ИНФЛЯЦИ БА МӨНГӨНИЙ БОДЛОГЫН ХАМААРЛЫН ШИНЖИЛГЭЭ

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21 31.1 31.2 41.3 42 52.1 52.2 63 73.1 , 73.2 (VAR) 93.2 (SVAR) 104 124.1 134.2 144.3 , 164.4 185 205.1 205.2 SVAR 205.3 SVAR 215.4 , SVAR 226 236.1 236.2 246.3 , 247 267.1 :267.2 :268 279 28

2007 , . , , , (SVAR) .

: (SVAR), , , ,

1 1.1 , (1968)

[footnoteRef:1] 1990 . . [1: .]

1990 , , 1990 . , , . , . , 100 130 . . , 2012 10 22- . 5 . 1. 2. , 3. 4. , 5. , , , (SVAR) .1.2

. 2007 2006 1 2013 12 , , , (1), , , , (SVAR) . 1. / , /2. / /3. / , , , / , , , 1.3 , , , . . 2 2.1 Eliana Gonzalez Miguel I.Gomez 2009 . . : 1) ,2) , 3) 3 . : . Christopher Adam Stephen OConnel 2012 . , , . . : . .

J. Davidson C.W Morgan2011 . . Dick Direvall Njuguna S. Ndung1999 VAR Vector Error Correction (VEC) . VEC , , , . : . 2.2 . - . ARIMA, VAR , , , , . .-2005 , , . 10%- 4.45%- . .3 3.1 , . . . . . . . . . . :1. (Trend)2. , (Seasonality)3. (Cycle)4. (Shock) . , , . , , . . . . . . , . AR, MA, ARMA, ARIMA . . . (3.1) (3.2): . : . : (3.3) .1. 4 2 (moving average ). . 12 2 . . . .2. . . , . 3. . (3.4) . 5- , 13- . . . (3.5) . .4. .3.2 (VAR) (VAR) . VAR Sims (1972 1980) , . VAR n- , n- . . VAR , , , VAR . (3.6) K , (3.7)VAR . . . VAR VAR .3.3 (SVAR) SVAR VAR , . , , . . , . SVAR SVAR . (SVAR) . (3.8): , - , - , - , - VAR(k) , - - , , - , , B- .SVAR , B . AIC (Akaike information criterion) :AIC . . (3.9) AIC .SC ( Schwarz criterion) :SC : : (3.10) SC .

, / /. .

. . , . 4 2006 287 , 2004 ,(2005 12 ), - . 2012 1 . 1 2011.122012.1

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: , , , , , , - . . 4.1 2006 1 2013 12 . 2005 12 31- 1- .2005 12- 31- 2013 12 2 . 8 2 . . 1 2005.XII.31-100%

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1-5 6 11 12 ( 2). , . .

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4.2 100 20 . 2012 90 , , , . 3 : , - , . - , . . 4 : , I-IV V VI-VII . VIII-X XII .5 :

4.3 , 2006 1 2013 12 , , , . 2005 12 31- , , 7- . . , . 6 : , 1-6 6 7 7- 12 . , , , . 2006 2009 . 2009-2012 2012 . . .

7

: 4.4 2- 1 . 1 [footnoteRef:2], 1 2 1 . 1 . [2: . ]

1 . 1 . 14

: 1 - . , 1 . 15

: - . 1-5 1 1-2 3-6 . 6-10 , 11-12 1- 9-11 12 . , , , . 16

:

- , . 5 5.1 2006.I-2013.XII . , , , 2005 12 , , , . (stationary) ADF-unit root I(1) . 2- . . 5.2 SVAR (SVAR) . () - - - () - - - SVAR . (5.1)[5.1]- . - 1 . . , . 5.3 SVAR (()) - - - - () - - SVAR . (5.2) , , , , .5.4 , SVAR ()) , - - - () - - - , SVAR . (5.3)

6 VAR VAR

SVAR 3, 4- . . Cholseky- (impulse response function) (one unit residual) . 1 . .6.1 . 1 ( ) 0.24 2 (II,III,) 0.06 . IV V . 3 (VI,VII,VIII) . II 0.1 . I,II III 0.07 IV-VI . 1 0.47 2 (II,III) IV 0.26 . 2 (V,VI) VII . - 1 0.18 II,III IV 0.23 . 6.2 1 I,II 0.1 III 0.31 IV,V 0.07 IV 0.18 . I- II 0.29 III . V 0.15 . 0.07 2 ( II,III) 0.31 . IV-VIII 0.11 IX . 1.53 III 0.15 IV VI VII . 0.02 II 0.27 . 6.3 , , 5 (II,III,IV,V) . VI 0.16 . , . 0.09 3 0.06 . , 3 0.18 . , 0.12 2 (II,III) 0.1 IV 0.3 . Inverse root of AR (5). VAR .

7 7.1 : 1 2 (II,III,) . IV V . 3 (VI,VII,VIII) . 2 ( II,III) . IV-VIII IX . , 5 (II,III,IV,V) . VI 0.16 . . 7.2 : II 0.1 . I- II 0.29 III . V 0.15 . , 3 0.18 .

8 Mardi Dugney A.P 1999 A Structural VAR model of the Australian economy. La Trobe University Asian Development Bank 2008 Food Prices and Inflation in Developing Asia Eliana Gonzalez, Miguel I. Gomez 2008 Forecasting Food Price Inflation In Developing Countries With Inflation Targeting Regimes: the Colombian Case Cornell University Christopher Adam, David Kwimbere 2012 Food Prices And Inflation in Tanzania University of Oxford and IGC Tanzania Dick Durevall, Njuguna S.Ndungu 1999 A Dynamic Model of Inflation for Kenya University of Gothenburg Michal Andrle, Andrew Berg 2013 Forecasting and Monetary Analysis in Low- Income Countries: Food and non- Food Inflation in Kenya International Monetary Fund Mohammad-Yusuf Tashrifov 2005 Monetary Policy Model of Tajikstan: A Structural Vector Autoregression Approach The Australian National University Arminia Fraga, Ilan Goldfan 2003 Inflation Targeting in Emerging Market Economies National Bureau of Economic Research . -, .-, . 2009 : Fan Chart, . 2008 . 2013 .-, . 2004

9 1 ,

1 RGDP 2005 ,

2 CBBR

3 M11 2005 ,

4 RATE

5 CPIC,

6 CPIF 2005.XII.31

7 CPIC2

8 , CPIE , 2005.XII.31

9 CPIT 2005.XII.31-

10 ER . ,

11 GP./ Bloomberg

12 CP./ Bloomberg

13 OP./ Bloomberg

2 ,5% . P-value

1CPIF-1.9442865.3803821.0000-

I(1) CPIF-1.944324-2.6452310.0086+

2CPIT-1.9442863.5218080.9999-

I(1) CPIT-1.944286-4.6087310.0000+

3CPIC-1.9442862.3665540.9956-

I(1) CPIC-1.944286-5.1138300.0000+

4M1-1.9442484.7651971.0000-

I(1) M1-1.944364-2.9009420.0041+

5Gap-1.944364-2.8040430.0055+

6GP-1.9442481.6939260.9776-

I(1) GP-1.944286-8.5819360.0000+

7OP-1.9442860.2469100.7557-

I(1) OP-1.944286-6.4045070.0000+

8CP-1.9442860.2880290.7673-

I(1) CP-1.944286-7.0346120.0000+

9CBBR-1.9442480.0892200.7087-

I(1) CBBR-1.944286-9.8203720.0000+

10Rate-1.944286-1.2619320.1893-

I(1) Rate-1.944286-13.704180.0000+

11ER-1.9443240.6539460.4314-

I(1) ER-1.944324-13.566790.0000+

12CPIC2-1.9442864.1669701.0000-

I(1)CPIC2-1.944286-3.5875630.0005+

13CPIC3-1.9442863.7084430.9999-

I(1) CPIC3-1.944286-3.5260900.0006+

14CPIE-1.9442865.1741551.0000-

I(1)CPIE-1.944324-2.8842560.0043+

3 Structural VAR Estimates

Included observations: 91 after adjustments

Estimation method: method of scoring (analytic derivatives)

Convergence achieved after 25 iterations

Structural VAR is over-identified (2 degrees of freedom)

Model: Ae = Bu where E[uu']=I

Restriction Type: short-run text form

@e1 = C(1)*@u1

@e2 = C(2)*@e1 + C(3)*@u2