We estimate real US GDP growth as a threshold autoregressive process, and construct confidence intervals for the parameter estimates. However, there are various approaches that can be used in constructing the confidence intervals. We construct confidence intervals for the slope coefficients and...

This paper develops an approach to decompose farmland price time series into three components: permanent fundamental component, temporary fundamental component, and nonfundamental component. This decomposition is useful for studying the importance of fundamental versus nonfundamental factors in...

This paper presents the results of a Monte Carlo comparison of feasible GLS estimators of the trend parameter in the linear trend plus noise model, where the noise component may or may not be a unit root process. We include an FGLS estimator that estimates the noise component using a...

This paper presents the results of a Monte Carlo comparison of feasible GLS estimators of the trend parameter in the linear trend plus noise model, where the noise component may or may not be a unit root process. We include an FGLS estimator that estimates the noise component using a...

Optimal designs provide a very efficient way to maximize the amount of information gained in an experiment. For linear models the information is a factor of the covariates, but for non-linear models maximizing the information becomes more difficult because of the relationship of information with...

We estimate real U.S. GDP growth as a threshold autoregressive process, and construct confidence intervals for the parameter estimates. However, there are various approaches that can be used in constructing the confidence intervals. Specifically, standard- t , bootstrap- t , and...

When the central banker’s loss function is asymmetric, changes in the volatility of inflation and/or unemployment affect equilibrium inflation. This suggests that changing macroeconomic volatilities may be an important driving force behind trends in observed inflation. Previous evidence, which...