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MOTEGI KaijiAssociate Professor
Research Interests: Econometrics, Time Series Analysis, Applied Macroeconomics.
My research interest lies in statistical analysis of economic time series. A fundamental objective of time series econometrics is to reveal and predict the dynamic properties of economic time series such as gross domestic product (GDP), inflation, stock prices, and interest rates. I am developing new methods to improve the accuracy of prediction in two fields below.
1. Mixed Data Sampling (MIDAS) Econometrics
Classical time series models require all data to have the same sampling frequency. Consider analyzing an interaction between unemployment and GDP in Japan, for example. The unemployment statistics are announced monthly, while the GDP statistics are announced quarterly in many counties. Classical techniques force us to aggregate monthly unemployment data into a quarterly level. Such a temporal aggregation causes the loss of information and consequently lowers the precision of statistical inference.
A new strand of research that emerged in the United States and Europe in around 2004 attempts to exploit all data available whatever their sampling frequencies are. This field is called "Mixed Data Sampling (MIDAS)" or "Mixed Frequency". Many researchers report that prediction accuracy improves dramatically by taking advantage of MIDAS techniques.
I have been working with theory and applications of MIDAS since I was a Ph.D. student at the Department of Economics, University of North Carolina at Chapel Hill from August 2009 through May 2014. First, I defined Granger causality (i.e. incremental predictive ability) with mixed frequency data, and then proposed a test for the mixed frequency Granger causality. Second, I revisited the sluggish private investment in Japan’s Lost Decade, taking advantage of the MIDAS approach.
The MIDAS literature is growing rapidly, and I believe that there still exists a large room for further development.
Photo: A poster presentation on MIDAS at the 25th EC2 Conference held at Universitat Pompeu Fabra in Barcelona, Spain, in December 2014.
2. White Noise Tests
A time series is called white noise if its future values are uncorrelated with past and present values. Testing for the white noise hypothesis might sound like a classical and trivial problem, but is actually a new and challenging topic. A major challenge is that serial uncorrelatedness is a substantially weaker condition than serial independence. It is therefore hard to establish the formal asymptotic theory under the null hypothesis of white noise.
I am proposing a new white noise test that is based on the largest sample autocorrelation across lags. After extensive simulation experiments, I have found that my test detects remote autocorrelations more accurately than existing white noise tests. As an empirical application, I am testing for the weak form efficiency of stock markets (i.e. white noise hypothesis of stock returns).
Photo: A poster presentation on white noise tests at the NBER-NSF Time Series Conference held at Columbia University, New York, in September 2016.
Photo: An oral presentation on the weak form efficiency of stock markets at the 3rd Annual International Conference on Applied Econometrics in Hawaii, hosted by the Graduate School of Economics, Kobe University in Honolulu, Hawaii in September 2017.
Lectures and Seminars
“Analysis of Stationary Time Series” & “Analysis of Nonstationary Time Series”
These are master's level English courses on theory and methods of time series analysis. A primary goal of these courses is to learn the literacy of time series econometrics so that students can perform a sensible analysis of time series data in their papers and theses. While a main focus will be put on theoretical aspects, I will also present simulation results and empirical illustrations as much as possible in order to keep a nice balance between theory and practice.
The required textbook is Walter Enders (2014, 4th edition) "Applied Econometric Time Series", Wiley. A supplemental reading is James D. Hamilton (1994) "Time Series Analysis", Princeton University Press.
“Analysis of Stationary Time Series” offered in the 1st Quarter of 2017 covers the stationary time series literature including autoregressive moving average (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), and vector autoregression (VAR).
“Analysis of Nonstationary Time Series” offered in the 3rd Quarter of 2017 covers the nonstationary time series literature including unit root, spurious regression, cointegration, and vector error correction model (VECM).
Many economic time series are nonstationary in levels and stationary in the first difference. It is thus of great use to learn a proper way of handling both stationary and nonstationary time series.
Some course materials are publicly available on my personal website so that you can preview and review the courses.
Photos: “Time Series Analysis” offered in the 3rd Quarter of 2016.
I always enjoy teaching time series analysis. It is exciting to learn the theory and practice of time series analysis. Discovering dynamic characteristics of economic variables will definitely have a positive impact on the economics literature and our entire society. I am looking forward to seeing you in class.
 E. Ghysels, J. B. Hill, and K. Motegi (2016). Testing for Granger Causality with Mixed Frequency Data. Journal of Econometrics, vol. 192, pp. 207-230.
 K. Motegi and A. Sadahiro (2017). Sluggish Private Investment in Japan's Lost Decade: Mixed Frequency Vector Autoregression Approach. North American Journal of Economics and Finance, accepted in October 2017 (forthcoming).
 E. Ghysels, J. B. Hill, and K. Motegi (2017). Testing a Large Set of Zero Restrictions in Regression Models, with an Application to Mixed Frequency Granger Causality.
 J. B. Hill and K. Motegi (2017). A Max-Correlation White Noise Test for Weakly Dependent Time Series.
 J. B. Hill and K. Motegi (2017). Testing for White Noise Hypothesis of Stock Returns.
 S. Hamori, K. Motegi, and Z. Zhang (2017). Semiparametric Estimation of Copula by Calibration Weighting under Missing Data.