In this paper, we identify the technology shock at business cycle frequencies to improve the performance of structural vector autoregression models in small samples. To this end, we propose a new identification method based on the spectral decomposition of the variance, which targets the contributions of the shock in theoretical models. Results from a Monte-Carlo assessment show that the proposed method can deliver a precise estimate of the response of hours in small samples. We illustrate the application of our methodology using US data and a standard Real Business Cycle model. We find a positive response of hours in the short run following a non-significant, near-zero impact. This result is robust to a large set of credible parameterizations of the theoretical model.

I examine whether an RBC model can generate a higher volatility of consumption relative to output, a strong negative correlation between output and the trade balance, and a weak countercyclicality of the real interest rate, phenomena that have been observed in the business cycles of emerging economies, including Korea. From an RBC model with recursive utility, I show that it is not the degree of relative risk aversion, but the elasticity of intertemporal substitution (EIS), that governs the movements of the variables of the model in the log linearized environment. The Bayesian estimation results based on Korean data from the period 1987 to 2013 suggest that there are some elements of success in describing the Korean economy based on the simple RBC model both with the EIS larger than one and with an error term for the real interest rate equation. An EIS larger than one improves the performance of the simple RBC model mainly in the direction of raising the volatility of consumption relative to output. Simulation results show that the error term for the real interest rate process mostly reflects the endogenous channel of financial frictions where the domestic real interest rate depends negatively on the expected (transitory) productivity shock.

In this paper, we investigate the ability of a modified real business cycle (RBC) model to reproduce asymmetries observed for macroeconomic variables over the business cycle. To replicate the empirical skewness of major U.S. macroeconomic variables, we introduce a capacity constraint into an otherwise prototypical RBC model. This constraint emerges because of the assumption of kinked marginal costs of utilization, where the kink is located at a utilization rate of 100%. We find that a model with a suitably calibrated cost function reproduces the empirical coefficients of skewness remarkably well.

The present paper uses an analytical approach to derive approximate closed-form solutions for the asset moments of a real-business-cycle model with idiosyncratic risk. To preserve analytical tractability, risk sharing is completely shut down. Further, the firm is assumed to maximize a variant of value maximization, given that its usual objective is no longer well defined under market incompleteness. When idiosyncratic risk is incorporated into the model, the asset moments can be decomposed into their value under identical households plus a new idiosyncratic term. Under full constrained persistence, in which case the same household determines the asset moments every period, the model is able to generate the risk premium in the data with reasonable parameter values, but it cannot generate the risk return trade-off. While the quantitative impact of idiosyncratic risk is smaller when there is only some persistence in who is constrained, the qualitative predictions are unaltered.