11.1.1. Book
Szikszai, Sz. 2005: Days of abundant liquidity over on emerging markets. Budapest Business Journal, 13, 43, p. 11.
Szikszai, Sz. 2005: Forintárfolyam-változás: nyertesek és vesztesek. (Changes in the forint exchange rate:
winners and losers) Magyar BBJ, 2, 21, p. 5.
Szikszai, Sz. 2005: Pénzpiaci kock ial market risks: the
multi-million forint question) M
onomy) Bank és Tőzsde, 10, 16, p. 9.
Andor L. – Szikszai, Sz. 2002: A pénzügypolitika négy éve (1998-2002). (Four years of monetary policy )) Cégvezetés, 10, 7.
zikszai, Sz. 2001: Most minden a látszat. (Everything is what it seems now) Bank és Tőzsde, 9, 36, p. 8.
1.1.4. Other
Andor, L. – Szikszai, Sz. 2004: Eufória, korrekció, pánik. A 2002-2004-es időszak a magyar pénzügypolitikában. (Euphoria, correction, panic. The period of 2002-2004 in Hungarian monetary policy) Álláspontok, 7, 1, pp. 84-115.
Makray, P. – Németh, Z. – Szikszai, Sz. – Vojnits, T. 2001: Hungary: Set for Convergence – How will this play out in financial markets? OTP Securities, Budapest, 30 p.
11.2. Conference lectures
ai, Sz. 2009: Evolving Market perception of Credibility of MNB’ Inflation Targeting Challenges for Analysis of the Economy, the Businesses, and Social Progress,
ersitas Szeged Press, Szeged, pp.133-134.
Szikszai, Sz. 2007: The institutional design of the Hungarian monetary policy authority. II. Pannon , pp. 359-364.
ázatok: a sok millió forintos kérdés. (Financ agyar BBJ, 2, 11, p. 14.
Szikszai, Sz. 2002: Politika a gazdaságban. (Politics in the ec
(1998-2002 S
1
Erdélyi, M. – Sziksz Regime.
s International Scientific Conference, Univ
Conference on Economic Studies, Volume II, Pannon Egyetem, Veszprém
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The list of websites mentioned or referred to in the text:
www.akk.hu
www.bankofengland.co.uk www.budacash.hu
www.cnb.cz/en/
www.ebroker.hu www.ecb.int www.euribor.org www.federalreserve.gov www.fn.hu
www.hirtv.net www.index.hu www.ksh.hu
www.merriam-webster.com www.mnb.hu
www.mno.hu www.napi.hu
www.nbp.pl/Homen.aspx?f=/srodeken.htm www.nol.hu
www.otpbank.hu www.portfolio.hu www.raiffeisen.hu www.reservebank.co.za/
www.reuters.hu
www.riksbank.com
www.tcmb.gov.tr/yeni/eng/
www.tozsdeforum.hu www.vg.hu
www2.pm.gov.hu
szixai.5mp.eu/web.php?a=szixai
Appendix 1
Chart- and table-pack on the most important economic and financial data between July 2001 and April 2009 (Excel)
Statistics of the daily EURHUF exchange rate changes*:
All days No MC meeting MC meeting Number of observations 1943 1804 139 Mean of changes (%) -0,0059% -0,0155% 0,1055%
Average absolute change (%) 0,4077% 0,4018% 0,4782%
Median of changes (%) 0,0123% 0,0081% 0,0850%
Standard deviation of changes (%) 0,6341% 0,6212% 0,7682%
St. dev. of absolute changes (%) 0,4857% 0,4740% 0,6104%
Kurtosis 11,99 12,43 9,50
Skewness -0,91 -0,81 -1,71
Range (%) 9,92% 9,92% 6,21%
Minimum (%) -5,76% -5,76% -3,81%
Maximum (%) 4,16% 4,16% 2,40%
Source: www.mnb.hu.
* Negative changes indicate forint weakening.
1943 1804 139
Mean of changes (%) -0,0007% -0,0013% -0,0165%
Average absolute change (%) 0,1142% 0,1112% 0,1132%
Median of changes (%) 0,0100% 0,0000% -0,0100%
Standard deviation of changes (%) 0,2250% 0,2154% 0,2320%
St. dev. of absolute changes (%) 0,1938% 0,1845% 0,2031%
Kurtosis 65,04 76,13 14,63
Skewness -2,28 -2,63 -0,06
Range (%) 6,49% 6,49% 2,50%
Minimum (%) -3,82% -3,82% -1,27%
Maximum (%) 2,67% 2,67% 1,23%
tatistics of the daily term spread changes:
S
All days No MC meeting MC meeting Number of observations
Source: www.akk.hu.
Statistics of Governor comments and MC bias:
Governor comments MC bias
Mean of values 0,18 -0,09
Median of values 0,17 0,00 Standard deviation of values 0,56 0,62
Kurtosis -0,31 -0,41
Skewness -0,37 0,07
Source: own calculations based on press collection and data from www.mnb.hu.
uarterly year-on-year headline CPI inflation before (black) and after (green) IT:
Quarterly year-on-year core CPI inflation:
0%
MNB base rate and the 1-month Bubor:
MNB base rate and the EURHUF exchange rate:
3%
overnor comments:
MNB base rate and the consensus value of G
Governor:
>0: Hawkish <0: Dovish -1,5
-1 -0,5 0 0,5 1 1,5
0%
2%
4%
6%
8%
10%
12%
14%
Consensus value of Governor comments (left) MNB base rate (right)
2001 2002 2003 2004 2005 2006 2007 2008 2009
Source: own calculations based on press collection and data from www.mnb.hu.
EURHUF exchange rate and the consensus value of Governor comments:
Governor:
>0: Hawkish <0: Dovish -1,5
-1 -0,5 0 0,5 1 1,5
230 244 258 272 286 300 314
Consensus value of Governor comments (left) EURHUF (right)
2001 2002 2003 2004 2005 2006 2007 2008 2009
Source: own calculations based on press collection and data from www.mnb.hu.
MNB base rate changes and the consensus value of Governor comments:
Consensus value of Governor comments (left) MNB base rate change (right)
2001 2002 2003 2004 2005 2006 2007 2008 2009
om www.mnb.hu.
Source: own calculations based on press collection and data fr MNB base rate changes and MC bias values:
MC bias:
MNB base rate change (right)
006 2007 2008 2009
ource: own calculations based on data from www.mnb.hu
2001 2002 2003 2004 2005 2
S .
MNB base rate and ECB key rate:
10-year government bond yields in Hungary and Germany
13%
Source: www.akk.hu and MNB.
istribution of daily EURHUF exchange rate changes:
D
0%
25%
5%
10%
15%
20%
≤-1% -0,8≥>-1% -0,6≥>-0,8% -0,4≥>-0,6% -0,2≥>-0,4% 0%≥>-0,2% 0,2%≥>0% 0,4%≥>0,2% 0,6%≥>0,4% 0,8%≥>0,6% 1%≥>0,8% >1%
Daily EURHUF change all days Daily EURHUF change MC meeting days Daily EURHUF change ex MC meeting days
Source: www.mnb.hu.
Distribution of daily term spread changes
30%
0%≥>-0,05% 0,05%≥>0% 0,1%≥>0,05% 0,15%≥>0,1% 0,2%≥>0,15% 0,25%≥>0,2% >0,25%
Distribution of daily term spread change MC meeting days Distribution of daily term spread change all days
25% Distribution of daily t
20%
15%
10%
5%
0%
≤-0,25% -0,2≥>-0,25% -0,15≥>-0,2% -0,1≥>-0,15% -0,05≥>-0,1%
erm spread change ex MC meeting days
Source: www.akk.hu.
ime plot of daily changes in log EURHUF:
T
0.03
-0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.04 0.05
2002 2003 2004 2005 2006 2007 2008 2009
EURHUF
Source: www.mnb.hu.
Time plot of daily changes in the term spread of the 10-year and 3-month government papers:
0.01
-0.04 -0.03 -0.02 -0.01 0 0.02 0.03
2002 2003 2004 2005 2006 2007 2008 2009
.hu
Termspread
Source: www.akk .
onthly budget balances (billion forints) M
jan.03; -615,8 -700
-600 -500 -400 300
2 2 03 03 04 4 05 5 06 6
n-07 Jul-07
Jan-08 Jul-08
Jan-09 200
100 0 -100 -200 -300
Jul-0 Jan-0
Jul-0
Jan- Ju
l-Jan- Jul-0
Jan- Jul-0
Jan- Jul-0 Ja 1
Source: www2.pm.gov.hu.
Current account balances (million euros)
-3000 -2700 -2400 -2100 -1800 300 0 -300
-1500 -1200 -600
jún.01
dec.01 jún.02
dec.02 jún.03
dec.03 jún.04
dec.04 jún.05
dec.05 jún.06
dec.06 jún.07
dec.07 jún.08 dec.08 Source: www.mnb.hu
-900
.
Quarterly year-on-year GDP growth rates
3%
2%
1%
0%
-1%
-2%
-3%
4%
5%
má máj nov. máj má nov.
04 máj.05
nov.05 máj.06 nov.
06 máj.07
nov.07 máj.08
nov.
j.01 08
nov.01 .02 02 .03
nov.03 j.04 Source: www.ksh.hu.
ICPI before and after T (Stata):
i) For the period before the introduction of
===================
Pre-IT
===================
. dfuller lo lags(1)
Augment r unit root of b
- Z(t) has t-distribution ---ritical 10% C---ritical Value Value ---
1.699 -1.311 --- -value for Z(t) = 0.1977
--- D.log_i | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---
L1. | -.0365717 .0424019 -0.86 0.395 -.1232933 .0501498 LD. | .4415143 .1626771 2.71 0.011 .1088024 .7742263 _cons | -.0764952 .0774028 -0.99 0.331 -.2348018 .0818114 ---
. estat dwatson
Appendix 2
Augmented Dickey-Fuller test statistics for the log of the seasonally adjusted VA I
the IT regime (1993-2001):
g_i, drift regress
ed Dickey-Fuller test fo Number o s = 32
- Test 1% Critical 5% C
Statistic Value
Z(t) 0.863 2.462
- ---p
log_i |
Durbin-Watson d-statistic( 3, 32) = 1.802026
. dfuller log_i, drift regress lags(5)
Number of obs = 28
--- Z(t) has t-distribution --- ritical Value --- -1.721 -1.323 ---
--- [95% Conf. Interval]
---+---
19683 .0881153 1795 1.072175 .396 -.6394429 .2635258 54 0.595 -.3288727 .5590798 L4D. | -.1524425 .2150708 -0.71 0.486 -.5997067 .2948216 L5D. | -.0724913 .1989467 -0.36 0.719 -.4862236 .341241 _cons | -.048127 .088894 -0.54 0.594 -.2329923 .1367383 ---
. estat dwatson
Durbin-Watson d-statistic( 7, 28) = 1.9388
. dfuller log_i, drift regress lags(15)
Augmented Dickey-Fuller test for unit root Number of obs = 18 Augmented Dickey-Fuller test for unit root
Test 1% Critical 5% Critical 10% C Statistic Value Value
---(t) -0.335 -2.518 Z
---p-value for Z(t) = 0.3704
D.log_i | Coef. Std. Err. t P>|t|
log_i |
L1. | -.0169265 .0505102 -0.34 0.741 -.12 LD. | .6386773 .2084511 3.06 0.006 .205 L2D. | -.1879585 .2171001 -0.87 0
L3D. | .1151035 .2134898 0.
- Z(t) has t-distribution
========
ost-IT
dfuller log_i, drift regress lags(1)
d Dickey-Fuller test for unit root Number of obs = 29
--- Z(t) has t-distribution --- est 1% Critical 5% Critical 10% Critical Statistic Value Value Value
--- .061 -2.479 -1.706 -1.315 ---
D.log_i | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--- log_i |
1 33
2
stat dwatson
urbin-Watson d-statistic( 3, 29) = 1.998371
. dfuller log_i, drift regress lags(5)
ii) For the period after the introduction of the IT regime (2001-2009):
===========
P
===================
.
Augmente
T
Z(t) -2
--p-value for Z(t) = 0.0247
---
L1. | -.2260546 .1096625 -2.06 0.049 -.4514691 -.000640 LD. | .4459184 .180496 2.47 0.020 .0749035 .81693 _cons | -.6928425 .3276612 -2.11 0.044 -1.36636 -.019325 ---
. e
D
Augmented Dickey-Fuller test for unit root Num 25
--- Z(t) has t-distribution ---
lue --- Z(t) -2.159 -2.552 -1.734 -1.330
---p-value for Z(t) = 0.0223
--- --- D.log_ . Err. t
--- ---
log_i
L1. 8 .1643917 -2.16 0.045 6 LD. 222621 2.82 0.011 78
L2D. 8087 0.09 0.929 7
2.45
.247072 -0.5 .3730549
0.4
.4947394 -2.1 -.0439693
---urbin-Watson d-statistic( 7, 25) = 2 341
ber of obs =
Test 1% Critical 5% Critical 10% Critical Statistic Value Value Va
---
--- --- --- i | Coef. Std P>|t| [95% Conf. Interval]
-+--- --- --- |
| -.354852 -.70022 9 -.0094787
| .6274871 . .15977 1.095196 | .0224742 .24 -.49873 2 .5436856 L3D. | .5634025 .2303296 0.025 .07949 1.047307 8 L4D. | -.1460241 9 0.562 -.6651031
L5D. | .1117629 .2425327 6 0.650 -.3977794 .6213051 _cons | -1.083378 9 0.042 -2.122787
--- ---
. estat dwatson
D .068
log of t CPI bef
ction
ions 1993:1-200 nally_a std. error t-rati
87 -Robust estimate of variance: 0,529 2
residu (with 3
5% 1 0,574 o
200 lly_a r t-rati
0,0497183
-,166 ted residu
(with 99
1 0,463 0,574
KPSS test statistics for the he seasonally adjusted VAI ore and after IT (Gretl):
i) For the period before the introdu of the IT regime (1993-2001):
KPSS regression
OLS, using observat 1:2 (T = 34) Dependent variable: l_Seaso d
coefficient o p-value --- ---
const -1,79927 0,06678 26,94 4,81e-024 ***
76
Sum of squares of cumulated als: 457,337 KPSS test for l_Seasonally_ad out trend) Lag truncation parameter =
Test statistic = 0,746788
10% 5% 2, % Critical values: 0,347 0,463 0,739
ii) For the period after the introduction f the IT regime (2001-2009):
KPSS regression
OLS, using observations 2001:3- 9:1 (T = 31) Dependent variable: l_Seasona d
coefficient std. erro o p-value --- ---
const -2,98473 60,03 8,18e-033 ***
Robust estimate of variance: 0 725
Sum of squares of cumula als: 23,2963 KPSS test for l_Seasonally_ad out trend) Lag truncation parameter = 2
Test statistic = 0,1453
10% 5% 2,5% % Critical values: 0,347 0,739
resse stima
el 2: OL 7-2009
Co t-ratio p-value
const 0,00 0428 0,000745415 0,8860 0,37793
consensusCPI 0,986282 0,0125895 78,3418 <0,00001 ***
Mean dependent var 0,055947 S.D. dependent var 0,019047 Sum squared resid 0,000498 S.E. of regression 0,002327
R-squared 0,985231 Adjusted R-squared 0,985071
F(1, 92) 6137,441 P-value(F) 5,15e-86
Log-likelihood 437,5569 Akaike criterion -871,1137
Schwarz criterion -866,0271 Hannan-Quinn -869,0591
rho 0,182099 Durbin-Watson 1,633647
est for normality of residual -
ull hypothesis: error is normally distributed est statistic: Chi-square(2) = 8,70596
ith p-value = 0,0128684 est for ARCH of order 12 -
ull hypothesis: no ARCH effect is present LM = 11,7278
with p lue = P(Chi-Square(12) > 11,7278) = 0,467779 White's test for heteroskedasticity -
Null hypothesis: heteroskedasticity not present Test statistic: LM = 3,79439
with p-value = P(Chi-Square(2) > 3,79439) = 0,149989 LM test for autocorrelation up to order 12 -
Null hypothesis: no autocorrelation Test statistic: LMF = 0,794084
with p-value = P(F(12,80) > 0,794084) = 0,655149
Appendix 3
Actual monthly CPI data reg d on the analysts’ consensus e te (Gretl):
Mod S, using observations 2001:0 :04 (T = 94) Dependent variable: actualCPI
efficient Std. Error 066
T N T w T N
Test statistic:
-va
Augmen -Fuller tes ona Laxton-N’Diaye credibility measure for Hungarian monetary policy (Gretl):
Augmen ller test for Credibili including )Credibility_LND sample size 1914
unit-root null hypothesis: a = 1
y(-1) + ... + for e (27, 1885) = 2, ,005449
1 ,1938 ith constant and trend
ff. for e
gged differences: F(27, 1884) = 2,122 [0,0007]
6149 2 symptotic p-value 0,3452
Appendix 4
ted Dickey t for stati rity of the
ted Dickey-Fu ty_LND
27 lags of (1-L
test with constant
model: (1-L)y = b0 + (a-1)* e 1st-order autocorrelation coeff. : 0,001 lagged differences: F 124 [0,0007]
estimated value of (a - 1): -0 23 test statistic: tau_c(1) = -2,2354
asymptotic p-value 0 w
model: (1-L)y = b0 + b1*t + (a-1)*y(-1) + ... + e 1st-order autocorrelation coe : 0,001 la
estimated value of (a - 1): -0,00 77 test statistic: tau_ct(1) = -2,4662 a
sts (S
ean St 394911
Mean St ax
Mean St ax
Appendix 5
Results of the Elliot-Müller te tata):
EM test
=======
* termspread *
. sum emtst
Variable | Obs M d. Dev. Min Max ---+--- emtst | 78 -7. 0 -7.394911 -7.394911
. sum emtstjt
Variable | Obs d. Dev. Min M ---+--- emtstjt | 78 -12.44769 0 -12.44769 -12.44769
.
EM test
=======
* termspread *
. sum emtst
Variable | Obs d. Dev. Min M
sum emtstjt
Variable | Obs Mean Std. Dev. Min Max ---+---
emtstjt | 91 -12.91959 0 -12.91959 -12.91959
M test
======
eurhuf *
sum emtst
Variable | Obs Mean Std. Dev. Min Max ---+---
emtst | 80 -5.472538 0 -5.472538 -5.472538
sum emtstjt
Obs Mean Std. Dev. Min Max ---+--- emtstjt | 80 -11.39814 0 -11.39814 -11.39814
.
---+--- --- emtst | 91 -4.776701 0 -4. 776701 -4. 776701
.
. E
=
*
.
.
Variable |
break in the term spread-CPI surpr e specification on two data sets (Gretl):
Model 1: OLS, using observations 2001:07-2009:04 (T = 93) Missing or incomplete observations dropped: 1
Coefficient Std. Error t-ratio p-value
Mean dependent var -0,000120 S.D. dependent var 0,001027
F(1, 91) 0,000340 P-value(F) 0,985333
-1013,038
-1007,973 Hannan-Quinn -1010,993
Chow test for structural break at observation 2004:03 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,20072
) > 2,20072) = 0,116714
Chow test for structural break at observation 2006:10 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 1,03925
with p-value = P(F(2, 89) > 1,03925) = 0,357972
Appendix 6
Results of augmented regressions of the Chow tests for structural is
Dependent variable: Tspreadchg
const -0,000119881 0,000107039 -1,1200 0,26568
NormCPIchg -1,97316e-06 0,000107039 -0,0184 0,98533
Sum squared resid 0,000097 S.E. of regression 0,001032
R-squared 0,000004 Adjusted R-squared -0,010985
Log-likelihood 508,5190 Akaike criterion
Schwarz criterion
Chow test for structural break at observation 2001:08 - Null hypothesis: no structural break
Test statistic: F(1, 90) = 0,527294
with p-value = P(F(1, 90) > 0,527294) = 0,469631 with p-value = P(F(2, 89 Chow test for structural b ervation 2001:09 -
Null hypothesis: no struc est statistic: F(2, 89) = 0,887022
ith p-value = P(F(2, 89) > 0,887022) = 0,41549
Chow test for structural break at observation 2004:04 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,20092
with p-value = P(F(2, 89) > 2,20092) = 0,116692
Chow test for structural break at observation 2006:11 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,553789
with p-value = P(F(2, 89) > 0,553789) = 0,576736 reak at obs
tural break T
w
Chow test for structural break at observation 2001:10 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,411187
with p-value = P(F(2, 89) > 0,411187) = 0,664116
Chow test for structural break at observation 2004:05 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,14097
with p-value = P(F(2, 89) > 2,14097) = 0,123556
Chow test for structural break at observation 2006:12 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,482188
with p-value = P(F(2, 89) > 0,482188) = 0,619034 Chow test for structural break at observation 2001:11 - Chow test for structu
Null hypothesis: no Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,463013
ith p-value = P(F(2, 89) > 0,463013) = 0,630892 w
ral break at observation 2004:06 - structural break
Test statistic: F(2, 89) = 2,16451
with p-value = P(F(2, 89) > 2,16451) = 0,120813
Chow test for structural break at observation 2007:01 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,433015
with p-value = P(F(2, 89) > 0,433015) = 0,64991 Chow test for structural break at observation 2001:12 -
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,45727
with p-value = P(F(2, 89) > 0,45727) = 0,634488 with p-value = P(F(2, 8
Chow test for structural break at observation 2004:07 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,36864
9) > 2,36864) = 0,099485
Chow test for structural break at observation 2007:02 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,556742
with p-value = P(F(2, 89) > 0,556742) = 0,575056 Chow test for str k at observation 2002:01 - Chow test for structur
Null hypothesis: no structural break Test statistic: F(2, 89) =
with p-value = P(F(2, 89 5) = 0,357484
al break at observation 2004:08 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,20071
with p-value = P(F(2, 89) > 2,20071) = 0,116714
Chow test for structural break at observation 2007:03 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,592729
with p-value = P(F(2, 89) > 0,592729) = 0,554984 uctural brea
1,04065 ) > 1,0406
Chow test for structural break at observation 2002:02 - Chow test for structural break at observation 2004:09 - Chow test for structural break at observation 2007:04 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,67756
with p-value = P(F(2, 89) > 0,67756) = 0,510455
Null hypothesis: no structural break Test statistic: F(2, 89) = 1,95021
with p-value = P(F(2, 89) > 1,95021) = 0,148276
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,538548
with p-value = P(F(2, 89) > 0,538548) = 0,585485 Chow test for structural break at observation 2002:03 -
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,576839
with p-value = P(F(2, 89) > 0,576839) = 0,563757
Chow test for structural break at observation 2004:10 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,01705
with p-value = P(F(2, 89) > 2,01705) = 0,139085
Chow test for structural break at observation 2007:05 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,607816
with p-value = P(F(2, 89) > 0,607816) = 0,546783 Chow test for structural break at observation 2002:04 -
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,675949
Chow test for structural break at observation 2004:11 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,12477
Chow test for structural break at observation 2007:06 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,657866
with p-value = P(F(2, 89) > 0,675949) = 0,511265 with p-value = P(F(2, 89) > 2,12477) = 0,125481 with p-value = P(F(2, 89) > 0,657866) = 0,520456 Chow test for structural break at observation 2002:05 - Chow test for structural break at observation 2004:12 - Chow test for structural break at observation 2007:07 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,613555
with p-value = P(F(2, 89) > 0,613555) = 0,543697
Null hypothesis: no structural break Test statistic: F(2, 89) = 2,05532
with p-value = P(F(2, 89) > 2,05532) = 0,134087
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,769835
with p-value = P(F(2, 89) > 0,769835) = 0,466148 Chow test for structural break at observation 2002:06 -
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,724429
with p-value = P(F(2, 89) > 0,724429) = 0,487436
Chow test for structural break at observation 2005:01 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,25136
with p-value = P(F(2, 89) > 2,25136) = 0,111219
Chow test for structural break at observation 2007:08 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,86602
with p-value = P(F(2, 89) > 0,86602) = 0,424136 Chow test for structural break at observation 2002:07 -
Null hypothesis: no structural break est statistic: F(2, 89) = 0,649162
Chow test for structural break at observation 2005:02 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,32567
Chow test for structural break at observation 2007:09 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,972664 T
with p-value = P(F(2, 89) > 0,649162) = 0,524939 with p-value = P(F(2, 89) > 2,32567) = 0,103629 with p-value = P(F(2, 89) > 0,972664) = 0,382057 Chow test for structural break at observation 2002:08 -
Null hypothesis: no structural break Test statistic: F(2, 89) = 0,902882
with p-value = P(F(2, 89) > 0,902882) = 0,40908
Chow test for structural break at observation 2005:03 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 2,51686
with p-value = P(F(2, 89) > 2,51686) = 0,0864439
Chow test for structural break at observation 2007:10 - Null hypothesis: no structural break
Test statistic: F(2, 89) = 1,02054
with p-value = P(F(2, 89) > 1,02054) = 0,364579 Chow test for structural break at observation 2002:09 - Chow test for structural break at observation 2
Null hypothesis: no structural break Null hypothesis: no structural break
Test statistic: F(2, 89) = 0,902559
Test statistic: F(2, 89) = 0,902559