### This website contains preliminary chapters of the book
'Structural Vector Autoregressive Analysis' by Lutz Kilian and Helmut Lütkepohl,
Cambridge University Press (to appear in 2017). The copyright rests with the
authors. The commercial use of these pdf files is prohibited. Click to download.

### MATLAB code for selected applications in the book. Click to download. (To be
completed).

### Chapter 2:

LS estimation
Restricted GLS estimation
Bias-corrected LS estimation
Lag-augmented LS estimation
Table 2.1: Alternative lag-order selection criteria for VAR models
### Chapter 3:

Johansen MLE of VECM with unknown cointegrating
vector and unrestricted intercept
Johansen MLE of VECM with unknown
cointegrating vector and with intercept absorbed into error correction term
MLE of VECM with known cointegrating vector and
unrestricted intercept
FGLS estimaor of VECM with unknown cointegrating
vector and unrestricted intercept
Table 3.1: Trace and Maximum Eigenvalue Tests
for Cointegrating Rank (Unrestricted Intercept)
Table 3.1: Trace and Maximum Eigenvalue Tests
for Cointegrating Rank (Restricted Intercept)
### Chapter 4:

Table 4.1: Forecast Error Variance Decomposition for U.S. Real Dividend
Growth
Table 4.2: Real-Time Risk Measures as of December 2010
Figure 4.1: Point estimates of responses of U.S. real dividends to selected structural
shocks
Figure 4.2: Historical decomposition of the real price of crude oil in percent
deviations from the mean
Figure 4.3: Contribution of each structural shock to the cumulative change in
the real price of oil from January 2003 to June 2008
Figure 4.4: Historical counterfactuals for the real price of crude oil from January
2003 to June 2008
Figure 4.5: Selected real-time forecast scenarios for the real price of crude oil
as of December 2010
Figure 4.6: Real-time probability weighted 1-year ahead density forecasts as of
December 2010 under different scenarios about the future state of the global economy
Figure 4.7: Actual level of global oil production and counterfactual level in the
absence of the U.S. shale oil boom
Figure 4.8: Counterfactual sequence of flow supply shocks in the absence of the
U.S. shale oil boom
Figure 4.9: Evolution of the nominal Brent price of
crude oil with and without shale oil
Figure 4.10: Counterfactual paths of key observables under alternative policy
counterfactuals
Figure 4.11: Sequence of policy shocks required to implement the KL policy
counterfactual
### Chapter 5:

Figure 5.2: Simulated quantiles of inflation responses to monetary policy shocks for different Minnesota priors
Figure 5.3: Simulated quantiles of inflation responses to monetary policy shocks for different Gaussian-inverse Wishart priors
Figure 5.4: Simulated quantiles of inflation responses to monetary policy shocks for different independent Gaussian-inverse Wishart priors
### Chapter 9:

Figure 9.1: Responses of the U.S. economy to an unexpected increase in the
real price of oil (Cholesky decomposition)
Figure 9.1: Responses of the U.S. economy to an unexpected increase in the
real price of oil (Iterative solution)
Figure 9.1: Responses of the U.S. economy to an unexpected increase in the
real price of oil (Rubio-Ramirez, Waggoner and Zha (2010) algorithm)
Figure 9.2: Responses of the U.S. economy to an aggregate supply shock in the
nonrecursive Keating (1992) model (Iterative solution)
Section 9.3: IV estimation of the recursive structural VAR(p) model
Section 9.4: Two-step ML estimation of the recursive structural VAR model
Section 9.4: Two-step ML estimation of the nonrecursive Keating (1992) model
### Chapter 11:

Figure 11.1: Responses to technology and non-technology shocks (Cholesky decomposition)
Figure 11.1: Responses to technology and non-technology shocks (Rubio-Ramirez, Waggoner and Zha (2010) algorithm)
Figure 11.1: Responses to technology and non-technology shocks (Iterative solution)
Figure 11.2: Responses to an unexpected U.S. monetary policy tightening (Rubio-Ramirez, Waggoner and Zha (2010) algorithm)
Figure 11.2: Responses to an unexpected U.S. monetary policy tightening (Iterative solution)
Figure 11.3: Responses to a productivity shock in the baseline model of King et al. (1991)
### Chapter 12:

Table 12.1: Percentage of Models in Joint Wald Confidence Set Consistent with
a Hump-shaped Response Function of Real GNP to an Unexpected Loosening of Monetary Policy
Figure 12.1: Example of how resampling overlapping
blocks may destroy the dependence structure in the bootstrap data at the point of
transition from one block to the next
Figure 12.2: Responses of U.S. real GNP to an
unexpected loosening of monetary policy: Shotgun plot implied by joint 68% Wald
confidence set
Figure 12.3: Responses to an unexpected increase in the
real price of oil: Shotgun plot implied by joint 68% Wald confidence set with subset
of stagflationary responses highlighted
Figure 12.4: Global oil market data
Figure 12.5: 95% delta method confidence intervals
based on bootstrap standard error estimates
Figure 12.6: Alternative 95% bootstrap confidence
intervals
Figure 12.7: Responses of the real price of oil to oil
demand and supply shocks with alternative 95% confidence intervals
Figure 12.8: 95% delta method confidence intervals
based on bootstrap standard error estimates in the overidentified model
### Chapter 13:

Numerical example for subrotation algorithm for block-recursive models in Section 13.9.2
Numerical example for general algorithm allowing for sign and exclusion restrictions in Section 13.9.2
Figure 13.7: Sign-identified oil market model impulse response functions in the modal model and 68% joint HPD regions
Figure 13.8: Stuctural impulse responses in the sign-identified oil market model
Figure 13.9: Responses to a monetary policy tightening in the original sign-identified model: Response functions in the
modal model and 68% joint HPD regions
Figure 13.10: Responses to a monetary policy tightening in the modified sign-identified model: Response functions in the
modal model and 68% joint HPD regions
Figure 13.11: Responses to a monetary policy tightening in the modified sign-identified model