractive way to economists by using information theory and Bayesian inference.

Asset pricing under rational learning about rare disasters : [Version 28 Juli 2011]

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors’ information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the likelihood of rare disasters drop to a much more pessimistic level once a disaster has occurred. Such a shift in beliefs can trigger massive declines in price-dividend ratios. Pessimistic beliefs persist for some time. Thus, belief dynamics are a source of apparent excess volatility relative to a rational expectations benchmark. Due to the low frequency of disasters, even an infinitely-lived investor will remain uncertain about the exact probability. Our analysis is conducted in continuous time and offers closed-form solutions for asset prices. We distinguish between rational and adaptive Bayesian learning. Rational learners account for the possibility of future changes in beliefs in determining their demand for risky assets, while adaptive learners take beliefs as given. Thus, risky assets tend to be lower-valued and price-dividend ratios vary less under adaptive versus rational learning for identical priors. Keywords: beliefs, Bayesian learning, controlled diffusions and jump processes, learning about jumps, adaptive learning, rational learning. JEL classification: D83, G11, C11, D91, E21, D81, C6

Booms and Busts in Asset Prices

We show how low-frequency boom and bust cycles in asset prices can emerge from Bayesian learning by investors. Investors rationally maximize infinite horizon utility but hold subjective priors about the asset return process that we allow to differ infinitesimally from the rational expectations prior. Bayesian updating of return beliefs then gives rise to self-reinforcing return optimism that results in an asset price boom. The boom endogenously comes to an end because return optimism causes investors to make optimistic plans about future consumption. The latter reduces the demand for assets that allow to intertemporally transfer resources. Once returns fall short of expectations, investors revise return expectations downward and set in motion a self-reinforcing price bust. In line with available survey data, the learning model predicts return optimism to comove positively with market valuation. In addition, the learning model replicates the low frequency behavior of the U.S. price dividend ratio over the period 1926-2006.

Ambiguity and macroeconomics:a rationale for price stickiness

This paper deals with the emergence of price stickiness, that is nominal price elasticity below one, in the wake of nominal shocks. The setting of analysis is a general equilibrium model with both ambiguity and rational expectations. Ambiguity and macroeconomics are linked exploiting a micro-founded framework. Ambiguity concerns the lack of knowledge of firms about the relationship between changes in the aggregated stock of money and in the money distribution across heterogeneous consumers in the economy. Ambiguity is represented through a multiple priors approach. It is shown that price stickiness can emerge even if a change in the money supply level does not alter the distribution of money across consumers (uniform monetary policy). The key assumption made in the paper is that attitude towards ambiguity of firms is asymmetric: ambiguity aversion towards uncertain positive outcomes (gains) and ambiguity seeking towards negative outcomes (losses). By focusing on the dynamics of beliefs following a change in the stock of money that does not alter the money distribution, it is shown that money neutrality remains true in the long runAmbiguity, multiple priors, incomplete information, price stickiness

Booms and Busts in Asset Prices

We show how low-frequency boom and bust cycles in asset prices can emerge from Bayesian learning by investors. Investors rationally maximize infinite horizon utility but hold subjective priors about the asset return process that we allow to differ infinitesimally from the rational expectations prior. Bayesian updating of return beliefs then gives rise to selfreinforcing return optimism that results in an asset price boom. The boom endogenously comes to an end because return optimism causes investors to make optimistic plans about future consumption. The latter reduces the demand for assets that allow to intertemporally transfer resources. Once returns fall short of expectations, investors revise return expectations downward and set in motion a self-reinforcing price bust. In line with available survey data, the learning model predicts return optimism to comove positively with market valuation. In addition, the learning model replicates the low frequency behavior of the U.S. price dividend ratio over the period 1926-2006.asset price fluctuations, boom and bust cycles

Consumer Misperceptions, Uncertain Fundamentals, and the Business Cycle

This paper explores the importance of shocks to consumer misperceptions, or "noise shocks", in a quantitative business cycle model. I embed imperfect information as in Lorenzoni (2009) into a new Keynesian model with price and wage rigidities. Agents learn about the components of labor productivity by only observing aggregate productivity and a noisy signal. Noise shocks lead to expectational errors about the true fundamentals triggering aggregate fluctuations. Estimating the model with Bayesian methods on US data shows that noise shocks contribute to 20 percent of consumption fluctuations at short horizons. Wage rigidity is pivotal for the importance of noise shocks.Imperfect Information, Noise Shocks, Aggregate Fluctuations, Bayesian Estimation