Water consumption, Applied econometrics, Mexico, Q25,

Water utility planning efforts are becoming increasingly difficult throughout the world. Located in a semi-arid region, Ciudad Juárez, Mexico is a fast growing municipality that faces both physical infrastructure and water supply constraints. This paper examines monthly water consumption in Ciudad Juárez utilizing a linear transfer function procedure (LTF). Analysis is carried out for per customer usage and for the total number of municipal water system accounts. Models estimated for both series are subjected to a series of simulation benchmark tests. Findings suggest that water consumption in Ciudad Juárez reacts quickly to changes in economic and weather conditions. Out-of-sample simulation results are mixed. Per customer usage forecasts do not fare as well those for total customers relative to random walk benchmarks. Copyright International Atlantic Economic Society 2006Q25, water consumption, applied econometrics, Mexico,

No abstract

Econometric prediction accuracy for personal income forecasts is examined for a region of the United States. Previously published regional structural equation model (RSEM) forecasts exist ex ante for the state of New Mexico and its three largest metropolitan statistical areas: Albuquerque, Las Cruces and Santa Fe. Quarterly data between 1983 and 2000 are utilized at the state level. For Albuquerque, annual data from 1983 through 1999 are used. For Las Cruces and Santa Fe, annual data from 1990 through 1999 are employed. Univariate time series, vector autoregressions and random walks are used as the comparison criteria against structural equation simulations. Results indicate that ex ante RSEM forecasts achieved higher accuracy than those simulations associated with univariate ARIMA and random walk benchmarks for the state of New Mexico. The track records of the structural econometric models for Albuquerque, Las Cruces and Santa Fe are less impressive. In some cases, VAR benchmarks prove more reliable than RSEM income forecasts. In other cases, the RSEM forecasts are less accurate than random walk alternatives. Copyright © 2005 John Wiley & Sons, Ltd.