Stochastic Optimization in Econometric Models – A Comparison of GA, SA and RSG

This paper shows that, in case of an econometric model with a high sensitivity to data, using stochastic optimization algorithms is better than using classical gradient techniques. In addition, we showed that the Repetitive Stochastic Guesstimation (RSG) algorithm –invented by Charemza-is closer to Simulated Annealing (SA) than to Genetic Algorithms (GAs), so we produced hybrids between RSG and SA to study their joint behavior. The evaluation of all algorithms involved was performed on a short form of the Romanian macro model, derived from Dobrescu (1996). The subject of optimization was the model’s solution, as function of the initial values (in the first stage) and of the objective functions (in the second stage). We proved that a priori information help “elitist “ algorithms (like RSG and SA) to obtain best results; on the other hand, when one has equal believe concerning the choice among different objective functions, GA gives a straight answer. Analyzing the average related bias of the model’s solution proved the efficiency of the stochastic optimization methods presented.underground economy, Laffer curve, informal activity, fiscal policy, transitionmacroeconomic model, stochastic optimization, evolutionary algorithms, Repetitive Stochastic Guesstimation

SURVEY DESIGN USING INDIVIDUAL NUMERICAL SCALES IN THE FRAMEWORK OF ANALYTIC HIERARCHY PROCESSES

This paper discusses the adequacy of a generalization of Saaty’s 1-9 scale proposed by Liang at all (2008) in the attempt to identify individual scales. Several surveys in completely different areas were conducted on different topics. Comparisons among the consistency index-as a measure of a “good answer” and the previously mentioned scale reveal a non monotonic correspondence among those two criterions. Also, the individual scale considered – which is in itself a generalization of other similar scales for measuring individual responses – is not uniquely determined for a single respondent and is very often contradictory. Yet, the potential benefits in determining individual scales of measurement are enormous and maybe the most important one is getting rid of the myth of the good appliance of the “law of large numbers” in social sciences.Analytic Hierarchy Processes, knowledge sharing, mapping, numerical scale, simulated annealing, verbal responses.

Repetitive Stochastic Guesstimation for Estimating Parameters in a GARCH(1,1) Model

A behavioral algorithm for optimization - Repetitive Stochastic Guesstimation (RSG) - is adapted, with complete proofs for its global convergence, for estimating parameters in a GARCH(1,1) model, based on a very small number of observations. Estimators delivered by this algorithm for the example of a GARCH(1,1) model are dependent on some computational capabilities - namely number of iterations and replications performed. In this context, the Large Numbers Law might be applied in a completely different dimension. An alternative toward waiting until the historical data series are recorded (while the underling process may change several times) is to use computers for correctly extracting information from the most recent data. Given the existent computational support, it is also possible to determine estimates for the rates of convergence. As a result, potential benefits of this econometric technique can be gained in case of very young financial markets from Eastern European countries. Also, prediction and political decisions based on these estimations are properly grounded.RSG, GARCH Model, financial markets

Intergenerational knowledge transfer in the academic environment of knowledge-based economy

In the immediate future, intergenerational knowledge transfer is one of the knowledge-based economy’s main challenges since an inner motivational force powers knowledge transfer. Knowledge transfer from individuals to groups and organization must follow knowledge creation in order to transform individual into organizational knowledge, along the epistemological dimension of the Nonaka’s knowledge dynamics model. Moreover, the knowledge intensive organizations increase their fluxes of knowledge across different age layers and different departments, reducing in the same time the company knowledge loss. The academic environment is, by nature, an age layered field or a nested functional structure. Intergenerational knowledge transfer becomes any university main driving force, while understanding its dynamics is important for academic life improvement. The purpose of the paper is to present some of our research results in the field of intergenerational knowledge transfer in the academic environment of the knowledge-based economy. We performed a qualitative and quantitative research of the knowledge transfer process in the academic environment, using the Analytic Hierarchy Processes (AHP). We analyzed the faculty staff attitudes toward cooperation, competition, and innovation as main priorities in performing research, writing books and publishing scientific papers. The above-mentioned activities are based on intergenerational knowledge transfer and lead to learning processes at individual and organizational levels. Respondents are members of the academic staff of economics and business faculties from the main Romanian universities.knowledge, knowledge-based economy, knowledge transfer, university