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Abstract | PDF (864 kilobytes)

This paper provides a novel approach to extract the trend and seasonal components
from panel data consisting of individual entries showing both strong trend and seasonality. For
such a data set, the usual principal component analysis generally fails to disentangle them. In the
paper, we suggest a methodology to separately identify them using the Hodrick-Prescott filter that is
commonly and widely used to remove trends in various economic data. We apply our methodology
to a food product sales panel data and show that it effectively disentangles the trend and seasonal
components in the data set.

Abstract | PDF (268 kilobytes)

We revisit Morris (2001) two-period advice game but with communication
error, and investigate how communication error affects the advisor’s
reputational incentives and thus her information transmission. Reputational incentives
differ from Morris (2001) and critically depend on the structure of noise
and specific equilibrium strategies. We borrow the notion of “plausible deniability”
by Blume et al. (2019) and explain the effects of communication error on
information transmission and welfare. We show that the weakened reputational
incentives reduce the good type advisor’s incentive to lie, compared to Morris
(2001).

Abstract | PDF (229 kilobytes)

We consider a series of signaling models of experience goods. In
the first model, the seller attempts to signal its quality by introductory pricing
in the trial phase in the hope of future profit. We identify plausible forms of
equilibria by applying the intuitive criterion. Then we expand the model horizon
to examine what happens after the trial phase. We show that when the product
is durable and requires costly maintenance, the price alone is not effective as a
signal of the seller’s long-livedness. A subscription scheme is suggested as an
effective instrument for ensuring long-term transaction. We also discuss interaction
between the two phases. These models can illuminate on recent business
practices, e.g. in the mobile applications market.

Abstract | PDF (3878 kilobytes)

We propose a new Bayesian Markov chain Monte Carlo algorithm
for continuous time GARCH diffusion limit stochastic volatility models and
demonstrate the performance of our algorithm through simulation experiments
and empirical analyses. Our algorithm exploits the normal distribution approximation
of the posterior density of conditional variance using the one-step Newton-
Raphson algorithm. Our algorithm can be applied not only to the continuous
time GARCH diffusion limit stochastic volatility model but also to the continuous
time stochastic volatility models of which the marginal probability density
functions or the probability kernels of them are known. We present an empirical
analysis results of the Feller square-root stochastic volatility model as well
as the continuous time GARCH diffusion limit stochastic volatility model as an
exhibition of the generality of our MCMC algorithm.