查看更多>>摘要：Firms face substantial uncertainty when doing business in new markets. We propose that multinational firms use "cross-market learning"to resolve such uncertainties. We develop a model of firm-level expectations formation with noisy signals from multiple markets and derive predictions on market entries and expectations formation over the firm's life cycle. Using a novel dataset of Japanese multinational corporations that includes sales expectations of each affiliate, we provide supportive evidence for the model's predictions. We find that firms rely on their performance in nearby markets to predict their profitability in a new market and make entry decisions. Such "cross-market"learning is less important after the firm has accumulated experience in the new market, but becomes more important if the uncertainty of the focal market is high or the firm has received more signals from the nearby markets.
查看更多>>摘要：Motivated by empirically characterizing the relationship between financial conditions and downside macroeconomic risks in the euro area, I develop a regime-switching skew-normal model with time-varying probabilities of transitions. Using Bayesian methods, the model estimates show that a strong cyclical pattern emerges from the conditional skewness, which has a tendency to rapidly decline to negative territory prior and during recessions. However, the inclusion of financial-specific information in time-varying probabilities does not help to anticipate such skewness nor more generally to provide advance warnings of tail risks.
查看更多>>摘要：This paper assesses information contained in the micro dataset of the ECB Survey of Professional Forecasters regarding quarterly Brent crude oil price forecasts. We examine the expectations building mechanism by referring to the processing of information and confirm the presence of information rigidity within the crude oil market. However, our findings also show that simple models of imperfect information considered in the literature are insufficient to explain the behavior of professional forecasters. We provide additional stylized facts which are helpful for designing more elaborate imperfect information models.
NSTL
Elsevier