National Bureau of Economic Research

About: National Bureau of Economic Research is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Monetary policy & Population. The organization has 2626 authors who have published 34177 publications receiving 2818124 citations. The organization is also known as: NBER & The National Bureau of Economic Research.

Are Technology Improvements Contractionary

Abstract: Yes. We construct a measure of aggregate technology change, controlling for varying utilization of capital and labor, non- constant returns and imperfect competition, and aggregation effects. On impact, when technology improves, input use and non- residential investment fall sharply. Output changes little. With a lag of several years, inputs and investment return to normal and output rises strongly. We discuss what models could be consistent with this evidence. For example, standard one-sector real-business-cycle models are not, since they generally predict that technology improvements are expansionary, with inputs and (especially) output rising immediately. However, the evidence is consistent with simple sticky-price models, which predict the results we find: When technology improves, input use and investment demand generally fall in the short run, and output itself may also fall.

Investing for the Long Run When Returns are Predictable

Abstract: We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical significance of the evidence for predictability makes it important to take estimation risk into account; a long-horizon investor who ignores it may over-allocate to stocks by a sizeable amount.

A Model of Investor Sentiment

Abstract: Recent empirical research in finance has uncovered two families of pervasive regularities: underreaction of stock prices to news such as earnings announcements; and overreaction of stock prices to a series of good or bad news. In this paper, we present a parsimonious model of investor sentiment that is, of how investors form beliefs that is consistent with the empirical findings. The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values.

The Economics and Psychology of Personality Traits

Abstract: This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.

Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets

Abstract: We examine the implications of arbitrage in a market with many assets. The absence of arbitrage opportunities implies that the linear functionals that give the mean and cost of a portfolio are continuous; hence there exist unique portfolios that represent these functionals. These portfolios span the mean-variance efficient set. We resolve the question of when a market with many assets permits so much diversification that risk-free investment opportunities are available. Ross 112, 141 showed that if there is a factor structure, then the mean returns are approximately linear functions of factor loadings. We define an approximate factor structure and show that this weaker restriction is sufficient for Ross' result. If the covariance matrix of the asset returns has only K unbounded eigenvalues, then there is an approximate factor structure and it is unique. The corresponding K eigenvectors converge and play the role of factor loadings. Hence only a principal component analysis is needed in empirical work.