We introduce continuous-time models that capture the salient features of the short-term interest rate and remain tractable for asset pricing applications. We extend classical specifications within and outside of the affine class to multi-factor settings with latent variables that are readily...

The properties and applications of the normal log-normal (NLN) mixture are considered. The moment of the NLN mixture is shown to be finite for any positive order. The expectations of exponential functions of a NLN mixture variable are also investigated. The kurtosis and skewness of the NLN...

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the yield curve. Despite their wide application in financial economics, very little is known on the time-series properties of the yield-factor volatilities. We examine three common...

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...

This paper extends the linear lasso estimators to non-linear case. We are especially interested in GMM type of Lasso estimators. Lasso estimators are generalization of ridge regression in least squares. With this setup we can deal with identification problem introduced by weak instruments. When...