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Implications of Partial Information for Applied Macroeconomic Modelling

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  • Adrian Pagan

    (School of Economics, University of Sydney, CAMA, Australian National University)

  • Tim Robinson

    (Melbourne Institute: Applied Economic & Social Research, The University of Melbourne)

Abstract

Implications of partial information for applied macroeconomic modelling along four dimensions are shown, and analysis provided on how they can be addressed. First, when permanent shocks are present a Vector Error-Correction Model including latent, as well as observed, variables is required to capture macroeconomic dynamics. Second, the assumption in Dynamic Stochastic General Equilibrium models that shocks are autocorrelated provides identifying information usable in Structural Vector AutoRe-gressions. Third, estimating models with more shocks than observed variables must yield correlated estimated structural shocks. Fourth, including measurement error, as commonly specified, implies a lack of co-integration between variables, even when actually present

Suggested Citation

  • Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2019n12
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    File URL: https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0006/3202629/wp2019n12.pdf
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    References listed on IDEAS

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    Cited by:

    1. Pagan, Adrian & Robinson, Tim, 2022. "Excess shocks can limit the economic interpretation," European Economic Review, Elsevier, vol. 145(C).
    2. Adrian Pagan & Michael Wickens, 2019. "Checking If the Straitjacket Fits," CAMA Working Papers 2019-81, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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