Usually macroeconomic time series show a tendency of growing over time and thus can be characterized as trending. The Beveridge-Nelson decomposition assumes that the data is non-seasonal, since 'seasonal adjustment logically precedes business cycles analysis'. The framework of Nelson-Plosser (NP) analysis is based on investigating two classes of non-stationary models. The first class of such models consists of deterministic function of time, known as time trend; and stationary stochastic process with zero mean. These models are called trend-stationary processes (TSP). The Hodrick-Prescott (HP) filter is considered to be among the most favored empirical techniques of researchers who attempt to separate cyclical behavior from the long run path of economic time series, or more precisely, the aim of the filter is to remove low frequency variation from an economic time series data. Nonetheless, this parameter does not have an intuitive interpretation and its choice generated great discussion in time series analysis literature, especially when concerning the frequency of the data observed.