Parameter Recovery Analysis with Remotely Sensed Variables

A 2026 replication package for an econometric analysis of bias in parameter estimates from remotely sensed variables. The dataset, created by Jonathan Proctor, supports the paper 'Parameter Recovery Using Remotely Sensed Variables' and includes code and documentation for replicating the study. It reanalyzes four prior papers to establish stylized facts about measurement errors in satellite data.

Use Cases

• Assessing coefficient bias and coverage rates based on non-classical errors in remote sensing variables.
• Applying multiple imputation techniques to reduce bias in analyses using satellite data as regressors.
• Replicating econometric findings on parameter recovery from four prior environmental economics studies.

Strengths

• Includes full replication code and documentation for a 2026 peer-reviewed journal article.
• Based on a large dataset of diverse remotely sensed variables as described in the abstract.
• Demonstrates a specific, implementable correction method (multiple imputation) for measurement error.

Limitations

• Two restricted-access datasets are excluded from this package, requiring separate access for full replication.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Journal of the Association of Environmental and Resource Economists Dataverse

Collection Method

Likely compiled from diverse remotely sensed data sources and prior published studies for reanalysis.

Freshness

Last updated 2026-06-03 12:54:24