Abstract
This paper investigates the effect of financial fragmentation on the monetary transmission mechanism in different Euro area economies, categorized into two groups: countries considered as “core” economies and countries characterized as “peripheral” economies. We analyze the effects of financial fragmentation on the monetary transmission mechanism through the traditional interest rate channel. To gauge the impact of changes in policy rates on the behavior of real variables such as aggregate output and employment we use a Smooth Transition VAR (VSTAR) model. Employing a nonlinear multivariate time series approach helps us capture the regime-dependent dynamics of the variables under study. The results obtained show that money market rates targeted by the central bank do not completely pass through to banks’ lending rates to firms, particularly in a financially fragmented environment. This finding supports the hypothesis of an impairment of the monetary transmission mechanism as a result of financial fragmentation. Given this impairment in some sectors and regions an accompanying credit volume policy might have been appropriate.
Appendix 1

IPI growth rate, year-on-year.

Harmonized Index of Consumer Price, monthly rate of change.

Lending rates to Small and Medium Enterprises, Amounts of up to 1 million Euro.

Unemployment rate, total.
Appendix 2
Traditional unit root tests.
| Test variables | France | Germany | Ireland | Italy | Netherland | Portugal | Spain |
|---|---|---|---|---|---|---|---|
| IPI | |||||||
| ADF | [−4.7856] <(0.01) | [−5.0517] <(0.01) | [−6.5869] <(0.01) | [−3.8156] (0.0194) | [−7.0426] <(0.01) | [−6.3127] <(0.01) | [−3.5601] (0.0383) |
| PP | [−288.82] <(0.01) | [−273.8] <(0.01) | [−236.77] <(0.01) | [−275.65] <(0.01) | [−206.04] <(0.01) | [−250.96] <(0.01) | [−287.52] <(0.01) |
| Inflation | |||||||
| ADF | [−5.6445] <(0.01) | [−6.8151] <(0.01) | [−4.6643] <(0.01) | [−4.2252] <(0.01) | [−6.6623] <(0.01) | [−5.2778] <(0.01) | [−4.4513] <(0.01) |
| PP | [−203.58] <(0.01) | [−265.89] <(0.01) | [−202.19] <(0.01) | [−111.18] <(0.01) | [−97.042] <(0.01) | [−130.93] <(0.01) | [−103.28] <(0.01) |
| Unempl. | |||||||
| ADF | [−3.2952] | [−3.5173] (0.04239) | [−3.2233] (0.0855) | [−4.3006] <(0.01) | [−2.3536] (0.428) | [−4.0267] <(0.01) | [−2.341] (0.4332) |
| PP | [−108.61] <(0.01) | [−159.96] <(0.01) | [−64.324] <(0.01) | [−276.52] <(0.01) | [−147.12] <(0.01) | [−152.71] <(0.01) | [−34.27] <(0.01) |
Unit root tests for nonstationarity against LSTAR model.
| Test variables | KSS ADF | Sollis F-test | Shint Inf-t-test |
|---|---|---|---|
| EONIA | [−1.1986] | [2.3769]** | [0.3802] |
| Spread | [−1.2794]* | [−0.2842] | [−0.3034] |
Below, Table 3 represents the critical values for untransformed (raw) series.
% t-tests, SOLLIS F-tests & for Shintani inf-test; 200 < T = 500.
Asymptotic critical values of unit root tests.
| ADF | KSS ADF | Sollis F-test | Shint Inf-t-test | |
|---|---|---|---|---|
| 1% | −2.60 | −2.79 | 4.241 | −3.21 |
| 5% | −1.95 | −2.51 | 2.505 | −2.66 |
Appendix 3
Linearity tests results.
| Linearity test for the LVSTAR model | |||||||
|---|---|---|---|---|---|---|---|
| France | Germany | Ireland | Italy | Netherlands | Portugal | Spain | |
| LR | 140.61 (0.0005) | 216.42 (0.0044) | 101.42 (0.1929) | 554.30 <(0.001) | 213.81 (0.0063) | 116.42 (0.0319) | 113.89 (0.004) |
| LM | 124.71 (0.0098) | 188.96 (0.0971) | 93.416 (0.381) | 407.37 (0.0003) | 185.68 (0.1291) | 104.47 (0.1413) | 104.63 (0.1388) |
| Rescaled LM | 1.62 (0.0012) | 1.204 (0.0600) | 1.217 (0.107) | 1.241 (0.0099) | 1.183 (0.0793) | 1.3618 (0.0258) | 1.363 (0.0252) |
| Wilks | 128.18 (0.0001) | 183.70 (0.0318) | 92.795 (0.080) | 396.56 (0.0001) | 181.39 (0.041) | 106.52 (0.0098) | 104.20 (0.0145) |
| Rao | 2.029 (0.0000) | 1.22 (0.0000) | 1.526 (0.0000) | -0.9038 (0.0000) | 1.200 (0.0000) | 1.5438 (0.0000) | 1.4989 (0.0000) |
Initial values for location and smoothness parameters in the LVSTAR model.
| Parameters evaluation | ||
|---|---|---|
| Initial values | Location parameter c | Smoothness parameter γ |
| France | 0.1763 | −0.2321 |
| Germany | 0.1660 | −0.6101 |
| Ireland | 0.1690 | 0.8916 |
| Italy | 0.1800 | 2.5007 |
| Netherlands | 0.1680 | 0.4389 |
| Portugal | 0.1946 | 3.4012 |
| Spain | 0.1663 | −0.0264 |
Optimized location and smoothness parameters in the LVSTAR model.
| Optimized parameters | Location parameter c | Smoothness parameter γ |
|---|---|---|
| France | 0.1758 | 0.7850 |
| Germany | 0.1550 | 0.6180 |
| Ireland | 0.1600 | 2.3314 |
| Italy | 0.1830 | 12.209 |
| Netherlands | 0.2639 | 1.5234 |
| Portugal | 0.2080 | 30.000 |
| Spain | 0.1560 | 0.8959 |
Appendix 4





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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2017-0097).
©2018 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Interview
- An Interview with Timo Teräsvirta
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- Testing for misspecification in the short-run component of GARCH-type models
- Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE
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Articles in the same Issue
- Interview
- An Interview with Timo Teräsvirta
- Research Articles
- Nonlinear and asymmetric pricing behaviour in the Spanish gasoline market
- Testing for misspecification in the short-run component of GARCH-type models
- Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE
- Time-varying asymmetry and tail thickness in long series of daily financial returns
- Modeling changes in US monetary policy with a time-varying nonlinear Taylor rule
- Financial fragmentation and the monetary transmission mechanism in the euro area: a smooth transition VAR approach
- P-star model for India: a nonlinear approach
- Can a Taylor rule better explain the Fed’s monetary policy through the 1920s and 1930s? A nonlinear cliometric analysis
- Modeling time-variation over the business cycle (1960–2017): an international perspective