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Economic uncertainty and its impact on the Croatian economy
Petar Sorić* 
Ivana Lolić*
Ivana Lolić
Affiliation: University of Zagreb, Faculty of Economics and Business Zagreb, Zagreb, Croatia
 0000-0003-3112-7699
Article | Year: 2017 | Pages: 443 - 477 | Volume: 41 | Issue: 4 Received: June 1, 2017 | Accepted: October 30, 2017 | Published online: December 11, 2017
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FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
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| BBD | polit | PI6 | R_index | nature | score | dis_7 | dis_8 | oil | ind* | int* | STOXX600 | int | rwage | HICP | ind | BBD | 1 | | | | | | | | | | | | | | | | polit | 0.57 | 1 | | | | | | | | | | | | | | | PI6 | 0.08 | -0.17 | 1 | | | | | | | | | | | | | | R_index | 0.71 | 0.38 | 0.25 | 1 | | | | | | | | | | | | | nature | -0.07 | 0.04 | -0.06 | 0.14 | 1 | | | | | | | | | | | | score | 0.88 | 0.60 | 0.32 | 0.89 | 0.20 | 1 | | | | | | | | | | | dis_7 | 0.19 | -0.20 | 0.62 | 0.48 | 0.30 | | 1 | | | | | | | | | | dis_8 | 0.05 | -0.01 | 0.07 | 0.06 | 0.19 | 0.09 | 0.06 | 1 | | | | | | | | | oil | -0.30 | -0.46 | 0.12 | -0.15 | 0.24 | -0.23 | 0.35 | 0.07 | 1 | | | | | | | | ind* | 0.25 | 0.28 | -0.49 | -0.03 | 0.02 | 0.05 | -0.82 | -0.03 | 0.11 | 1 | | | | | | | int* | -0.54 | -0.48 | -0.35 | -0.52 | -0.13 | -0.67 | -0.60 | -0.23 | 0.25 | 0.13 | 1 | | | | | | STOXX600 | 0.19 | 0.33 | -0.25 | -0.04 | 0.01 | 0.07 | -0.63 | 0.19 | -0.23 | 0.69 | 0.05 | 1 | | | | | int | -0.48 | -0.35 | -0.04 | -0.35 | -0.25 | -0.50 | -0.15 | -0.24 | -0.08 | -0.52 | 0.64 | -0.46 | 1 | | | | rwage | -0.28 | -0.17 | -0.35 | -0.42 | -0.26 | -0.46 | -0.04 | -0.30 | -0.46 | -0.11 | 0.45 | -0.29 | 0.55 | 1 | | | HICP | 0.65 | 0.54 | 0.21 | 0.63 | 0.14 | 0.74 | 0.43 | 0.06 | 0.00 | 0.34 | -0.74 | 0.03 | -0.78 | -0.73 | 1 | | ind | -0.37 | -0.08 | -0.53 | -0.57 | -0.20 | -0.58 | -0.64 | -0.26 | -0.26 | 0.17 | 0.69 | 0.11 | 0.52 | 0.71 | -0.67 | 1 |
Source: Authors’ calculations
Uncertainty indicator
unc | STOXX600 | oil | ind* | int* | unc | int | rwage | HICP | ind | BBD | 0.25 | 0.16 | 0.06 | 0.04 | 0.01 | 0.01 | 0.09 | 0.02 | 0.36 | polit | 0.24 | 0.13 | 0.06 | 0.04 | 0.04 | 0.01 | 0.08 | 0.02 | 0.37 | PI6 | 0.26 | 0.16 | 0.05 | 0.01 | 0.01 | 0.01 | 0.09 | 0.03 | 0.34 | R_index | 0.23 | 0.15 | 0.06 | 0.04 | 0.04 | 0.01 | 0.09 | 0.02 | 0.34 | nature | 0.25 | 0.15 | 0.06 | 0.05 | 0.01 | 0.01 | 0.09 | 0.02 | 0.36 | score | 0.25 | 0.16 | 0.06 | 0.04 | 0.01 | 0.01 | 0.09 | 0.02 | 0.36 | dis_7 | 0.12 | 0.21 | 0.12 | 0.10 | 0.00 | 0.01 | 0.08 | 0.03 | 0.33 | dis_8 | 0.13 | 0.19 | 0.11 | 0.09 | 0.03 | 0.01 | 0.08 | 0.02 | 0.34 |
Keywords | Logical conjunction I | Logical conjunction II | economy
economic | prime minister
minister
parliament
Croatian National Bank
CNB
European Central Bank
ECB
International Monetary Fund
IMF
European Commission
EC | uncertainty
uncertain
not certain
risk
risky
non-reliable
not reliable |
Keywords | Logical conjunction | economy
economic | recession
crisis |
Keywords | Logical conjunction | elections
electoral
dismissal
dismissed
migrant
asylum
Schengen
trial
accusation
accused
investigation
investigated
corruption
corruptive
corrupt
affair
prison
custody | prime minister
president
minister
Parliament
Croatian National Bank
CNB
European Central Bank
ECB
International Monetary Fund
IMF
European Commission
EC |
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Keywords
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Logical conjunction
|
|
flood
hail
drought
earthquake
epidemic
|
Croatia
|
|
Keywords
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Logical negation
|
|
tax
|
dismissal
dismissed
appointment
appointed
municipality
city
county
|
Variable | Description | Time span | Source | int | 3-month money market interest rate (ZIBOR) | 2002 M11 – 2016 M12 | Eurostat | rwage | Real net wage; 2010=100; deflated by the Harmonized Index of Consumer Prices | Eurostat | HICP | 2010=100; all categories of goods included | Eurostat | ind | Industrial production index; 2010.=100 | Eurostat | STOXX600 | Stock index of 600 companies from 17 developed European economies | Thomson Reuters database | oil | Brent Europe; in USD | U.S. Energy Information Administration | ind* | Industrial production index; 2010.=100 | Eurostat | int* | 3-month money market interest rate (EURIBOR) | Eurostat | BBD, R_index, PI6, nature and polit | Uncertainty indicators | 2002 M11 – 2016 M12 | Authors’ calculation | dis_4, dis_7 and dis_8 | Disagreement indicators | 2005 M05 – 2016 M12 | European Commission | dis_agr | 2008 M05 – 2016 M12 | European Commission |
Graph 1Baker Bloom Davis index DISPLAY Graph
Graph 2Political uncertainty index DISPLAY Graph
Graph 3The R-word index DISPLAY Graph
Graph 4Natural disaster uncertainty index DISPLAY Graph
Graph 5Fiscal uncertainty indicator DISPLAY Graph
Graph 6Disagreement measures DISPLAY Graph
Table 1Correlation matrix of the analysed variables DISPLAY Table
Table 2Forecasting error variance decomposition (augmented model) DISPLAY Table
Graph 7Industrial production IRFs (shock in economic uncertainty) DISPLAY Graph
Graph 8Comparison of time-varying IRFs of industrial production (shock in economic uncertainty) DISPLAY Graph
Graph 9Time-varying IRFs of industrial production (shock in R_index) DISPLAY Graph
Graph 10Time-varying IRFs of industrial production (shock in PI 6) DISPLAY Graph
Graph 11Time-varying IRFs of industrial production (shock in score) DISPLAY Graph
Table A2R_index DISPLAY Table
Table A3polit DISPLAY Table
Table A4nature DISPLAY Table
Table A6Dataset description DISPLAY Table
* The authors thank two anonymous reviewers for their valuable comments, which have considerably improved the overall quality of the paper.
1 The readers may consult Governor Vujčić’s frequent public presentations (e.g. Vujčić,  2016a; 2016b) as indications of the strict focus on the stability of the HRK/EUR exchange rate.
2 The Croatian version of the BBD index, as suggested in this paper, is founded solely on the media reports on uncertainty. The uncertainty stemming from tax changes will be evaluated separately, just as the influence of forecasting disagreement (quantified through equation 1) on economic activity, will be treated separately. Therefore, a separate label is introduced for the Baker Bloom Davis index ( BBD), different from the US EPU index, comprising all three original components.
3 It should be noted that the list of keywords for the polit indicator does not comprise the terms “country prefect”, “mayor”, etc. The authors’ intention was to extract only those political shocks that might significantly influence the Croatian macroeconomic tendencies. Following that approach, our analysis did not include (inter alia) the corruption scandals related to local potentates like the mayors Vlahović (Dubrovnik), Sabo (Vukovar) or Bandić (Zagreb), and the local prefect Lovrić Merzel (Sisačko-Moslavačka County).
4 The results are very similar if a 4-month or 8-month cumulative is chosen.
5 Answers “don’t know” are excluded from further consideration.
6 Girardi and Reuter ( 2016) obtain rather standard results in comparison to the related literature. All the observed disagreement measures have a negative and strictly short-run effect on the euro area GDP.
7 For the exact wording of all BCS questions, see European Commission ( 2014).
8 The industrial production index is analysed as a proxy for GDP to increase the data frequency and ensure an adequate sample size. The same procedure is also used in similar studies: Bloom ( 2009), Bachmann, Elstner and Sims ( 2013), Jurado, Ludvigson and Ng ( 2015), and Baker, Bloom and Davis ( 2016).
9 score is obtained as the first principal component of the five analysed variables (the obtained eigenvalues is equal to 2.1645). The corresponding weights (loadings) are: 0.61 for BBD, 0.22 for nature, 0.32 for PI6, 0.28 for polit and 0.63 for R_index. It can therefore be concluded that the lion’s share of aggregate uncertainty can be attributed to media reports ( BBD) and recession ( R_index). They are followed by tax changes and political instability, while the natural disasters have the weakest influence. The proportion of the total variance explained by the first component is 0.4329. It should be noted that Baker, Bloom and Davis ( 2016) apply a similar strategy of aggregating different types of uncertainty, but they apply arbitrarily chosen weights.
10 It is important to notice that the constant term is not included in equation (2) for simplicity. However, it has been included in the empirical estimation of the model in this paper.
11 The results are available upon request.
12 We also tried to quantify uncertainty through a GARCH estimation of conditional variance of the industrial production index (in line with e.g. Fountas et al., 2006), but that variable was negligibly positively correlated to economic activity. The same conclusion is also drawn for Google trends data (frequency of Web searches by the terms “economic crisis” and “recession”) for Croatia. Therefore, all of these alternatives are, just as dis_4 and dis_agr, excluded from further analysis.
13 The results of the initial and augmented model (impulse response functions and forecasting error variance decompositions) are qualitatively very similar. To save space, only the augmented model results are shown here.
14 In cases when the chosen lag order was not sufficient to eliminate autocorrelation from the model, additional lags were successively added to the model up to the non-rejection of the null hypothesis of a Lagrange Multiplier autocorrelation test of 12 th order.
15 We suppress the IRFs resulting from shocks in variables BBD and nature, since these were not shown to be significantly different from zero. They are available upon request.
16 Alternatively, STOXX 600 was replaced by oil (with the average share in the forecast error variance of industrial production equal to 16.38%). The results obtained that way showed to be qualitatively very similar.
17 For each of the stated indicators, somewhat different date points are considered, but the basic conclusions have remained the same.
18 IRFs for BBD, polit, and nature are left out because they were not statistically significant.
19 As a robustness check, we also attempted to redo the analysis with quarterly data for the Croatian economy (comprising GDP as the dependent variable and quarterly versions of other variables, obtained as averages of the corresponding monthly observations). However, due to there being too few data points and to the complexity of the assumed relationships between the observed variables, the utilized numerical methods used to estimate the model parameters were not able to converge to stable estimates.
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