The paper applies methods of functional data analysis – functional auto-regression, principal components and canonical correlations – to the study of the dynamics of interest rate curve. In addition, it introduces a novel statistical tool based on the singular value decomposition...

The recently proposed family of hypernormal density functions possess the analytically convenient and computationally efficient property of closed form moments and anti-derivatives in the univariate case. While this result allows many univariate applications to be solved faster and/or more...

It is now widely understood how to obtain first-order accurate approximations to the solution to a dynamic, stochastic general equilibrium model (DSGE model). Such solutions are fairly easy to construct and useful for a wide variety of purposes. They are likely to be accurate enough to be a...

In times of low-inflation, conventional monetary policy is perpetually exposed to the risk of being caught by the liquidity trap. As a part of a pre-emptive monetary policy to avoid the liquidity trap, many economists have pointed out that this risk can be possibly circumvented by targeting a...

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment (and from a ``timeless...

Understanding and forecasting financial time series depend crucially on identifying any non-linearity which may be present. Recent developments in tests for non-linearity very commonly display low power, most likely because of over-smoothing and discarding pertinent information. In this...

In this paper, we consider testing marginal distributional assumptions. Special cases that we consider are the Pearson's family like the Gaussian, Student, Gamma, Beta and uniform distributions. The test statistics we consider are based on the first moment conditions derived by Hansen and...

There is a growing literature on unit root testing in threshold autoregressive models. This paper makes two contributions to the literature. First, an asymptotic theory is developed for unit root testing in a threshold autoregression, in which the errors are allowed to be dependent and...

Techniques for simulated maximum likelihood (SML) estimation, filtering, and assessing the fit of stochastic volatility models are examined. Both one- and two-factor models (with leverage effects) are considered. The techniques are computationally efficient, robust, straightforward to implement,...

This paper develops a dynamic stochastic model to examine the joint patent application and renewal behavior under an international patent protection regime. This framework makes it possible to utilize both the cross-sectional (multi-country filing) and the time-series (patent renewal) dimensions...