The Best Way To Correct Measurement Errors When Evaluating Dynare

Sometimes your computer may display an error indicating that a dynare Estimate measurement error has occurred. This problem can have several causes.

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    Your Guide to Defining Observational Equations for Evaluating DSGE Models lists 3 reasons for adding measurement errors.

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  • May I ask if there is a reason for tightening – not a conceptual/variable model that typically maps data, but simply using observed data as a proxy… like in the RED paper “Investment Shocks and These Relative Investment Prices”, in the article, marketers negotiate data scatter with MEI surprise using the following observed equation,

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    spread_obs=scale parameter* MEI shock+ Error

    be ready for it.
    dynare estimation measurement error

    Does your measurement error seem to contribute to the discrepancy between real data and even indirect data? Is this the reason for adding measurement error to Independence Day?

    Dilemmas Of Measurement, Stoch_simul And Evaluation

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    Measurement Error 4:47, Stoch_simul And Estimate

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    (1) 50 Measurement errors included. Deviations with them are not part of these parameters that need to be evaluated, so I include them in the hitting part. Is this the correct way to include measurement errors?

    (2) Role of stoch_simul before evaluation. The .mod works fine when I am in front of the evaluation command with stoch_simul. Without stoch_simul errors appear:

    (3) Where can I find the use of the output of the Monte Carlo set optimization procedure (mode_compute=6).

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    Re: Measurement Error And Stoch_simul Estimation

    1. Yes, this is how the measurement error during calibration is corrected

    2. In Dynare 4.5 I can’treproduce this issue. This appears to be due to a bug Dynare plugged into qz_criterium in 4.4.3, since your celebrity has a unit root. This selects

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    Measurement Error Is Always At The Upper Limit

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    Messaging: Sat May 16, 2015 4:15 AM

    Measurement Error, Be Careful, Reach The Upper Limit

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    Whenever I add measurement errors as shocks to a model that is likely to be estimated, the inverse standard deviation of the measurement error is usually the corresponding upper bound.

    In this scenario, mode_compute=4 or 9 will probably fail from time to time because the Hessian is not positive definite. I can then use mode_compute=6 to start MCMC which is the only way to do this

    Solve a problem. Will such MCMC chains ultimately converge unambiguously and the results be reliable? You will find 3 convergence figures attached and therefore 1 post hoc plot and 1 method control plot.

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    Re: Measurement Error Always Reaches Upper Limit

    dynare estimation measurement error

    Unfortunately, my problem is common. Any incorrect machine specification can lead to measurement errors. Because most updates contain blunders, the predicted measurement error tends to be huge. The measurement occurs regularly, so that the error reaches the upper limit when each limit is relatively small. This is the case, for example, in Schmitt-Grohe/Uribe (2012) “What’s New in Business Cycles” map and in Garcia-Cicco and (2010) AER with the performance error mentioned in the article (see

    However, even if its mode is at its limit, the global distribution of posterior parameters is, as a rule, notoriously degenerate. If MCMC is run which has a real covariance matrix, it is correctly practiced from behind. This, of course, would simply be inefficient; great efforts to achieve convergence. Instead of a link

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    You can expect to beuse it directly

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    Option (see manual).

    Another style is to use a well-informed multimeter prior error that shifts the posterior to this lower limit.101


    Messaging: Sat May 15, 2015 4:15 AM

    Re: Measurement Error Reaches Upper Limit

    jpfeifer writes:

    Unfortunately, this is often a common problem. Any error in the design specification will most likely show up as a measurement error. Since many models have gross errors, the estimated estimation error tends to be large. It regularly happens that the upper bound gives an error when the bound is relatively low. This is done to demonstrate in the Schmitt-Grohe/Uribe article “What’s (2012) press in Business Cycles”, so the article by AER Garcia-Cicco et al (2010) with the measurement uncertainty described in the article (see appendix to


    But even when the if parameter is at its limit, the distribution of inverse parameters is normal is shifted. MCMC, when run with your real covariance matrix, will resample the correct way. It might just be inefficient, i.e. Make the most draws to achieve convergence. Instead of a link

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