Published papers

© 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.

© 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.

© 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.

© 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.

Is there asymmetry in the distribution of government bond returns in developed countries? Can asymmetries be predicted using financial and macroeconomic variables? To answer the first question, we provide evidence for asymmetry in government bond returns in particular for short maturities. This finding has important implications for modeling and forecasting government bond returns. For example, widely used models for yield curve analysis such as the affine term structure model assume symmetrically distributed innovations. To answer the second question, we find that liquidity in government bond markets predicts the coefficient of skewness with a positive sign, meaning that the probability of a large and negative excess return is more likely in a less liquid market. In addition, a positive realized return is associated with a negative coefficient of skewness, or a small probability of a large and negative return in the future. © 2013 Elsevier B.V.

Koopman and Shephard (1992). [1] and Segal and Weinstein (1989). [4] propose a formula for calculating the exact score vector for a general form of linear Gaussian state space models. However, for applying their method, one needs to calculate the derivatives of functions with respect to vectors and matrices, which can be intractable in many practical cases. Koopman and Shephard (1992). [1] derive its explicit expression only for a particular case. In this note, we complement Koopman and Shephard (1992). [1] and Segal and Weinstein. [4] by deriving an explicit vector expression of the exact score vector for the general form of linear Gaussian state space models. © 2013.

This paper investigates the spurious effect in forecasting asset returns when signals from technical trading rules are used as predictors. Against economic intuition, the simulation result shows that, even if past information has no predictive power, buy or sell signals based on the difference between the short-period and long-period moving averages of past asset prices can be statistically significant when the forecast horizon is relatively long. The theoretical analysis reveals that both 'momentum' and 'contrarian' strategies can be falsely supported, while the probability of obtaining each result depends on the type of the test statistics employed. © 2012 Elsevier B.V. All rights reserved.

In this article, we develop the asymptotic theory of Hwang and Basawa (2005) for explosive random coefficient autoregressive (ERCA) models. Applying the theory, we prove that a locally best invariant (LBI) test in McCabe and Tremayne (1995), which is for the null of a unit root (UR) process against the alternative of a stochastic unit root (STUR) process, is inconsistent against a class of ERCA models. This class includes a class of STUR processes as special cases. We show, however, that the well-known Dickey-Fuller (DF) UR tests and a LBI test of Lee (1998) are consistent against a particular case of this class of ERCA models. © 2009 Elsevier B.V. All rights reserved.

In this paper, we show that the sequential logit (SL) model, in which a choice process is characterized as a sequence of independent multinomial logit models, is a limiting case of the nested logit (NL) model. For testing the SL model against the NL model, we propose Wald, likelihood ratio and Lagrange multiplier tests after suitably reparameterizing the NL model. It is found that when the NL model parameters are "weakly identified", the Wald test severely underrejects the true model, whereas the sizes of the LR and LM tests are not significantly affected. © Journal compilation © 2009 Japanese Economic Association.

In this paper, we propose a new test for coefficient stability of an AR(1) model against the random coefficient autoregressive model of order 1 neither assuming a stationary nor a non-stationary process under the null hypothesis of a constant coefficient. The proposed test is obtained as a modification of the locally best invariant (LBI) test by Lee [(1998). Coefficient constancy test in a random coefficient autoregressive model. J. Statist. Plann. Inference 74, 93-101]. We examine finite sample properties of the proposed test by Monte Carlo experiments comparing with other existing tests, in particular, the LBI test by McCabe and Tremayne [(1995). Testing a time series for difference stationary. Ann. Statist. 23 (3), 1015-1028], which is for the null of a unit root process against the alternative of a stochastic unit root process. © 2009 Elsevier B.V. All rights reserved.

In this note, I consider a general class of unobserved components (UC) models and derive a relevant inequality. This inequality implies that either of the two assumptions of standard UC models, namely, a random walk trend and uncorrelated shocks, is not satisfied if the impulse response measure introduced by [Campbell, J.Y. and N.G. Mankiw, 1987a. Are output fluctuations transitory? Quarterly Journal of Economics 102, 857-880, Campbell, J.Y. and N.G. Mankiw, 1987b. Permanent and transitory components in macroeconomic fluctuations, American Economic Review (Papers and Proceedings) 77, 111-117] is greater than 1. © 2008 Elsevier B.V. All rights reserved.