Details of a Researcher - Nagakura Daisuke

Nagakura Daisuke

写真a

Affiliation: Faculty of Economics （Mita）

Position: Professor

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Message from the Faculty Member 【 Display / hide 】

私の研究分野は計量経済学(Econometrics)、金融計量経済学(Financial Econometrics)、時系列分析(Time Series Analysis)です。

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Career 【 Display / hide 】

2016.04

-

Present

慶應義塾大学経済学部, 教授
2011.04

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2016.03

Associate Professor, Department of Economics, Keio University, 准教授
2010.04

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2011.03

Graduate School of Finance, Accounting, and Law, Waseda University, 助教
2007.09

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2010.03

Institute for Monetary and Economic Studies, Bank of Japan

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Academic Background 【 Display / hide 】

2007.08

University of Washington, Department of Economics

United States, Graduate School, Completed, Doctoral course
2001.03

Yokohama National University, 国際社会科学研究科

Graduate School, Completed, Master's course
1999.03

Yokohama National University, Faculty of Economics

University, Graduated

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Academic Degrees 【 Display / hide 】

Ph.D in Economics, University of Washington, Coursework, 2007.08

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Papers 【 Display / hide 】

Testing for Random Coefficient Autoregressive and Stochastic Unit Root Models

Daisuke Nagakura

Studies in Nonlinear Dynamics and Econometrics 2021

Research paper (scientific journal), Single Work, Accepted
Computing exact score vectors for linear Gaussian state space models

Daisuke Nagakura

Communications in Statistics - Simulation and Computation forthcoming 2021

Research paper (scientific journal), Single Work, Accepted, ISSN 03610918

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© 2019, © 2019 Taylor & Francis Group, LLC. A recursive formula for computing the exact value of score vectors is proposed for a general form of the linear Gaussian state space model, which is more desirable than approximate values in some statistical analyses. Unlike most extant methods, our formula calculates all components of the score vector simultaneously. This approach significantly simplifies its programing, in particular, with some matrix-oriented programing languages, such as MATLAB. We also consider a way of handling initial conditions that depend on unknown parameters. This issue has not yet been explicitly addressed in the existing literature in the context of exact score computing for a general case, such as the one that we consider in this paper. It is also shown that our formula is especially useful for calculating score tests with an outer product of gradient asymptotic covariance matrix estimator.
- Access to Document (DOI)
Further Results on the vecd Operator and Its Applications

Daisuke Nagakura

Communications in Statistics, Theory and Methods 49 （ 10 ） 2321 - 2338 2020

Research paper (scientific journal), Single Work, Accepted, ISSN 03610926

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© 2019, © 2019 Taylor & Francis Group, LLC. In this paper, we consider the matrix vectorization operator termed the vecd operator, which has recently been introduced in the literature. This operator stacks up distinct elements of a symmetric matrix in a way that differs from that of the well-known vech operator; it stacks up not columns, but diagonals. We give further consideration to the vecd operator and related matrices, and derive their various useful properties. We provide some statistical applications of the vecd operator to illustrate its usefulness.
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On the Relationship between the Matrix Operators, vech and vecd

Daisuke Nagakura

Communications in Statistics, Theory and Method 47 （ 13 ） 3252 - 3268 2018

Research paper (scientific journal), Single Work, Accepted, ISSN 03610926

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© 2018 Taylor & Francis Group, LLC. We introduce a matrix operator, which we call “vecd” operator. This operator stacks up “diagonals” of a symmetric matrix. This operator is more convenient for some statistical analyses than the commonly used “vech” operator. We show an explicit relationship between the vecd and vech operators. Using this relationship, various properties of the vecd operator are derived. As applications of the vecd operator, we derive concise and explicit expressions of the Wald and score tests for equal variances of a multivariate normal distribution and for the diagonality of variance coefficient matrices in a multivariate generalized autoregressive conditional heteroscedastic (GARCH) model, respectively.
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A State Space Approach to Estimating the Integrated Variance under the Existence of Market Microstructure Noise

Daisuke Nagakura (with Toshiaki Watanabe)

Journal of Financial Econometrics 13 （ 1 ） 45 - 82 2015

Research paper (scientific journal), Joint Work, Accepted, ISSN 14798409

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© The Author, 2013. This article develops a state space method for estimating the integrated variance under the existence of market microstructure noise (MMN). Our method is based on a state space representation of the noisecontaminated RV (NCRV), namely, the realized variance (RV) calculated with observed prices contaminated by MMNs. The main idea of our method is to filter out the bias component, which we call the microstructure noise (MN) component, from the NCRV using the Kalman filter. We apply the proposed method to yen/dollar exchange rate data.We find that about half of the variation in NCRV is because of the MN component. The proposed method can serve as a convenient way to estimate a general class of continuous-time stochastic volatility models under the existence of MMN.
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