Explanation of the Tests
When comparing macroeconomic parameters over time, clear correlations can be observed. These correlations are highly interesting for economic policy. Some empirical observations contradict prevailing theoretical expectations.
We test these correlations primarily with regression tests (SPSS Statistical software). Whether the correlations are random or systematic, and whether they are strong or weak, we judge on the basis of statistical parameters. The significance, also called the P-value, tells whether a correlation is random or not. This is also called the error probability. The randomness is between 1.000 = random and 0.000 = free of randomness. And the beta value measures the strength of the influence of one variable on another dependent variable, between 0.0 = no influence and 1.0 = completely determining.
Finally, in the case of relevant results, it must be clarified whether a cause and effect relationship exists. If significant time lags are observed, then the trailing variables can be considered as effects. Otherwise, additional facts must be used to establish cause or effect. Otherwise, it remains with the mere determination of correlations.
We define:
Significance (sig.):
0.000 Highly significant
Up to 0.050 Significant
0.051 - 0.099 Weakly significant
0.100 and greater Not significant
Beta:
0.8 and greater Highly dominant correlation
From 0.6 up to but excluding 0.8 Dominant correlation
From 0.4 up to but excluding 0.6 Medium correlation
From 0.2 up to but excluding 0.4 Low correlation
Under 0.2 Weak/no correlation
Durbin-Watson (DW):
Normally, the estimation errors of regression tests, i.e., the deviation of the estimated values from the values to be tested, are random. However, if there are systematic correlations between them, this is called autocorrelation.
The Durbin-Watson test determines whether autocorrelation exists for the correlation tested. If yes, then the calculated quality values are too high. To avoid such estimation errors of the regression, the Cochrane-Orcutt corrected estimation procedure is applied.
Abbreviations list:
SPSS AREG Cochrane-Orcutt autoregression test
N Series length
R2K Corrected R-squared
DW Durbin-Watson measure of autocorrelation
Regressor Quantitative relationship between the
independent variables and the dependent variable
Beta Strength of the influence of the independent
variables on the dependent variable
sig. Significance, size of the probability of error of the
test result.
In the text, we summarize the findings of regression tests:
Significance The quality of the observed correlation, the
smaller the significance value, the lower the
probability of error
Beta Highly dominant means that only minor other
influences can exist in addition to the identified
influence.
Dominant means that weaker other influences can
exist alongside it.
Medium means that other, similarly strong
correlations may exist alongside it.
Minor means that other stronger influences may
exist alongside it.
For economic policy, only clear and weighty correlations are of interest, the influence of which have noticeable effects. Therefore, we limit ourselves to the examination of such weighty economic correlations. In addition to the correlations identified here, there are certainly multiple weaker correlations that we do not deal with.