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An unobserved components model of total factor productivity and the relative price of investment

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  • Chan, Joshua C.C.
  • Wemy, Edouard

Abstract

This paper applies the common stochastic trends representation approach to the time series of total factor productivity (TFP) and the relative price of investment (RPI) to investigate the modeling of neutral technology (NT) and investment-specific technology (IST) and its econometric ramifications on the analysis of general equilibrium model. The permanent and transitory movements in both series are estimated efficiently via Markov chain Monte Carlo methods using band matrix algorithms. The results indicate that TFP and the RPI are, each, well represented by a differenced first-order autoregressive process. In addition, their time series share a common trend component that we interpret as reflecting changes in general purpose technology. These results are consistent with studies that suggest that (1) the traditional view of assuming that NT and IST follow independent processes is not supported by the features of the time series and (2) improper specification of secular trends may distort estimation and inference. Notably, the findings provide some guidance to minimize the effect of idiosyncratic and common trend misspecifications on the analysis of impulse dynamics and propagation mechanisms in macroeconomic models.

Suggested Citation

  • Chan, Joshua C.C. & Wemy, Edouard, 2023. "An unobserved components model of total factor productivity and the relative price of investment," Macroeconomic Dynamics, Cambridge University Press, vol. 27(5), pages 1397-1423, July.
  • Handle: RePEc:cup:macdyn:v:27:y:2023:i:5:p:1397-1423_9
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    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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