Predicting Future Forestland Area: A Comparison of Econometric Approaches

By Soeun Ahn, Andrew J. Plantinga, R. Alig. Forestry sciences, 2000

70 citations recorded by the source corpus.

. Predictions of future forestland area are an important component of forest policy analyses. In this article, we test the ability of econometric land use models to accurately forecast forest area. We construct a panel data set for Alabama consisting of county and time-series observation for the period 1964 to 1992. We estimate models using restricted data sets—namely, data from early periods—and use out-of-sample values of dependent and independent variables to construct precise tests of the model's forecasting accuracy. Three model specifications are examined: ordinary least squares, dummy variables (fixed effects), and error components (random effects). We find that the dummy variables model produces more accurate forecasts at the county and state level than the other model specifications. This result is related to the ability of the dummy variables model to more completely control for cross-sectional variation in the dependent variables. This suggests that the estimated model parameters better capture the temporal relationship between forest area and economic variables. FOR. SCI. 46(3): 363–376.

Subfields: Economics and Econometrics, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/74c36f059b67eafbe60e77f923f38da56ebdfccb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Junjie Wu
2. Andrew J. Plantinga
3. D. Lewis

In Global and Planetary Change

1. Junjie Wu
2. Andrew J. Plantinga
3. D. Lewis

In Nature and Landscape Conservation

1. Junjie Wu
2. Andrew J. Plantinga

Citations in the corpus

• Co-Benefits from Carbon Sequestration in Forests: Evaluating Reductions in Agricultural Externalities from an Afforestation Policy in Wisconsin (2003, methodology): “The model [1] is formally derived from the individual landowner’s decision rule in Ahn, Plantinga, and Alig (2000), which draws on earlier work in Miller and Plantinga (1999) and Plantinga (1996).”