Jouchi Nakajima

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online.

Generalized extreme value distribution with time-dependence using the AR and MA models in state space form

A new state space approach is proposed to model the time-dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either follow an autoregressive (AR) process or a moving average (MA) process with innovations arising from a Gumbel distribution. Using a Bayesian approach, an efficient algorithm is proposed to implement Markov chain Monte Carlo method where we exploit a very accurate approximation of the Gumbel distribution by a ten-component mixture of normal distributions. The methodology is illustrated using extreme returns of daily stock data. The model is fitted to a monthly series of minimum returns and the empirical results support strong evidence for time-dependence among the observed minimum returns.

Identifying conventional and unconventional monetary policy shocks: a latent threshold approach

Abstract This paper proposes a new estimation framework for identifying monetary policy shocks in both conventional and unconventional policy regimes using a structural VAR model. Exploiting a latent threshold modeling strategy that induces time-varying shrinkage of the parameters, we explore a recursive identification switching with a time-varying overidentification for the interest rate zero lower bound. We empirically analyze Japan’s monetary policy to illustrate the proposed approach for modeling regime-switching between conventional and unconventional monetary policy periods, and find that the proposed model is preferred over a nested standard time-varying parameter VAR model. The estimation results show that increasing bank reserves lowers long-term interest rates in the unconventional policy periods, and that the impulse responses of inflation and the output gap to a bank reserve shock appear to be positive but highly uncertain.

Monetary Policy Transmission under Zero Interest Rates: An Extended Time-Varying Parameter Vector Autoregression Approach

This paper attempts to explore monetary policy transmission under zero interest rates by explicitly incorporating the zero lower bound (ZLB) of nominal interest rates into the time-varying parameter structural vector autoregression model with stochastic volatility (TVP-VAR-ZLB). Nominal interest rates are modeled as censored variables with Tobit-type non-linearity and are incorporated into the TVP-VAR framework. For estimation, an efficient Markov chain Monte Carlo (MCMC) method is constructed in the context of Bayesian inference. The model is applied to Japanese macroeconomic data, including the periods of the zero interest rates policy and the quantitative easing policy. The empirical results show that a dynamic relationship between monetary policy and macroeconomic variables operates through changes in medium-term interest rates rather than policy interest rates under the ZLB. However, the explicit consideration of the ZLB does not otherwise affect macroeconomic dynamics.

Anhedonia in Japanese patients with Parkinson's disease: Analysis using the Snaith-Hamilton Pleasure Scale

BACKGROUND Anhedonia, a lowered ability to experience physical or social pleasure, has recently been recognized as a non-motor symptom of Parkinsons disease. OBJECTIVE To identify the frequency of anhedonia and the factors influencing hedonic tone in Japanese patients with Parkinsons disease. PATIENTS AND METHODS We recruited 86 consecutive outpatients with a clinical diagnosis of PD attending two Japanese hospitals (one university hospital and one community hospital) in February 2010. We used the self-rating Snaith-Hamilton Pleasure Scale (SHAPS) translated into Japanese language from the original English version to assess and quantify hedonic tone as a subjectively experienced phenomenon. We studied the association of anhedonia with the variables age, age at onset, gender, disease duration, disease severity and antiparkinsonian drugs. RESULTS Thirty-nine patients (45%) were male and 47 (55%) were female. Mean age was 72.01±9.07 (49-89) years, with mean age at onset of 64.93±11.42 (31-88) years. Mean disease duration was 7.20±5.54 (1-23) years. The mean Hoehn and Yahr scale was 2.76±0.78. The mean SHAPS score of the total sample was 1.19±1.86. The SHAPS score of 14 patients (16.3%) was 3 or more, indicating anhedonia. The mean SHAPS score was lower in patients taking pramipexole (0.58±0.97) than in patients not taking pramipexole (1.57±2.16). Multiple linear regression analysis identified pramipexole as a significant negative influencing factor on the SHAPS score, while disease severity and entacapone treatment were identified as positive influencing factors. The age, onset age, gender, disease duration, and use of pergolide, amantadine, zonisamide, selegiline, anticholinergic agents and droxidopa did not significantly affect the SHAPS score. CONCLUSION Anhedonia is not rare non-motor symptom in Japanese patients with Parkinsons disease. This study suggests an anti-anhedonic property of pramipexole.

Deteriorating Bank Health and Lending in Japan: Evidence from Unlisted Companies under Financial Distress

Abstract When a borrower faces a hold-up problem, deteriorating bank health might reduce a borrowers credit availability. However, a bank with an impaired balance-sheet might attempt to ‘gamble for resurrection’ and hence might increase risky lending to zombie firms. The purpose of this paper is to investigate what impacts weakened financial conditions of banks had on loans outstanding to medium size firms in Japan. Estimating lending functions, we examine the determinants of lending to unlisted Japanese companies in the late 1990s and the early 2000s. We find that two alternative measures of the bank health, regulatory capital adequacy ratios and ratios of non-performing loans (NPLs), had opposite impacts on lending. In the case of regulatory capital adequacy ratios, its deterioration had a perverse impact on the banks lending. The deteriorating NPL ratios, however, increased lending to troubled firms to keep otherwise economically bankrupt firms alive.

Dynamic network signal processing using latent threshold models

We discuss multivariate time series signal processing that exploits a recently introduced approach to dynamic sparsity modelling based on latent thresholding. This methodology induces time-varying patterns of zeros in state parameters that define both directed and undirected associations between individual time series, so generating statistical representations of the dynamic network relationships among the series. Following an overview of model contexts and Bayesian analysis for dynamic latent thresholding, we exemplify the approach in two studies: one of foreign currency exchange rate (FX) signal processing, and one in evaluating dynamics in multiple electroencephalography (EEG) signals. These studies exemplify the utility of dynamic latent threshold modelling in revealing interpretable, data-driven dynamics in patterns of network relationships in multivariate time series.

Bayesian analysis of multivariate stochastic volatility with skew return distribution

ABSTRACT Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.

Bayesian Analysis of Generalized Autoregressive Conditional Heteroskedasticity and Stochastic Volatility: Modeling Leverage, Jumps and Heavy‐Tails for Financial Time Series

This paper develops a Bayesian model comparison of two broad major classes of varying volatility model, the generalized autoregressive conditional heteroskedasticity and stochastic volatility models, on financial time series. The leverage effect, jumps and heavy‐tailed errors are incorporated into the two models. For estimation, the efficient Markov chain Monte Carlo methods are developed and the model comparisons are examined based on the marginal likelihood. The empirical analyses are illustrated using the daily return data of US stock indices, individual securities and exchange rates of UK sterling and Japanese yen against the US dollar. The estimation results indicate that the stochastic volatility model with leverage and Student‐t errors yield the best performance among the competing models.

Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.