Essays on Macroeconomics and Dynamic Factor Models
This dissertation will consist of three chapters investigating topics on macroeconomics and dynamic factor models. In the first chapter, I develop theoretical open-economy economic models to analyze the role of asymmetric information in explaining short-run international business cycles fluctuations. In the second chapter, I propose a bootstrap estimator for dynamic factor models and show the estimator improves our understanding of macroeconomic data. In the third chapter, I introduce heterogeneous information into a standard closed-economy business cycle model with real estate production and demonstrate information heterogeneity as a key role in explaining housing price fluctuations and residential and non-residential investment dynamics. The first chapter is based on joint work with Mototsugu Shintani. In this chapter, we introduce a noisy information structure into an otherwise standard international real business cycle model with two countries. When domestic firms observe current foreign technology with some noise, predictions of the model on international correlations become very different from that of a standard perfect information model. We show that the model can explain: (i) positive output correlation both in complete and incomplete market models; (ii) consumption correlation smaller than output correlation with an introduction of information-constrained households; and (iii) observation of both positive and negative productivity-hours correlation in two countries. Our results are consistent for models with and without capital as an input in the production functions. The second chapter is also based on joint work with Mototsugu Shintani. In this chapter, we investigate the finite sample properties of the two-step estimators of dynamic factor models when unobservable common factors are estimated by the principal components methods in the first step. Effects of the number of individual series on the estimation of an autoregressive model of a common factor are investigated both by theoretical analysis and by a Monte Carlo simulation. When the number of the series is not sufficiently large, relative to the number of time series observations, the autoregressive coefficient estimator of positively autocorrelated factor is biased downward and the bias is larger for a more persistent factor. In such a case, bootstrap procedures are effective in reducing the bias, and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of controlling the coverage probability. Finally, the third chapter studies information heterogeneity, housing dynamics and the business cycle. We empirically show that house prices are highly volatile and closely correlated with the business cycle, and the fact is at odds with the evidence that rental prices are relatively stable and almost uncorrelated with the business cycle. To explain the fact, we introduce information heterogeneity into a standard dynamic stochastic general equilibrium (DSGE) model with financial frictions. Agents are endowed with heterogeneous shocks, and rationally extract information from market activities. Since agents are confused by changes in average private signals about future fundamentals, the model generates an amplified effect of technology shocks on house prices, which accounts for the disconnect between house prices and the discounted sum of future rents. In addition, the model provides insights for the lead-lag relationship between residential and nonresidential investment over the business cycle. The solution method developed in this paper can be applied in other DSGE models with heterogeneous information.