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Yasumasa Matsuda

Personal Details

First Name:Yasumasa
Middle Name:
Last Name:Matsuda
Suffix:
RePEc Short-ID:pma1928
[This author has chosen not to make the email address public]

Affiliation

Graduate School of Economics and Management
Tohoku University

Sendai, Japan
http://www.econ.tohoku.ac.jp/
RePEc:edi:fetohjp (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Wali Ullah & Yoshihiko Tsukuda & Yasumasa Matsuda, 2012. "Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Does Macroeconomic Factors Imply Better Out-of-Sample Forecasts?," TERG Discussion Papers 287, Graduate School of Economics and Management, Tohoku University.
  2. Masaki Narukawa & Yasumasa Matsuda, 2008. "Broadband semiparametric estimation of the long-memory parameter by the likelihood-based FEXP approach," TERG Discussion Papers 239, Graduate School of Economics and Management, Tohoku University.
  3. Yoshihiro Yajima & Yasumasa Matsuda, 2008. "Asymptotic Properties of the LSE of a Spatial Regression in both Weakly and Strongly Dependent Stationary Random Fields," CIRJE F-Series CIRJE-F-587, CIRJE, Faculty of Economics, University of Tokyo.
  4. Yoshihiro Yajima & Yasumasa Matsuda, 2003. "On Nonparametric and Semiparametric Testing for Multivariate Time Series," CIRJE F-Series CIRJE-F-253, CIRJE, Faculty of Economics, University of Tokyo.

Articles

  1. Masaki Narukawa & Yasumasa Matsuda, 2011. "Broadband semi‐parametric estimation of long‐memory time series by fractional exponential models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 175-193, March.
  2. Yasumasa Matsuda & Yoshihiro Yajima, 2009. "Fourier analysis of irregularly spaced data on Rd," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 191-217, January.
  3. Yasumasa Matsuda, 2006. "A test statistic for graphical modelling of multivariate time series," Biometrika, Biometrika Trust, vol. 93(2), pages 399-409, June.
  4. Yasumasa Matsuda & Yoshihiro Yajima, 2004. "On testing for separable correlations of multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 501-528, July.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Wali Ullah & Yoshihiko Tsukuda & Yasumasa Matsuda, 2012. "Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Does Macroeconomic Factors Imply Better Out-of-Sample Forecasts?," TERG Discussion Papers 287, Graduate School of Economics and Management, Tohoku University.

    Cited by:

    1. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & Muhammed Hasan Yilmaz, 2020. "Do Local and Global Factors Impact the Emerging Markets’s Sovereign Yield Curves? Evidence from a Data-Rich Environment," Working Papers 2004, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    3. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2014. "Dynamics of the term structure of interest rates and monetary policy: is monetary policy effective during zero interest rate policy?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 546-572, March.
    4. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Wali Ullah, 2017. "Term structure forecasting in affine framework with time-varying volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 453-483, August.
    6. Wali Ullah & Yasumasa Matsuda, 2012. "Term Structure Modeling and Forecasting of Government Bond Yields : Does Macroeconomic Factors Imply Better Out-of-Sample Forecasts?," TERG Discussion Papers 304, Graduate School of Economics and Management, Tohoku University.
    7. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2015. "Generalized Nelson-Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 876-904, April.

  2. Yoshihiro Yajima & Yasumasa Matsuda, 2008. "Asymptotic Properties of the LSE of a Spatial Regression in both Weakly and Strongly Dependent Stationary Random Fields," CIRJE F-Series CIRJE-F-587, CIRJE, Faculty of Economics, University of Tokyo.

    Cited by:

    1. Javier Hidalgo & Myung Hwan Seo, 2013. "Specification For Lattice Processes," STICERD - Econometrics Paper Series 562, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Hidalgo, Javier & Seo, Myung Hwan, 2015. "Specification tests for lattice processes," LSE Research Online Documents on Economics 66104, London School of Economics and Political Science, LSE Library.
    3. Peter M Robinson, 2011. "Inference on Power Law Spatial Trends (Running Title: Power Law Trends)," STICERD - Econometrics Paper Series 556, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    4. Robinson, Peter M., 2011. "Inference on power law spatial trends (Running Title: Power Law Trends)," LSE Research Online Documents on Economics 58100, London School of Economics and Political Science, LSE Library.

  3. Yoshihiro Yajima & Yasumasa Matsuda, 2003. "On Nonparametric and Semiparametric Testing for Multivariate Time Series," CIRJE F-Series CIRJE-F-253, CIRJE, Faculty of Economics, University of Tokyo.

    Cited by:

    1. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

Articles

  1. Masaki Narukawa & Yasumasa Matsuda, 2011. "Broadband semi‐parametric estimation of long‐memory time series by fractional exponential models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 175-193, March.

    Cited by:

    1. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    2. Tommaso Proietti & Alessandra Luati, 2013. "The Exponential Model for the Spectrum of a Time Series: Extensions and Applications," CREATES Research Papers 2013-34, Department of Economics and Business Economics, Aarhus University.
    3. Jan Beran & Sucharita Ghosh, 2020. "Estimating the Mean Direction of Strongly Dependent Circular Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 210-228, March.
    4. Masaki Narukawa, 2016. "Semiparametric Whittle estimation of a cyclical long-memory time series based on generalised exponential models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 272-295, June.

  2. Yasumasa Matsuda & Yoshihiro Yajima, 2009. "Fourier analysis of irregularly spaced data on Rd," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 191-217, January.

    Cited by:

    1. Sam Efromovich, 2014. "Efficient Non-Parametric Estimation Of The Spectral Density In The Presence Of Missing Observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 407-427, August.
    2. Gupta, A, 2015. "Autoregressive Spatial Spectral Estimates," Economics Discussion Papers 23825, University of Essex, Department of Economics.
    3. Chen, Kun & Chan, Ngai Hang & Yau, Chun Yip & Hu, Jie, 2023. "Penalized Whittle likelihood for spatial data," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    4. Robinson, Peter, 2019. "Spatial long memory," LSE Research Online Documents on Economics 102182, London School of Economics and Political Science, LSE Library.
    5. Delgado, Miguel A. & Robinson, Peter M., 2013. "Non-nested testing of spatial correlation," LSE Research Online Documents on Economics 58169, London School of Economics and Political Science, LSE Library.
    6. Yasumasa Matsuda, 2013. "Generalized Whittle Estimate For Nonstationary Spatial Data," TERG Discussion Papers 305, Graduate School of Economics and Management, Tohoku University.
    7. Soutir Bandyopadhyay & Suhasini Subba Rao, 2017. "A test for stationarity for irregularly spaced spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 95-123, January.
    8. Tata Subba Rao & Granville Tunnicliffe Wilson & Soutir Bandyopadhyay & Carsten Jentsch & Suhasini Subba Rao, 2017. "A Spectral Domain Test for Stationarity of Spatio-Temporal Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 326-351, March.
    9. Giovanna Jona Lasinio & Gianluca Mastrantonio & Alessio Pollice, 2013. "Discussing the “big n problem”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 97-112, March.
    10. Kurisu, Daisuke, 2019. "On nonparametric inference for spatial regression models under domain expanding and infill asymptotics," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    11. Salim Bouzebda & Inass Soukarieh, 2022. "Non-Parametric Conditional U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 11(1), pages 1-69, December.
    12. Salim Bouzebda, 2024. "Limit Theorems in the Nonparametric Conditional Single-Index U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 12(13), pages 1-81, June.
    13. Arthur P. Guillaumin & Adam M. Sykulski & Sofia C. Olhede & Frederik J. Simons, 2022. "The Debiased Spatial Whittle likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1526-1557, September.
    14. Zhang, Shibin, 2020. "Nonparametric Bayesian inference for the spectral density based on irregularly spaced data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).

  3. Yasumasa Matsuda, 2006. "A test statistic for graphical modelling of multivariate time series," Biometrika, Biometrika Trust, vol. 93(2), pages 399-409, June.

    Cited by:

    1. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

  4. Yasumasa Matsuda & Yoshihiro Yajima, 2004. "On testing for separable correlations of multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 501-528, July.

    Cited by:

    1. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    2. J. Hidalgo & M. Schafgans, 2020. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Papers 2006.14409, arXiv.org.
    3. Javier Hidalgo & Marcia M Schafgans, 2015. "Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence," STICERD - Econometrics Paper Series /2015/583, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    4. Eichler, Michael, 2008. "Testing nonparametric and semiparametric hypotheses in vector stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 968-1009, May.
    5. Holger Dette & Efstathios Paparoditis, 2009. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 831-857, September.
    6. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," LSE Research Online Documents on Economics 107426, London School of Economics and Political Science, LSE Library.
    7. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    9. Hidalgo, Javier & Schafgans, Marcia M. A., 2017. "Inference without smoothing for large panels with cross-sectional and temporal dependence," LSE Research Online Documents on Economics 87748, London School of Economics and Political Science, LSE Library.
    10. Javier Hidalgo & Marcia M Schafgans, 2017. "Inference Without Smoothing for Large Panels with Cross- Sectional and Temporal Dependence," STICERD - Econometrics Paper Series 597, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    11. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Journal of Econometrics, Elsevier, vol. 223(1), pages 125-160.

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