The idea of business cycle accounting (BCA hereafter) developed by Chari, Kehoe, and McGrattan (2002, 2004, 2007a) is to assess which wedge is important for the fluctuation of an economy which is assumed to be described as a prototype model with time-varying wedges. These wedges resemble productivity, labor and investment taxes, and government consumption. Since these wedges are measured using the production function and first order conditions to fit the actual macroeconomic data, this method can be interpreted as a generalization of growth accounting.
In this paper, we apply the parameterized expectations algorithm (PEA hereafter) to BCA. There are two contributions of this paper. The first one is application of the PEA to BCA. The PEA introduced by Marcet (1988) is one of the methods to solve the non linear dynamic stochastic general equilibrium model. Marcet and Lorenzoni (1998) provide applications of PEA to some economic models. The basic idea of the PEA is to approximate the expectation function by a smooth function, a polynomial function in general. The PEA has an advantage in that it is simpler and easier to understand and implement than the other non-linear solution methods.
Secondly, we apply BCA to the Japanese economy using the PEA which relaxes the perfect foresight assumption and show that the result is similar to the main result in the deterministic BCA by Kobayashi and Inaba (2006). They assume perfect foresight in the prototype economy so that all wedges are given deterministically as in Chari et al. (2002). The assumption of perfect foresight enables us to avoid complicated calculations. As they point out, however, the effects of the investment wedge are sensitive to the assumption on the future values of wedges. On the other hand, the stochastic model that wedges are assumed to be an exogenous stochastic process which is estimated from the data does not suffer from the arbitrary choices of the future values of wedges.
Chakraborty (2004) also applies BCA to the Japanese economy using a log linearized dynamic stochastic general equilibrium model. The simulation result on the investment wedge is somewhat different from Kobayashi and Inaba (2006). In this paper, we find that the result of BCA using PEA is similar to the result of the perfect foresight BCA. Therefore, we can conclude that the causes of the difference in the results between Chakraborty and Kobayashi-Inaba must be in data constructions, data sources, and log-linearization. In cases where the economy is far away from the steady state or highly non-linear, the approximation error may be large. Therefore, taking account of non-linearities may be important.
Download
Business cycle accounting for the Japanese economy using the parameterized expectations algorithm
