Template-Type: ReDIF-Paper 1.0 
Title: Measuring the uncertainty of shadow economy estimates using Bayesian and frequentist model averaging
Author-Name: Piotr Dybka 
Author-Name: Bartosz Olesiński 
Author-Name: Marek Rozkrut 
Author-Name: Andrzej Torój 
Abstract: Economic literature provides little discussion on the uncertainty around the macroeconometric shadoweconomy estimates. We fill this gap by deriving the measurement error of the shadow economy estimates stemming from the model uncertainty by using frequentist and Bayesian model averagingtechniques. This allows us to make useful insights into the optimal selection of regressors within theCurrency Demand Analysis (CDA) framework, basing on the marginal probabilities that the selectedvariables are included in the ''true'' model. Hence, we provide the CDA researchers with an additional guidance with respect to the selection of shadow economy determinants that makes CDA-basedshadow economy measurements less arbitrary. Our results show that the selection of regressors canhave a material and highly country-specific impact on the estimated level of the shadow economy. In consequence, one cannot attribute the same level of uncertainty to every country across the panel.We use our results to demonstrate the average shadow economy estimates as of 2014 for 64 countries,along with the confidence intervals
Number: 2020-046
Length: 31 pages 
Creation-Date: 2020-03
Keywords: shadow economy, currency demand approach, measurement error, confidence intervals
Classification-JEL: C10, C51, C59, E26, H26, O17
File-URL: https://hdl.handle.net/20.500.12182/1098
File-Format: Application/pdf
DOI: 10.33119/kaewps2020046
Handle: RePEc:sgh:kaewps:2020046