1. Agata STĘPIEŃ Bilge Kagan OZDEMIR Renata SADOWSKA Winfield TURPIN
Structural VAR Modelling Of Monetary Policy For Small Open Economies: The Turkish Case
Agata STĘPIEŃ
Bilge Kagan OZDEMIR
Renata SADOWSKA
Winfield TURPIN

2. Introduction

3. - on account of the effects of monetary policy shocks -
VAR methodology:
Produces efficient results for small closed economies
Provides uncertain empirical results for small open economies
- on account of the effects of monetary policy shocks -

4. AIM: to present why SVAR methodology is better than VAR We investigate the utility of the structural VAR approach in conventional empirical puzzles:
The price puzzle
The liquidity puzzle
The exchange rate puzzle

5. Non-recursive VAR’s are called structural VAR (SVAR) models.
Empirical Puzzles
Result from the recursive structure implied by the standard identification procedure of VAR models
Non-recursive identification schemes effectively solve these puzzles:
Non-recursive VAR’s are called structural VAR (SVAR) models.

6. Price Puzzle Sims (1992)
In various empirical VAR studies, a contractionary monetary shock causes a persistent increase in price level rather than a decrease.
This odd response of the price level to a restrictive monetary policy shock is called “the price puzzle”

7. The Liquidity Puzzle Leeper & Gordon (1992)
A similar anomaly has been observed in the response of interest rates to a shock to monetary aggregates. Following an expansionary shock to the money variable, the interest rate exhibits a positive response creating “the liquidity puzzle”.

8. The Exchange Rate Puzzle Grilli and Roubini (1995) & Sims (1992)
In an open economy environment a positive innovation in interest rates seems to result in a depreciation of the local currency rather than an appreciation. This is “the exchange rate puzzle”.

9. The data
All of our estimations use monthly data for Turkey covering the period 1997:1 to 2004:12
IPI : Industrial production index
P : Wholesale price index
M : Monetary aggregate (M1)
R : Short-term interest rates (overnight rates)
REDEX : Real effective exchange rate index
EX : Nominal exchange rate
All variables are in logarithm levels except the short-term rate.

10. Structural VAR methodology

11. Structural VAR methodology
pth order reduced form VAR:
yt - n x 1 vector of endogenous variables
Ai - the coefficient vector of lagged variables yt - p
et - the vector of serially uncorrelated reduced form errors
with (etet`) = Σ
the more compact form:
A(L) - a matrix polynomial in the lag operator L

12. the structural form of VAR:
where:
B(L) - a pth order matrix polynomial in the lag operator
ut - nx1 vector of structural innovations, with:
ut – serially uncorrelated and diagonal
The relationship between the structural and the reduced model
B0A(L)=B(L)
B0e=u
Σ=(B0-1)Ω(B0-1)

13. Imposing parameter restictions
Cholesky decomposition - orthogonalizing the covariance matrix of reduced form residuals 
gives an exactly identified system,
implies a recursive structure among the variables of the system.
structural VAR
- allows us to use a non-recursive structure
- we identify the model by imposing short-run restrictions on B0, or long-run restrictions on B1
Kim and Roubini (2000): indentification = at least n(n+1)/ restrictions on B0

14. Determining the set of restrictions on B0
2 approaches:
(i) an explicit macroeconomic model (Gal (1992))
(ii) choosing restrictions based on the structure of the economy ((Leeper et al. (1996) and Kim and Roubini (2000)).
- restrictions, which produce the results consistent with economic theories,
- restrictions, which are not rejected by data.

15. VAR MODEL

16. Lag order selection FPE - the final prediction error,
. varsoc R lIPI lOP lM lP lREDEX
Selection order criteria
Sample: 1997m m Number of obs =
|lag | LL LR df p FPE AIC HQIC SBIC |
| |
| 0 | e |
| 1 | e * * |
| 2 | e |
| 3 | e-13* |
| 4 | * e * |
Endogenous: R lIPI lOP lM lP lREDEX
Exogenous: _cons
FPE - the final prediction error,
AIC - Akaike's information criterion,
BIC - the Bayesian information criterion,
HQIC - the Hannan and Quinn information criterion

17. VAR model - results . var R lIPI lOP lM lP lREDEX, lag(1/3)
Vector autoregression
Sample: 1997m m No. of obs =
Log likelihood = AIC =
FPE = 3.24e HQIC =
Det(Sigma_ml) = 2.70e SBIC =
Equation Parms RMSE R-sq chi2 P>chi2 R
lIPI
lOP lM lP
lREDEX

18. ------------------------------------------------------------------------------
| Coef. Std. Err z P>|z| [95% Conf. Interval]
R |
L1 |
L2 |
L3 |
lIPI |
L1 |
L2 |
L3 |
lOP |
L1 |
L2 |
L3 |
lM |
L1 |
L2 |
(-) L3 |
lP |
L1 |
L2 |
L3 |
lREDEX |
L1 |
L2 |
L3 |
_cons |

19. -------------+----------------------------------------------------------------
lIPI |
R |
L1 |
L2 |
L3 |
L1 |
L2 |
L3 |
lOP |
L1 |
L2 |
L3 |
lM |
L1 |
L2 |
L3 |
lP |
L1 |
L2 |
L3 |
lREDEX |
L1 |
L2 |
L3 |
_cons |

20. -------------+----------------------------------------------------------------
lOP |
R |
L1 |
L2 |
L3 |
lIPI |
L1 |
L2 |
L3 |
L1 |
L2 |
L3 |
lM |
L1 |
L2 |
L3 |
lP |
L1 |
L2 |
L3 |
lREDEX |
L1 |
L2 |
L3 |
_cons |

21. -------------+----------------------------------------------------------------
lM |
R |
L1 |
L2 |
L3 |
lIPI |
L1 |
L2 |
L3 |
lOP |
L1 |
L2 |
L3 |
L1 |
L2 |
L3 |
lP |
L1 |
L2 |
L3 |
lREDEX |
L1 |
L2 |
L3 |
_cons |

22. -------------+----------------------------------------------------------------
lP |
R |
L1 |
L2 |
L3 |
lIPI |
L1 |
L2 |
L3 |
lOP |
L1 |
L2 |
L3 |
lM |
(-) L1 |
L2 |
L3 |
L1 |
L2 |
L3 |
lREDEX |
L1 |
L2 |
L3 |
_cons |

23. -------------+----------------------------------------------------------------
lREDEX |
R |
(+) L1 |
L2 |
L3 |
lIPI |
L1 |
L2 |
L3 |
lOP |
L1 |
L2 |
L3 |
lM |
L1 |
L2 |
L3 |
lP |
L1 |
L2 |
L3 |
L1 |
L2 |
L3 |
_cons |

24. The stability of the model
. varstable
Eigenvalue stability condition
| Eigenvalue | Modulus |
| |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
| | |
| | |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
| i | |
All the eigenvalues lie inside the unit circle
VAR satisfies stability condition
The stability of the model

25. Lagrange Multiplier test for autocorrelation in the residuals of VAR model
. varlmar
Lagrange-multiplier test
| lag | chi2 df Prob > chi2 |
| |
| 1 | |
| 2 | |
H0: no autocorrelation at lag order

26. Impulse-response functions for VAR model

29. Structural VAR

30. 1st model Equations: eOP =  eIPI = eOP +  eP = eIPI + 
eR = eOP + eIPI + eREDEX + 
eREDEX = eOP + eR + 

31. Results . svar lOP lIPI lP R lREDEX, aeq(A)
Estimating short-run parameters
Sample: 1997m m Number of obs =
Log likelihood =
LR test of overidentifying restrictions LR chi2( 8) =
Prob > chi =
Equation Obs Parms RMSE R-sq chi P
lOP
lIPI lP R
lREDEX
VAR Model lag order selection statistics
FPE AIC HQIC SBIC LL Det(Sigma_ml)
6.872e e-11
| Coef. Std. Err z P>|z| [95% Conf. Interval]
a_2_ |
_cons |
a_4_ |
_cons |
a_5_ |
_cons |
a_3_ |
_cons |
a_4_ |
_cons |
a_5_ |
(+) _cons |
a_4_ |
_cons |
Results

32. 2nd model Equations: eIPI = eOP +  eP = eIPI + 
eR = eOP + eIPI + eREDEX + 
eREDEX = eOP + eIPI + eP + eR + 

33. Results . svar lOP lIPI lP R lREDEX, aeq(A)
Sample: 1997m m Number of obs =
Log likelihood =
LR test of overidentifying restrictions LR chi2( 6) =
Prob > chi =
Equation Obs Parms RMSE R-sq chi P
lOP
lIPI lP R
lREDEX
VAR Model lag order selection statistics
FPE AIC HQIC SBIC LL Det(Sigma_ml)
6.872e e-11
| Coef. Std. Err z P>|z| [95% Conf. Interval]
a_2_ |
_cons |
a_4_ |
_cons |
a_5_ |
_cons |
a_3_ |
_cons |
a_5_ |
_cons |
a_4_ |
_cons |
a_5_ |
_cons |
a_5_ |
_cons |
a_4_ |
_cons |
Results

34. Impulse-response functions for SVAR model

37. 3rd model Equations: eIPI = eOP +  eP = eOP + eIPI + 
eM = eP + eR + 
eR = eOP + eM + eREDEX + 
eREDEX = eOP + eIPI + eP + eM + eR + 

38. Results VAR Model lag order selection statistics
FPE AIC HQIC SBIC LL Det(Sigma_ml)
3.598e e-14
| Coef. Std. Err z P>|z| [95% Conf. Interval]
a_2_ |
_cons |
a_3_ |
_cons |
a_5_ |
_cons |
a_6_ |
_cons |
a_3_ |
_cons |
a_6_ |
_cons |
a_4_ |
(+) _cons |
a_6_ |
_cons |
a_5_ |
_cons |
a_6_ |
_cons |
a_4_ |
_cons |
a_6_ |
(+) _cons |
a_5_ |
_cons |
Results

39. Impulse-response functions for money in SVAR model

42. CONCLUSIONS

43. of responses functions
Comparison
of responses functions
for VAR and SVAR models

44. SVAR responses functions:

45. SVAR responses functions:

46. SVAR responses functions:

47. SVAR responses functions:

48. Why SVAR is better than VAR?
VAR MODELS:
it is often difficult to draw any conclusion from the large number of coefficient estimates in a VAR system,
vector autoregressions have the status of „reduced form'' and, thus, are merely vehicles to summarize the dynamic properties of the data,
the parameters do not have an economic meaning and are subject to the so-called „Lucas critique'‘.
SVAR MODELS:
SVAR’s do not contain fixed-coefficient expectational rules. They are best thought of as giving linear approximations to the behavior of the private sector and monetary authorities. The private behavior they model thus implicitly includes dynamics arising from revision in forecasting rules as well as other sources of dynamics.