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Critically assessing estimated DSGE models: A case study of a multi-sector model

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Abstract

We describe methods for assessing estimated Dynamic Stochastic General Equilibrium (DSGE) models. One involves the computation of alternative impulse responses from models constrained to have an identical likelihood and the same contemporaneous signs as responses in the DSGE model. Others ask how well the model matches the data generating process; whether there is weak identification; the consequences of including measurement error with growth rates of non-stationary variables; and whether the model can reproduce features of the data that involve combinations of moments. The methods are applied to a large-scale small-open economy DSGE model, typical of those used at policy institutions.

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  • X. Liu & A.R. Pagan & T. Robinson, 2018. "Critically assessing estimated DSGE models: A case study of a multi-sector model," CAMA Working Papers 2018-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2018-04
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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. Daniel Buncic & Adrian Pagan & Tim Robinson, 2023. "Recovering stars in macroeconomics," Melbourne Institute Working Paper Series wp2023n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    3. Christopher G Gibbs & Jonathan Hambur & Gabriela Nodari, 2018. "DSGE Reno: Adding a Housing Block to a Small Open Economy Model," RBA Research Discussion Papers rdp2018-04, Reserve Bank of Australia.
    4. Adrian Pagan & Tim Robinson, 2020. "Too Many Shocks Spoil the Interpretation," Melbourne Institute Working Paper Series wp2020n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Georgiadis, Georgios & Jančoková, Martina, 2020. "Financial globalisation, monetary policy spillovers and macro-modelling: Tales from 1001 shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    6. Sam Ouliaris & Adrian Pagan, 2022. "Three Basic Issues that Arise when Using Informational Restrictions in SVARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 1-20, February.
    7. 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.
    8. Wickens, Michael R. & Pagan, Adrian, 2019. "Checking if the Straitjacket Fits," CEPR Discussion Papers 14140, C.E.P.R. Discussion Papers.
    9. Christopher G. Gibbs & Jonathan Hambur & Gabriela Nodari, 2021. "Housing and Commodity Investment Booms in a Small Open Economy," The Economic Record, The Economic Society of Australia, vol. 97(317), pages 212-242, June.
    10. Xianglong Liu & Adrian R. Pagan & Tim Robinson, 2018. "Critically Assessing Estimated DSGE Models: A Case Study of a Multi‐sector Model," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 349-371, December.

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