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  • Same IRF, lower and upper values in estimation of SVAR model

    Dear all,

    I am dealing with a SVAR model and I have two models: baseline and extended model. When I run the baseline model, I receive relatively satisfying results, both theoretically and intuitively. However, when I run my extended model (just including more variables), I receive pretty weird results. First, the estimated elements of the lower diagonal in the A matrix are nearly 1. Depending on this (I guess), when I drew the IRF of variables with their confidence intervals, I realized that the values of IRF, lower and upper are the same! Is this a little strange? Let me show you my estimation process.

    Code:
    matrix A2 = (1,0,0,0,0,0 \ .,1,0,0,0,0 \ .,.,1,0,0,0 \ .,.,.,1,0,0 \ .,.,.,.,1,0 \ .,.,.,.,.,1 )    // Causal Ordering: PIMP, HICP, r, wage, markup, PS
    
    svar d_ln_pimp ln_inf ln_r d_ln_wage ln_markup ln_PS, lags(1/8) aeq(A2)     //where ln=log, d: first difference
    
    matlist e(A)
    
    irf create model2, set(var1.irf) replace step(16)
    Matrix A:

    Click image for larger version

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    When I create the IRF graphs, the confidence intervals do not appear. At first, I thought it might be related to the charting code. However, when I looked at the results, I noticed that the confidence intervals are not visible since the lower and upper values are the same as the IRF values.

    Estimation results:

    Click image for larger version

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    Response of Inflation to PIMP shock:


    Click image for larger version

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    What could be the reason for this? Thank you.
    Last edited by Necip Bulut; 16 Feb 2024, 23:46.
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