Bayesian comparison of production function-based and time-series GDP models

Jacek Osiewalski, Justyna Wróblewska () and Kamil Makieła

Empirical Economics, 2020, vol. 58, issue 3, No 18, 1355-1380

Abstract: Abstract A purely Bayesian vector autoregression (VAR) framework is proposed to formulate and compare tri-variate models for the logs of the economy-wide aggregates of output and inputs (physical capital and labour). The framework is derived based on the theory of the aggregate production function, but at the same time, accounts for the dynamic properties of macroeconomic data, which makes it particularly appealing for modelling GDP. Next, using the proposed framework we confront a-theoretical time-series models with those that are based on aggregate production function-type relations. The common knowledge about capital and labour elasticities of output as well as on their sum is used in order to formulate prior distribution for each tri-variate model, favouring the linearly homogenous Cobb–Douglas production function-type relation. In spite of this, production function-based co-integration models fail empirical comparisons with simple VAR structures, which describe the three aggregates by three stochastic trends.

Keywords: Bayesian inference; VAR models; Economic growth models; Co-integration analysis; Aggregate production function; Potential output (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 O40 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00181-018-1575-8

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