Fig 1.
Unemployment rate in the Visegrad countries.
The group of countries is evidently quite heterogenous in the unemployment rates. The Hungarian rate starts at the lowest level but increases stably during the whole period. The Czech rate begins at quite low levels and decreases up to the outbreak of the financial crisis when the rate surges up until 2010, after which it remains quite stable. The Polish and Slovakian rates commence at very high levels of unemployment, which decrease again up until the outbreak of the crisis, after which they change trends, similarly to the Czech rate.
Table 1.
Jarque-Bera test with the null hypothesis of a symmetric distribution with no excess kurtosis is used here, p-values are reported in the brackets.
Fig 2.
Google search queries for the job-related terms in the Visegrad countries.
The patterns are again quite heterogenous, and the connection between the Google searches and the unemployment rates can be observed for the Czech and Hungarian rates. For the other two, the connection is not visible by the naked eye. Detailed treatment of the interconnections is given in the Results section of the text. Google data are registered trademarks of Google Inc., used with permission.
Table 2.
Augmented Dickey-Fuller test (ADF) for a presence of unit root and KPSS test for stationarity are used. *, ** and *** stand for statistical significance at 10%, 5% and 1% levels, respectively. Number of lags for the tests is based on the Akaike Information Criterion (AIC) selection.
Table 3.
Nowcasting summary (in-sample).
The whole analyzed period 01/2004-12/2013 is covered here. Model in Eq 2 is used here with varying maximum lag L. Joint significance of variables is a simple F-test based on heteroskedasticity and autocorrelation consistent (HAC) standard errors (p-values are reported in the brackets). Adjusted coefficient of determination controls for the number of independent variables used in the model.
Table 4.
Nowcasting summary (out-of-sample).
The period between 01/2004 and 12/2011 is used for model fitting and the rest of the period between 01/2012 and 12/2013 is used for the forecasting comparison. Diebold-Mariano test described in the Methods section compares the “Google model” defined in Eq 2 to the base model defined in Eq 3 with a null hypothesis of no difference of forecasting accuracy versus the alternative of the “Google model” being more accurate.