Recent studies show that uncertainty shocks have quantitatively important effects on the real economy. This paper examines one particular channel at work: the supply of credit. It presents a model in which a bank, even if managed by risk-neutral shareholders and subject to limited liability, can exhibit self-insurance, and thus loan supply contracts when uncertainty increases. This prediction is tested with the universe of U.S. commercial banks over the period 1984-2010. Identification of credit supply is achieved by looking at the differential response of banks according to their level of capitalization. Consistent with the theoretical predictions, increases in uncertainty reduce the supply of credit, more so for banks with lower levels of capitalization. These results are weaker for large banks, and are robust to controlling for the lending and capital channels of monetary policy, to different measures of uncertainty, and to breaking the dataset in subsamples. Quantitatively, uncertainty shocks are almost as important as monetary policy ones with regards to the effects on the supply of credit.

This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.

In recent years nonlinearities have gained increasing importance in economic and econometric research, particularly after the financial crisis and the economic downturn after 2007. This book contains theoretical, computational and empirical papers that incorporate nonlinearities in econometric models and apply them to real economic problems. It intends to serve as an inspiration for researchers to take potential nonlinearities in account. Researchers should be aware of applying linear model-types spuriously to problems which include non-linear features. It is indispensable to use the correct model type in order to avoid biased recommendations for economic policy.

A timeless classic of economic theory that remains fascinating and pertinent today, this is Frank Knight's famous explanation of why perfect competition cannot eliminate profits, the important differences between "risk" and "uncertainty," and the vital role of the entrepreneur in profitmaking. Based on Knight's PhD dissertation, this 1921 work, balancing theory with fact to come to stunning insights, is a distinct pleasure to read. FRANK H. KNIGHT (1885-1972) is considered by some the greatest American scholar of economics of the 20th century. An economics professor at the University of Chicago from 1927 until 1955, he was one of the founders of the Chicago school of economics, which influenced Milton Friedman and George Stigler.

The authors conducted a systematic empirical study of cross-sectional inequality in the U.S., integrating data from various surveys. The authors follow the mapping suggested by the household budget constraint from individual wages to individual earnings, to household earnings, to disposable income, and, ultimately, to consumption and wealth. They document a continuous and sizable increase in wage inequality over the sample period. Changes in the distribution of hours worked sharpen the rise in earnings inequality before 1982, but mitigate its increase thereafter. Taxes and transfers compress the level of income inequality, especially at the bottom of the distribution, but have little effect on the overall trend. Charts and tables. This is a print-on-demand publication; it is not an original.

Uncertain Futures considers how economic actors visualize the future and decide how to act in conditions of radical uncertainty. It starts from the premise that dynamic capitalist economies are characterized by relentless innovation and novelty and hence exhibit an indeterminacy that cannot be reduced to measurable risk. The organizing question then becomes how economic actors form expectations and make decisions despite the uncertainty they face. This edited volume lays the foundations for a new model of economic reasoning by showing how, in conditions of uncertainty, economic actors combine calculation with imaginaries and narratives to form fictional expectations that coordinate action and provide the confidence to act. It draws on groundbreaking research in economic sociology, economics, anthropology, and psychology to present theoretically grounded empirical case studies. These demonstrate how grand narratives, central bank forward guidance, economic forecasts, finance models, business plans, visions of technological futures, and new era stories influence behaviour and become instruments of power in markets and societies. The market impact of shared calculative devices, social narratives, and contingent imaginaries underlines the rationale for a new form of narrative economics.

The literature on leverage until now shows how an increase in volatility reduces leverage. However, in order to explain pro-cyclical leverage it assumes that bad news increases volatility. This paper suggests a reason why bad news is more often than not associated with higher future volatility. We show that, in a model with endogenous leverage and heterogeneous beliefs, agents have the incentive to invest mostly in technologies that become volatile in bad times. Together with the old literature this explains pro-cyclical leverage. The result also gives rationale to the pattern of volatility smiles observed in the stock options since 1987. Finally, the paper presents for the first time a dynamic model in which an asset is endogenously traded simultaneously at different margin requirements in equilibrium.