Decomposition of Ex Ante State Model Forecasting Errors

Charney, Alberta H.; Taylor, Carol A.

doi:10.1111/j.1467-9787.1984.tb01034.x

Cited by 17 publications

(7 citation statements)

References 15 publications

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“…Dynamic out-of-sample forecasting experiments have been reported for a host of other models in the literature I:e.g., Charney and Taylor (1984) ; Duobinis (1981); Glennon, Lane, and Johnson (1987); Latham, Lewis, and Landon (19791, Liu and Stocks (1983); Rubin and Eriekson (1980); and Taylor (198211 and could be compared to the ECM and BECM results presented here. But the point here is to simply note that the performance of the methods described here is quite comparable to that from other regional econometric models despite its relative simplicity and low cost.…”

Section: Forecasting Experiments With the Modelmentioning

confidence: 89%

Forecasting Metropolitan Employment Using an Export‐base Error‐corbection Model

LeSage

1990

Journal of Regional Science

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ABSTRGCT. This paper shows that a time-series version of export-based location quotients, which decompose area employment into export and local components are co-integrated. Given the statistically established co-integrating relation between the time series for export and local employment, the Granger Representation Theorem tells us that an error-correction mechanism model is the appropriate time-series model specification. A set of forecasting experiments is carried out, in order to test whether the error-correction model produces better long-run forecasts that hold together in economically meaningful ways. Given the computer hardware and software that currently exist, the model developed here represents a simple, low-cost method for accurately forecasting local-area employment in the tradition of past work on export-base models.

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Section: Forecasting Experiments With the Modelmentioning

confidence: 89%

Forecasting Metropolitan Employment Using an Export‐base Error‐corbection Model

LeSage

1990

Journal of Regional Science

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“…This is perhaps not surprising. As discussed by Charney and Taylor [1984], econometric forecast errors tend to be large for regions where demographic aggregates are subject to substantial revision. Evidence given by West [2003] further indicates that forecast accuracy is difficult to obtain in regions characterized by relatively high rates of unemployment (or underemployment).…”

Section: Resultsmentioning

confidence: 99%

“…That is achieved by including one period lags of the water meters in the specifications for water customers. Per customer usage forecasts, however, cannot be shielded from the reverberation error impacts of population data revisions and high rates of unemployment and/or underemployment [ Charney and Taylor , 1984; West , 2003].…”

Section: Methodsmentioning

confidence: 99%

“…For the three municipal water systems for which forecasts are published every year, three additional observations are relevant to out‐of‐sample predictive accuracy prospects. As noted by Charney and Taylor [1984], population variable estimate revisions frequently lead to notable levels of predictive inaccuracy in regional econometric forecasts. Beyond that, regions with high rates of unemployment also tend to exhibit greater levels of forecast inaccuracy [ West , 2003].…”

Section: Previous Studiesmentioning

confidence: 99%

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Municipal water consumption forecast accuracy

Fullerton

Molina

2010

Water Resources Research

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[1] Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

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“…Of note here is the difficulty in forecasting interest rates, a particularly problematic issue given the interest sensitivity of residential construction (Cooper and Nelson 1972;McKnees 1992). Second, local construction sector dependency on forecast local population growth may introduce considerable reverberative error in out-ofsample model simulations (Charney and Taylor 1984). In rapidly growing states like Florida, net migration is the principal component underlying population expansion.…”

Section: Resultsmentioning

confidence: 99%

Regional Econometric Housing Start Forecast Accuracy in Florida

Fullerton¹,

West²

1998

Review of Regional Studies

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Regional econometric forecasting accuracy assessment has traditionally received less attention than its national macroeconometric counterpart. While evidence is available that state and local employment forecasts perform well relative to standard benchmarks, little is known about the historical performance of widely cited regional housing start forecasts. This paper attempts to fill partially that gap in the literature by assessing the accuracy of previously published residential construction forecasts for Florida and its six largest metropolitan areas. Results indicate that regional econometric housing start forecasts are less reliable than regional employment structural model forecasts.

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Decomposition of Ex Ante State Model Forecasting Errors

Cited by 17 publications

References 15 publications

Forecasting Metropolitan Employment Using an Export‐base Error‐corbection Model

Forecasting Metropolitan Employment Using an Export‐base Error‐corbection Model

Municipal water consumption forecast accuracy

Regional Econometric Housing Start Forecast Accuracy in Florida

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