Humanities and Social Sciences

Central European Journal of Economic Modelling and Econometrics

Content

Central European Journal of Economic Modelling and Econometrics | 2015 | No 2 |

Abstract

Various approaches have been introduced over the years to evaluate information in the expected utility framework. This paper analyzes the relationship between the degree of risk aversion and the selling price of information in a lottery setting with two actions. We show that the initial decision on the lottery as well as the attitude of the decision maker towards risk as a function of the initial wealth level are critical to characterizing this relationship. When the initial decision is to reject, a non-decreasingly risk averse decision maker asks for a higher selling price as he gets less risk averse. Conversely, when the initial decision is to accept, non-increasingly risk averse decision makers ask a higher selling price as they get more risk averse if information is collected on bounded lotteries. We also show that the assumption of the lower bound for lotteries can be relaxed for the quadratic utility family.

Go to article

Abstract

In 1993 Engle and Kozicki proposed the notion of common features of which one example is a serial correlation common feature. We say that stationary, non-innovation processes exhibit common serial correlation when there exists at least one linear combination of them which is an innovation. Later on in 1993 Vahid and Engle combined the notions of cointegration among I(1) processes with common serial correlation within their first differences. It is commonly known that cointegrated time series have vector error correction (VEC) representation. The existence of common serial correlation leads to an additional reduced rank restriction imposed on the VEC model’s parameters. This type of restriction was later termed a strong form (SF) reduced rank structure, as opposed to a weak one introduced in 2006 by Hecq, Palm and Urbain.

The main aim of the present paper is to construct the Bayesian vector error correction model with these additional strong form restrictions.

The empirical validity of investigating both the short- and long-run co-movements between macroeconomic time series will be illustrated by the analysis of the price-wage nexus in the Polish economy.

Go to article

Abstract

The paper considers the modeling and estimation of the stochastic frontier model where the error components are assumed to be correlated and the inefficiency error is assumed to be autocorrelated. The multivariate Farlie-Gumble-Morgenstern (FGM) and normal copula are used to capture both the contemporaneous and the temporal dependence between, and among, the noise and the inefficiency components. The intractable multiple integrals that appear in the likelihood function of the model are evaluated using the Halton sequence based Monte Carlo (MC) simulation technique. The consistency and the asymptotic efficiency of the resulting simulated maximum likelihood (SML) estimators of the present model parameters are established. Finally, the application of model using the SML method to the real life US airline data shows significant noise-inefficiency dependence and temporal dependence of inefficiency.

Go to article

Editorial office

Editors

JACEK OSIEWALSKI, Cracow University of Economics, Poland
ALEKSANDER WELFE, University of Lodz, Poland

Co-Editors
MAŁGORZATA DOMAN, University of Economics, Poznań, Poland
RYSZARD DOMAN, Adam Mickiewicz University, Poznań, Poland
JAKUB GROWIEC, SGH Warsaw School of Economics, Poland
MAREK GRUSZCZYŃSKI, SGH Warsaw School of Economics, Poland
BOGUMIŁ KAMIŃSKI, SGH Warsaw School of Economics, Poland
MARCIN KOLASA, SGH Warsaw School of Economics, Poland

Contact

CEJEME Editorial Office - Ms. Karolina Jaszczyk, Polish Academy of Sciencies - Lodz Branch
Piotrkowska Str. 137/139, 90-434 Lodz, Poland
e-mail: cejeme@pan.pl

Instructions for authors

Submission Guidelines and Instructions for Authors of accepted papers please visit: http://cejeme.org/submissionguidelines.aspx

This page uses 'cookies'. Learn more