Working Papers 1998 – Abstracts

1/98
U.S. deficit sustainability: A new approach based on multiple endogenous breaks
Gael M. Martin

Recent empirical work has questioned the consistency of U.S. fiscal policy with an intertemporal budget constraint. Empirical results have tended to indicate that the deficit process has undergone at least one structural shift during recent decades, with the deficit becoming either unsustainable or sustainable in only a weak sense in the post-shift period. In this paper, we re-examine sustainability using a new approach, based on a cointegration model with multiple endogenous breaks. A Bayesian methodology is applied, incorporating Markov chain Monte Carlo simulators. In contrast to previous analyses, we find evidence of a sustainable deficit process over the 1947 to 1992 period, despite the occurrence of breaks during the 1970's and 1980's.

2/98
Estimating long-term trends in tropospheric ozone levels
Michael Smith, Paul Yau, Thomas Shively and Robert Kohn.

This paper estimates the long-term trends in the daily maxima of tropospheric ozone at six sites around the state of Texas. The statistical methodology we use controls for the effects of meteorological variables because it is known that variables such as temperature, wind speed and humidity substantially affect the formation of tropospheric ozone. A nonparametric regression model is estimated in which a general trivariate surface is used to model the relationship between ozone and these meteorological variables because there is little, or no, theory to specify the functional dependence of ozone on these variables. The model also allows for the effects of wind direction and seasonality. Each function in the model is represented as a linear combination of basis functions located at all the design points. A trivariate basis is used for the function representing the combined effect of temperature, wind speed and humidity, while univariate bases are used to represent the other functions in the model. To estimate the functions nonparametrically we use a Bayesian hierarchical framework with a fractional prior. Due to the high dimensional representation of the signal a Markov chain Monto Carlo sampling scheme, employing Gibbs sub-chains that `focus' on the basis terms that are most likely to contribute to the signal, is used to carry out the computations. We also estimate an appropriate data transformation simultaneously with the function estimates. The empirical results indicate that key meteorological variables explain most of the variation in daily ozone maxima through a non linear interaction and that their effects are consistent across the six sites. However, the estimated trends vary considerably from site to site, even within the same city. A simulation based on the design of the data indicates that the Bayesian approach is substantially more efficient that MARS (Friedman, 1991).

3/98
Exponential smoothing methods of forecasting and general ARMA time series representations
Roland G Shami and Ralph D.Snyder

The focus of this paper is on the relationship between the exponential smoothing methods of forecasting and the integrated autoregressive-moving average models underlying them. In this paper we derive, for the first time, the general linear relationship between their parameters. A method, suitable for implementation on computer, is proposed to determine the pertinent quantities in this relationship. It is illustrated on common forms of exponential smoothing. It is also applied to a new seasonal form of exponential smoothing with seasonal indexes which always sum to zero.

Next Abstract