The University of Tokyo · Economics, Econometrics and Finance
Professor Mototsugu Shintani's research lab specializes in econometric theory, time series analysis, and macroeconomic modeling with a focus on nonlinear dynamics, unit root testing, and business cycle fluctuations. The lab investigates complex economic phenomena such as news shocks, nonlinear adjustment in exchange rates, and chaotic behavior in macroeconomic time series using advanced statistical and computational methods. A central theme is the development of robust econometric techniques—particularly nonparametric and semiparametric methods—for detecting structural changes, nonlinear trends, and long-memory properties in economic data. The lab's work bridges theoretical econometrics with empirical macroeconomic applications, especially in the context of U.S. and Japanese economies.
Figures are computed from collected data and may differ slightly.
We examine whether the news shocks, as explored in Beaudry and Portier (2004), can be a major source of aggregate fluctuations. For this purpose, we extend a standard dynamic stochastic general equilibrium model of Christiano, Eichenbaum, and Evans (2005) and Smets and Wouters (2003, 2007) by allowing news shocks on the total factor productivity (TFP), and estimate the model using Bayesian methods. Estimation results on the U.S. and Japanese economies suggest that (i) news shocks play a relative
A positive Lyapunov exponent is one practical definition of chaos. We develop a formal test for chaos in a noisy system based on the consistent standard errors of the nonparametric Lyapunov exponent estimators. For international real output series, the hypothesis of the positive Lyapunov exponent is significantly rejected in many cases. One possible interpretation of this result is that the traditional exogenous models are better able to explain business cycle fluctuations than is the chaotic en
This article develops a novel test for a unit root in general transitional autoregressive models, which is based on the infimum of t ‐ratios for the coefficient of a parametrized transition function. Our test allows for very flexible specifications of the transition function and short‐run dynamics and is significantly more powerful than all the other existing tests. Moreover, we develop a large sample theory general enough to deal with randomly drifting parameter spaces, which is essential to pr
This paper extends the diffusion index (DI) forecast approach of Stock and When the number of series is large, a two-step procedure based on the principal components method is useful since it allows the wide variety of the nonlinearity in the factors. The factors extracted from a large Japanese data suggest some evidence of nonlinear structure. Furthermore, both the linear and nonlinear DI forecasts in Japan outperform traditional time series forecasts, while the linear DI forecast, in most case
Abstract It has been claimed that the deviations from purchasing power parity are highly persistent and have quite long half‐lives under the assumption of a linear adjustment of real exchange rates. However, inspired by trade cost models, nonlinear adjustment has been widely employed in recent empirical studies. This paper proposes a simple nonparametric procedure for evaluating the speed of adjustment in the presence of nonlinearity, using the largest Lyapunov exponent of the time series. The e
Abstract This paper proposes a new test for the presence of a nonlinear deterministic trend approximated by a Fourier expansion in a univariate time series for which there is no prior knowledge as to whether the noise component is stationary or contains an autoregressive unit root. Our approach builds on the work of Perron and Yabu ( ) and is based on a Feasible Generalized Least Squares procedure that uses a super‐efficient estimator of the sum of the autoregressive coefficients α when α = 1. T
We investigate the finite sample properties of the estimator of a persistence parameter of an unobservable common factor when the factor is estimated by the principal components method. When the number of cross-sectional observations is not sufficiently large, relative to the number of time series observations, the autoregressive coefficient estimator of a positively autocorrelated factor is biased downward, and the bias becomes larger for a more persistent factor. Based on theoretical and simul
Abstract We construct business cycle indexes based on the daily Japanese newspaper articles and estimate the Phillips curve model to forecast inflation at a daily frequency. We find that the news-based leading indicator, constructed from the topic on future economic conditions, is useful in forecasting the inflation rate in Japan.
Open papers in the app to read, cite, and organize with AI.