The Business Cycles Implications of Fluctuating Long Run Expectations

Abstract

I consider a real-business cycle, DSGE model where consumption is a function of the present discounted value of wage and capital income. The agent is uncertain if these income variables are stationary or non-stationary and puts positive probability on both representations. The agent uses Bayesian learning to update his probability weights on each model and these weights vary over time according to how well each model fits the data. The model exhibits an improved fit to the data relative to the rational expectations benchmark. The model requires half the level of exogenous shocks to match the volatility of output and still matches the relative volatilities of key business cycle variables. The model lowers the contemporaneous correlation of consumption and wages with output and generates positive auto-correlation in model growth rates. Impulse responses exhibit persistent responses and consistent with survey evidence forecast errors are positively serially correlated. Finally, in contrast to the existing literature, the model endogenously generates observed time varying volatility and long run predictability of business cycle variables, especially for investment.