LEARNING ABOUT REGIME CHANGE
研究了全要素生产率和投资特定技术增长中的体制转换行为，构建了理性预期真实商业周期模型，发现学习体制转换能拟合数据、影响冲击响应、增加不对称性、产生预测误差并提高波动福利成本。
Abstract Total factor productivity (TFP) and investment specific technology (IST) growth both exhibit regime‐switching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We develop a general perturbation solution algorithm for a wide class of models with unobserved regime‐switching. Using our method, we show learning about regime‐switching fits the data, affect the responses to regime shifts and intraregime shocks, increase asymmetries in the responses, generate forecast error bias even with rational agents, and raise the welfare cost of fluctuations.