Description of the Data and Methods Used
The method of the ordinary least squares is used for the equations (1) and (2) mentioned above. A total of eight regression functions were estimated separately, i.e. individually. Each of the equations was examined separately as a single equation model. In the simul- taneous equation models (SEM), more than one dependent variable is involved and the model necessitates as many equations as is the number of endogenous variables. The fact that the endogenous variable in one equation may appear as an explanatory variable in other equation of the system is a unique feature of simultaneous equation models. For this reason, such an endogenous explanatory variable becomes stochastic and is usually correlated with the disturbance term of the equation in which it appears as an explanato- ry variable. In this situation the OLS method may not be applied. One of the crucial assumption of the method of OLS is that the explanatory variables are either non- stochastic or, if stochastic (random), distributed independently of the stochastic disturb- ance term. If neither of these conditions is met, then the least-squares estimators are not biased but also inconsistent. For this reason we consider a model with independent equations, despite the fact that there is a limited amount of information in estimation procedures for individual reaction functions (Gujarati and Porter, 2009).
Furthermore, statistical and economic verification is made and basic econometric tests (econometric verification) are performed. The data obtained were statistically analysed, extreme values replaced, tests of stationarity of time series conducted using ADF (Augmented Dickey-Fuller test), as well as autocorrelation (ACF) and partial autocorre- lation (PACF) tests.7 The next step is to determine the extent of dependence of both policies on business cycle, how authorities behave during achieving their aims, how they react to the changing economic environment and, in particular, to what extent and how one policy reacts to that of the other.