𝔖 Bobbio Scriptorium
✦   LIBER   ✦

The bullionist controversy: a time-series analysis

✍ Scribed by Lawrence H. Officer


Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
256 KB
Volume
5
Category
Article
ISSN
1076-9307

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Forecasting accounting data: A multiple
✍ S. C. Hillmer; D. F. Larcker; D. A. Schroeder πŸ“‚ Article πŸ“… 1983 πŸ› John Wiley and Sons 🌐 English βš– 938 KB

## Abstract This paper examines the relative forecasting performance of multivariate time‐series analysis. One hundred consecutive monthly observations for three accounting series were obtained from a manufacturing division of a large corporation. Regression, univariate time‐series, transfer‐functi

US health services employment: A time se
✍ Michael Kendix; Thomas E. Getzen πŸ“‚ Article πŸ“… 1994 πŸ› John Wiley and Sons 🌐 English βš– 891 KB

The growth of health services employment in the United States is modelled using ARIMA analysis, and related to the growth in total U.S. employment. It is argued that specific features of the medical care sector (licensed professional manpower, non-profit firms, third-party financing) create institut

A time series analysis on the seasonalit
✍ Masatsugu Wakaura; Yosihiko Ogata πŸ“‚ Article πŸ“… 2007 πŸ› John Wiley and Sons 🌐 English βš– 500 KB

## Abstract Surface air temperature anomalies relative to seasonal variations are of great concern from a long‐term forecasting perspective. This article is concerned with marginally normalized time series in which the original data of the temperatures are standardized using the mean values and var

On Selecting a Power Transformation in T
✍ Cathy W. S. Chen; Jack C. Lee πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 214 KB

The primary aim of this paper is to select an appropriate power transformation when we use ARMA models for a given time series. We propose a Bayesian procedure for estimating the power transformation as well as other parameters in time series models. The posterior distributions of interest are obtai