Kai Sun
;
Daniel J. Henderson
;
Subal C. Kumbhakar
You're currently viewing an old version of this dataset. To see the current version, click here.

biases in approximating log production (replication data)

Most empirical work in economic growth assumes either a Cobb-Douglas production function expressed in logs or a log-approximated constant elasticity of substitution specification. Estimates from each are likely biased due to logging the model and the latter can also suffer from approximation bias. We illustrate this with a successful replication of Masanjala and Papagerogiou (The Solow model with CES technology: nonlinearities and parameter heterogeneity, Journal of Applied Econometrics 2004; 19: 171-201) and then estimate both models in levels to avoid these biases. Our estimation in levels gives results in line with conventional wisdom.

Data and Resources

This dataset has no data

Suggested Citation

Sun, Kai; Henderson, Daniel J.; Kumbhakar, Subal C. (2011): Biases in approximating log production (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://journaldata.zbw.eu/dataset/biases-in-approximating-log-production?activity_id=77456d8c-c9ad-4961-95da-d9d94f4caf5e