Central European Journal of Economic Modelling and Econometrics

We develop and study in detail a new family of distributions called Half-logistic Odd Power Generalized Weibull-G (HLOPGW-G) distribution, which is a linear combination of the exponentiated-G family of distributions. From the special cases considered, the model can fit heavy tailed data and has non-monotonic hazard rate functions. We further assess and demonstrate the performance of this family of distributions via simulation experiments. Real data examples are given to demonstrate the applicability of the proposed model compared to several other existing models.

The studies using Mincer equations are generally applied to cross-sectional data at the micro-level. There are however limited studies conducted with macro or panel data for wage equations. Pseudo panel data methods can be applied to empirical studies by creating cohorts from repeated cross-sectional data in the absence of genuine panel data. Difference in both the human and labour resources according to the spatial positions may also affect the prediction of the wage equations. We aim to introduce the application of spatial pseudo panel models by creating cohorts according to the birth years of employees and regions in which they live from the Turkish household labour survey for the period 2010-2015. As a result, we find that the spatial autocorrelation model is appropriate for wage equations of Turkey. We also find that return of education on wages is 11% while return of experience on wages is 4%.

In this article we have described a multiproduct model of economical dynamics of Gale type, in which the changes in production technology (the dynamics of Gale type production spaces) depend upon the scale of targeted investments. Under such assumptions we have proved a so-called “weak” version of a multilane turnpike theorem in the Gale type economy with varying technology which converges to a certain limit technology. It states that in the long periods of time, regardless of the initial state of the economy, the optimal growth processes almost always lie close to the family of steady growth paths with maximum growth rate called the multilane turnpike.

In this paper we consider a class of nonlinear autoregressive models in which a specific type of dependence structure between the error term and the lagged values of the state variable is assumed. We show that there exists an equivalent representation given by a p-th order state-dependent autoregressive (SDAR(p)) model where the error term is independent of the last p lagged values of the state variable (y_{t-1},…,y_{t-p}) and the autoregressive coefficients are specific functions of them. We discuss a quasi-maximum likelihood estimator of the model parameters and we prove its consistency and asymptotic normality. To test the forecasting ability of the SDAR(p) model, we propose an empirical application to the quarterly Japan GDP growth rate which is a time series characterized by a level-increment dependence. A comparative analyses is conducted taking into consideration some alternative and competitive models for nonlinear time series such as SETAR and AR-GARCH models.