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Parameter Identification in a Estimated New Keynesian Open Economy Model

Author

Listed:
  • Adolfson, Malin

    (Monetary Policy Department, Central Bank of Sweden)

  • Lindé, Jesper

    (Division of International Finance)

Abstract

In this paper, we use Monte Carlo methods to study the small sample properties of the classical maximum likelihood (ML) estimator in artificial samples generated by the New- Keynesian open economy DSGE model estimated by Adolfson et al. (2008) with Bayesian techniques. While asymptotic identification tests show that some of the parameters are weakly identified in the model and by the set of observable variables we consider, we document that ML is unbiased and has low MSE for many key parameters if a suitable set of observable variables are included in the estimation. These findings suggest that we can learn a lot about many of the parameters by confronting the model with data, and hence stand in sharp contrast to the conclusions drawn by Canova and Sala (2009) and Iskrev (2008). Encouraged by our results, we estimate the model using classical techniques on actual data, where we use a new simulation based approach to compute the uncertainty bands for the parameters. From a classical viewpoint, ML estimation leads to a significant improvement in fit relative to the log-likelihood computed with the Bayesian posterior median parameters, but at the expense of some the ML estimates being implausible from a microeconomic viewpoint. We interpret these results to imply that the model at hand suffers from a substantial degree of model misspecification. This interpretation is supported by the DSGE-VAR( ) analysis in Adolfson et al. (2008). Accordingly, we conclude that problems with model misspecification, and not primarily weak identification, is the main challenge ahead in developing quantitative macromodels for policy analysis.

Suggested Citation

  • Adolfson, Malin & Lindé, Jesper, 2011. "Parameter Identification in a Estimated New Keynesian Open Economy Model," Working Paper Series 251, Sveriges Riksbank (Central Bank of Sweden).
    • Handle: RePEc:hhs:rbnkwp:0251
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Justiniano & Bruce Preston, 2010. "Monetary policy and uncertainty in an empirical small open‐economy model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 93-128, January.
    2. Lorenzo Burlon & Paolo D'Imperio, 2019. "The euro-area output gap through the lens of a DSGE model," Questioni di Economia e Finanza (Occasional Papers) 477, Bank of Italy, Economic Research and International Relations Area.
    3. Malin Adolfson & Stefan Las√Âen & Jesper Lind√ & Lars E.O. Svensson, 2011. "Optimal Monetary Policy in an Operational Medium-Sized DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(7), pages 1287-1331, October.
    4. Daniel O. Beltran & David Draper, 2018. "Estimating dynamic macroeconomic models: how informative are the data?," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 501-520, February.
    5. Giesen, Sebastian & Scheufele, Rolf, 2016. "Effects of incorrect specification on the finite sample properties of full and limited information estimators in DSGE models," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 1-18.
    6. Van Nguyen, Phuong, 2020. "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers 59, CEPREMAP.
    7. Burlon, Lorenzo & D’Imperio, Paolo, 2020. "Reliable real-time estimates of the euro-area output gap," Journal of Macroeconomics, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Identification; Bayesian estimation; Monte-Carlo methods; Maximum Likelihood estimation; New-Keynesian DSGE Model; Open economy.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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