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Central bank balance sheet and inflation in a euroised small open economy: a cointegrated SVAR analysis
Altin Tanku*
Article | Year: 2024 | Pages: 529 - 552 | Issue: 4 Received: April 11, 2024 | Accepted: September 18, 2024 | Published online: December 13, 2024
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FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
Panel B: Long-run Coefficients
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Panel C: Diagnostic Statistics
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Constant
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LGDP_SA
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LER_SA
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LPCOM_SA
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F-Bound test statistic
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ECM
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0.59***b
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0.18***
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-0.12***
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0.016**
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11.23***
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-0.48***
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(2.27)a
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(5.15)
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(5.64)
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(2.56)
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I(0) at 1%
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I(1)at 1%
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(6.83)
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5.17
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6.36
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a Figures in parenthesis show the value of t statistic, b***/** indicate significance at 1% and 5% respectively. Source: Authors’ calculations.
F
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ƐPCOM
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ƐGDP
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ƐER
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Ɛlr
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ƐREPO
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ƐLI
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S
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ƐPCOM
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ƐGDP
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ƐER
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Ɛlr
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ƐREPO
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ƐLI
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PCOM
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NA
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0
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0
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0
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0
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0
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PCOM
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NA
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NA
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NA
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NA
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NA
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NA
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GDP
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NA
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NA
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NA
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0
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0
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0
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GDP
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NA
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NA
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NA
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NA
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NA
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NA
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ER
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NA
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NA
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NA
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0
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0
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0
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ER
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NA
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NA
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NA
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NA
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NA
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NA
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CPIlr a
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NA
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NA
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NA
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NA
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0
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0
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CPIlr
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NA
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NA
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NA
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NA
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NA
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NA
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REPO
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NA
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NA
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NA
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NA
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NA
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NA
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REPO
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NA
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NA
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0
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NA
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NA
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0
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LI
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NA
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NA
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NA
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NA
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NA
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NA
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LI
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NA
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NA
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NA
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NA
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NA
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NA
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a Correct estimation of SVAR requires that CPI is substituted by the cointegration relationship, hence we added “lr” superscript to CPI (see also in footnote 17).
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CPI
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M3
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ER
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GDP
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REPO
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LI
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PCOM
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Mean
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83.03
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906,759.2
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131.35
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298,645.3
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4.30
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93.95
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118.40
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Maximum
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103.07
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1,582,310
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141.75
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429,309.3
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13.18
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106.66
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189.51
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Minimum
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59.79
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289,340.6
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121.55
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143,213.3
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0.50
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85.39
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48.82
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Std. dev.
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12.52
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371,910.3
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7.03
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69,616.80
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2.70
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5.08
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40.02
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Skewness
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-0.137
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-0.18
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-0.01
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-0.28
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0.40
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0.67
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0.05
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Kurtosis
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1.75
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1.70
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1.35
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2.18
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3.07
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2.46
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2.04
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Observations
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88
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88
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88
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88
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88
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88
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88
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CPI
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PCOM
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GDP
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ER
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M3
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REPO
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LIa
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Constant
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Level
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-2.117
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-1.822
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-2.089
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-1.554
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-3.786***
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-1.937
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-2.212
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First difference
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-12.31***
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-5.97***
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-11.38***
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-6.69***
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-2.887
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-5.152***
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-3.24**
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Constant and trend
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Level
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-1.833
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-2.15
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-2.475
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-1.523
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-1.527
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-5.597***
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-10.209***
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First difference
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-13.06***
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-5.94***
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-11.61***
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-6.68***
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-8.475***
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-5.074***
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-10.159***
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***/** indicate significance at 1% and 5%, respectively. a LI_SA turned out to be break-point stationary, with a structural break identified in 2008:Q3.
Sample: 2000Q1 2022Q1
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Included observations: 85
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Series: LGDP_SA LCPI_SA LER_SA LPCOM_SA
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Lags interval: 1 to 2
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Selected (0.05 levela) Number of
Cointegrating Relations by Model
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Data Trend:
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None
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None
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Linear
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Linear
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Quadratic
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Test Type
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No Intercept
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Intercept
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Intercept
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Intercept
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Intercept
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No Trend
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No Trend
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No Trend
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Trend
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Trend
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Trace
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1
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1
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1
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1
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1
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Max-Eig
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1
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1
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0
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1
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1
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a Critical values based on MacKinnon-Haug-Michelis (1999).
Graph 1Financial intermediation and M0 money multiplier 2000 Q1=100 (in %) DISPLAY Graph
Graph 2BoA liabilities and NFA, 2002-22, in billions of ALL DISPLAY Graph
Graph 3Monetary indicators, as percentage of BoA liabilities and monetary base in lek (MBL) DISPLAY Graph
Graph 4Balance sheet structural indicator DISPLAY Graph
Table 1Estimates of linear ARDL equation (1) DISPLAY Table
Table 2SVAR identification restrictions: long-run restrictions matrix F and short-run restrictions matrix S DISPLAY Table
Graph 5Response of variables to structural VAR innovations (shocks 1 – 6) DISPLAY Graph
Graph 6Response to structural VAR innovation (shocks are defined in the same way as in graph 5) DISPLAY Graph
Graph 7Response of ER to structural VAR innovations shock 5 DISPLAY Graph
Graph 8Response of ER to structural VAR innovations shock 6 DISPLAY Graph
Table A1Summary statistics DISPLAY Table
Table A2Augmented Dickey-Fuller test results DISPLAY Table
Table A3Johansen cointegration test results DISPLAY Table
Graph A1Time series of individual variables DISPLAY Graph
Graph A2Scattered diagrams portraying first degree of autocorrelation and bilateral relationships DISPLAY Graph
* The authors would like to thank two reviewers for their helpful comments and advice.
1 See Sejko ( 2021) for a detailed discussion of how implementation of balance sheet policies is constrained by shallow, underdeveloped financial and capital markets and the lack of tradable securities.
2 The literature typically refers to permanent vs. non-permanent monetary injections. In this paper we use the terms non-borrowed and permanent injections interchangeably, in the sense that permanent funds comprise currency in circulation plus all bank reserves that are not borrowed from the central bank.
3 For the purpose of this paper monetary shocks are referred to as balance sheet structural shocks. The early literature on the topic that emerged in the late 1990s referred to monetary base rather than balance sheet shocks.
4 In the second half of 2022, foreign currency loans represented 51% of total loans and foreign currency deposits 46% of total deposits in the Albanian banking system (BoA, 2023).
5 Authors’ calculations, based on Ministry of Finance ( 2023: 52).
6 See BoA Supervisory Council Decision no. 49 (6 June 2018). Despite these interventions, the BoA held on to its free-floating exchange rate regime: it announced the amount and the calendar of interventions at the beginning of each year and executed them on schedule so as not to surprise the market (see Sejko, 2021; Tanku, Vika and Gjermeni, 2007; and BoA Annual Reports).
7 While this is relatively small compared with balance sheet expansions of the Federal Reserve and the ECB, it is still significant given that the BoA decided not to implement standard quantitative easing (see Sejko, 2021).
8 “That is … a permanent increase in the stock of base money through an irreversible open market purchase by the central bank of non-monetary sovereign debt held by the public – that is, QE” (Buiter, 2014: 1).
9 See, e.g. Beekworth ( 2017), and Sumner ( 2021).
10 Note that the lek and foreign currency deposits account for about 50% of total deposits each.
11 See Krugman, Dominquez and Rogoff ( 1998) for an early discussion of the breakdown in money multiplier and the inability of monetary policy to influence inflation and economic activity.
12 Oularis, Pagan and Restrepo ( 2018) emphasise that we must specify equation (7) in terms of Δwt if we want the shocks of stationary effects to have a permanent effect in the system.
13 See for example Kolasi, Shijaku and Shtylla ( 2010), Shijaku ( 2016), Bahmani, Miteza and Tanku ( 2020), and Miteza, Tanku and Vika ( 2023).
14 Calculations are based on the sectoral balance sheet data.
15 See, e.g. Shijaku ( 2016), Bahmani, Miteza and Tanku ( 2020), Miteza, Tanku and Vika ( 2023).
16 Oularis, Pagan and Restrepo ( 2018) show that shocks in the SVAR are the same as in the original SVECM, but estimated elasticities are different. Therefore, we focus on estimated impulse response functions and do not report the coefficients.
17 We call this shock Ɛlr (with lr standing for the long run) to highlight that it represents the cointegration relationship estimated by the ARDL method of Pesaran, Shin and Smith ( 2001).
18 We acknowledge that an alternative estimation approach, such as full information maximum likelihood estimator, could allow for more flexibility and precision in the specification of the correct functional form for the SVAR. However, we leave that extension for further research.
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