Assignment 2
ChenyangXia V00880621
2022/1/22
Univariate Time Series.
(a) De-trend the natural log of GDP and government purchases using the HP Filter with λ =1600. Plot the ACF and the PACF of the cyclical components as well as the original series. Do you think they are stationary? Do you think the data display persistence?
Yes they are stationary specially for ACF, and the data display persistence for PACF is less persistence. 
(b) Fit AR(1) models for the cyclical components of output and government purchases. Report the estimates as well as their standard errors. For reference, the model is: xt = a + ρxt−1 + εt Where εtiid∼ (0, σ2^ε).
gdpar1Fit <- fit_ARMA_gdp <- auto.arima(df.var$log.gdp.cycle, seasonal = FALSE, max.d = 0)
print(summary(fit_ARMA_gdp))## Series: df.var$log.gdp.cycle
## ARIMA(1,0,0) with zero mean
##
## Coefficients:
## ar1
## 0.6985
## s.e. 0.0567
##
## sigma^2 = 1.553: log likelihood = -267.01
## AIC=538.02 AICc=538.09 BIC=544.21
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.002710737 1.242471 0.6692236 48.22829 112.5056 1.014083
## ACF1
## Training set 0.0418685govtar1Fit <- fit_ARMA_govt <- auto.arima(df.var$log.govt.cycle, seasonal = FALSE, max.d = 0)
print(summary(fit_ARMA_govt))## Series: df.var$log.govt.cycle
## ARIMA(1,0,0) with zero mean
##
## Coefficients:
## ar1
## 0.7447
## s.e. 0.0516
##
## sigma^2 = 0.6464: log likelihood = -195.63
## AIC=395.26 AICc=395.34 BIC=401.45
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.002553811 0.8015352 0.553415 47.59402 94.13619 0.9344763
## ACF1
## Training set -0.08369049(c) Perform residual diagnostics for both models using the check residuals() function. Do you have any concerns? My concern is both p-value are greater than 0.05, which means we don’t have enough statistical evidence to reject the null hypothesis. So we can not assume that the values are dependent.
(d) Solve algebraically for the impulse response of output at yt+j with respect to a shock at time t, εt. Using your point estimate from part (a) what is the impulse response for j ∈{1, 4, 10, 20}. Interpret in words what this means.
Q1d
(e) Sketch the shape of an impulse response function for an AR(1) process for the following values of ρ. ρ ∈ {−0.5, 0, 0.75, 1}
Q1e
#Vector Autoregressions.
(a) You are given the following Structural Vector Autoregression of order one with k variables Azt = c + Ψzt−1 + Σεt where εt IID∼ N (0,I) and Σ is a diagonal matrix with the standard deviations for the structural shocks. Write this as a reduced form VAR(1). How many parameters need to be estimated in the Structural VAR? How many parameters need to be estimated in the Reduced Form VAR?
Q2a
(b) Solve for the moving average representation as well as the long-run mean and variance of the reduced form VAR(1). 
(c) Fit a reduced form VAR(1) for the cyclical components of log GDP and log government purchases. You can use the data you created in Question 1. Discuss the sign and statistical significance of the coeffecients, Φ
##
## VAR Estimation Results:
## =========================
## Endogenous variables: GDP, govt
## Deterministic variables: none
## Sample size: 162
## Log Likelihood: -429.21
## Roots of the characteristic polynomial:
## 0.8581 0.6044
## Call:
## VAR(y = adas, p = 1, type = "none", exogen = NULL)
##
##
## Estimation results for equation GDP:
## ====================================
## GDP = GDP.l1 + govt.l1
##
## Estimate Std. Error t value Pr(>|t|)
## GDP.l1 0.69923 0.05595 12.498 <2e-16 ***
## govt.l1 -0.16628 0.08011 -2.076 0.0395 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 1.221 on 160 degrees of freedom
## Multiple R-Squared: 0.4944, Adjusted R-squared: 0.4881
## F-statistic: 78.24 on 2 and 160 DF, p-value: < 2.2e-16
##
##
## Estimation results for equation govt:
## =====================================
## govt = GDP.l1 + govt.l1
##
## Estimate Std. Error t value Pr(>|t|)
## GDP.l1 -0.09060 0.03631 -2.495 0.0136 *
## govt.l1 0.76334 0.05199 14.683 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.7927 on 160 degrees of freedom
## Multiple R-Squared: 0.5745, Adjusted R-squared: 0.5692
## F-statistic: 108 on 2 and 160 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## GDP govt
## GDP 1.492 0.4840
## govt 0.484 0.6284
##
## Correlation matrix of residuals:
## GDP govt
## GDP 1.0 0.5
## govt 0.5 1.0## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1##
## VAR Estimation Results:
## =========================
## Endogenous variables: GDP, govt
## Deterministic variables: none
## Sample size: 161
## Log Likelihood: -423.761
## Roots of the characteristic polynomial:
## 0.8526 0.5068 0.2507 0.112
## Call:
## VAR(y = adas, p = 2, type = "none", exogen = NULL)
##
##
## Estimation results for equation GDP:
## ====================================
## GDP = GDP.l1 + govt.l1 + GDP.l2 + govt.l2
##
## Estimate Std. Error t value Pr(>|t|)
## GDP.l1 0.81917 0.09104 8.998 7.15e-16 ***
## govt.l1 -0.33885 0.13979 -2.424 0.0165 *
## GDP.l2 -0.16281 0.09017 -1.806 0.0729 .
## govt.l2 0.18656 0.14046 1.328 0.1860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 1.211 on 157 degrees of freedom
## Multiple R-Squared: 0.494, Adjusted R-squared: 0.4811
## F-statistic: 38.32 on 4 and 157 DF, p-value: < 2.2e-16
##
##
## Estimation results for equation govt:
## =====================================
## govt = GDP.l1 + govt.l1 + GDP.l2 + govt.l2
##
## Estimate Std. Error t value Pr(>|t|)
## GDP.l1 -0.06256 0.05989 -1.045 0.298
## govt.l1 0.67897 0.09197 7.383 8.52e-12 ***
## GDP.l2 -0.02677 0.05932 -0.451 0.652
## govt.l2 0.10521 0.09240 1.139 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.7969 on 157 degrees of freedom
## Multiple R-Squared: 0.5779, Adjusted R-squared: 0.5672
## F-statistic: 53.74 on 4 and 157 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## GDP govt
## GDP 1.4666 0.4848
## govt 0.4848 0.6350
##
## Correlation matrix of residuals:
## GDP govt
## GDP 1.0000 0.5024
## govt 0.5024 1.0000(d) Compute and plot the structural impulse responses using the assumption that shocks to GDP do not contemporaneously affect government purchases.
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 8.66290510326273e-07 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 5.15639660592093e-06 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 0.0291834270730932 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1.28507742658268e-06 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 0.00120023494162846 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 0.000202665640667066 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 3.04838083442505e-07 .## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .
## Warning in SVAR(x = varboot, Amat = amat, Bmat = NULL): Convergence not achieved
## after 100 iterations. Convergence value: 1 .