Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Refine search results

Journals help

  1. 3 Przegląd Statystyczny

Years help

  1. 3 2007

Authors help

  1. 3 Wrobel-Rotter R.

Results found: 3

first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1

DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELS: AN EXAMPLE FOR THE POLISH ECONOMY

100%
Wrobel-Rotter R.
Przegląd Statystyczny
|
2007
|
vol. 54
|
issue 3
26-48
EN
The paper discusses fundamental problems concerning the construction and estimation of Dynamic Stochastic General Equilibrium (DSGE) models on the basis of an example taken from the literature. The DSGE models constitute at present the major tool for the monetary policy analysis as a consequence of the existence of estimation methods and considerable flexibility ot the models that allows to formulate and test a wide range of economic hypotheses. Models belonging to the class of DSGE combine in one specification the optimization behavior of consumers and producers with mechanisms that allow to model the nominal and real rigidities observed at the macroeconomic level. The first part of the article contains the overview of the most important elements of the model with the discussion of utility and profits maximizations problems of producers and consumers that are solved in a decision making process. The second part of the text presents log-linearised structural equations, shortly discusses possible techniques of model solutions and subsequently sketches the Bayesian approach to the parameters estimation. The structural equations of any DSGE model are derived from the first order conditions of the agents' optimization problems, the resource constraints, policy rules, equations describing flows between countries and stochastic processes governing the exogenous variables and shocks. Nominal and real delays in adjustments of macroeconomic variables after a stochastic shock are modeled by introduction of the time intervals that restrict frequency of the optimization of prices and wages over time. The most widely used in practice in the Calvo mechanism of price and wage setting. The structural equations of the DSGE model form a nonlinear rational expectations system that can be solved using the nonlinear methods or after logarithmic linearization can be solved by the linear techniques . The possibility of the likelihood construction enables estimation of the structural parameters including the fundamental parameters characterizing the technology and preferences. The Bayesian methods additionally allow to include in the process of parameters estimation the prior information of the economy what is typically interpreted as an introduction of some evidence obtained from the microeconomic research. The estimated DSGE model can be used as a tool for the standard macroeconomic analysis concerning impulse responses, forecasting, shocks propagation and duration etc.
2

THE BAYESIAN COMPARISON OF FLEXIBLE FUNCTIONAL FORMS AS STOCHASTIC COSTS FRONTIERS

100%
Wrobel-Rotter R.
Przegląd Statystyczny
|
2007
|
vol. 54
|
issue 2
5-27
EN
Tne main goal of the paper is to discriminate among stochastic frontier cost functions. The unknown cost function is approximated by the locally flexible functional forms: the translog, generalised Leontoef and McFadden and two non-flexible forms: the Cobb-Douglas model with varying returns to scale and the Cobb-Douglas model with the Muentz-Schatz series expansion of order one for the price aggregator. Numerical approximations of moments of marginal posterior distributions are accomplished by implementation of the Markov Chain Monte Carlo techniques, that is the Metropolis-Hastings algorithms within Gibbs sampling. Marginal data density is obtained on the basis of the S. Chib's method proposed in 1995. Detailed discussion of specification of the prior distribution for the technology parameters with the posterior sensitivity analysis is included in the paper. After assuming the same prior standard deviation for the technology parameters in each model the authoress concludes that ther most probable a posteriori is the translog model, whose posterior probability is almost equal to one. The generalised Leontief and McFadden flexible forms are equally probable and are much more supported by the data than the non-flexible forms, such as the Cobb-Douglas. The ranking of models does not change when the dispersion of the prior distribution of technology parameters increases evenly but it can be noticed that the marginalised likelihood drops slightly faster for the Generalised McFadden model than for the Generalised Leontief and Muentz-Szatz series expansions of order one. After assuming the same marginal prior distribution of all individual technology parameters in each model the posterior model probabilities are not affected by the prior model probabilities. The marginal posterior distribution of technology characteristics and the cost efficiency, weighted by the posterior model probabilities, would be equal to the one obtained according to translog.
3

THE BAYESIAN COMPARISON OF CONDITIONAL INPUT DEMAND SYSTEMS IN THE PRESENCE OF INEFFICIENCY

100%
Wrobel-Rotter R.
Przegląd Statystyczny
|
2007
|
vol. 54
|
issue 1
5-19
EN
The main goal of the paper is to discriminate among stochastic conditional demand systems. In this paper the authoress presents the Bayesian comparison of conditional input demand functions derived from stochastic cost frontiers approximated by different flexible functional forms. The advantage of applying input demand (or cost share) systems over the production of cost frontiers relays on the fact that they allow for modelling both technical and allocative efficiency. The technical efficiency is related to the excessive inputs usage in a production process while the allocative inefficiency is related to the incorrect proportions of inputs. The composed error structure of each of the equations of the conditional demand systems is defined by the Bayesian random effects model, so-called common efficiency distribution model. In the empirical analysis there are considered three demand systems derived from the microeconomic short-run variable cost function approximated by three locally flexible functional forms: the translog, the Generalised Leontief and the Generalised McFadden. Details of prior specification are discussed on the basis of the short-run Generalised Leontief input demand system. The comparison of models is conducted by marginal likelihood that is calculated by implemetation of Markov Chain Monte Carlo terchniques. An example of input demand systems estimated using data obtained from 31 Polish electric power stations illustrates the methodology. The results show insignificant differences in the description of the underlying production process by competing systems but the authoress founds our significantly different explanatory power of various conditional demand models.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.