JAN GAJDA EKONOMETRIA PDF

Most widely held works by Jan Bogusław Gajda. Ekonometria praktyczna by Jan Bogusław Gajda(Book) 4 editions published between and in Polish. Jan gajda malgorzata grocholinska michal kasiel natalia lobejko karina lysakowska oliwier malinowski sandra papis natalia piekarska bartosz rutkowski jan. Course coordinators. Jan Gajda Gajda J., Prognozowanie i symulacje a decyzje gospodarcze, wyd. C. H. Beck, Warszawa Ekonometria. Prognozowanie.

Author: Akinoktilar Gakus
Country: Lithuania
Language: English (Spanish)
Genre: Automotive
Published (Last): 25 June 2007
Pages: 468
PDF File Size: 10.94 Mb
ePub File Size: 12.64 Mb
ISBN: 879-7-64509-631-6
Downloads: 90131
Price: Free* [*Free Regsitration Required]
Uploader: Mazugis

Additional information registration calendar, class conductors, localization and schedules of classesmight be available in the USOSweb system:. An analysis of ex post and ex ante forecasting errors.

Deterministic and stochastic simulation.

Gajda, Jan Bogusław

An example of the seasonality of economic phenomena. Skills of building and estimating econometric models and using them in practice. Student is able to: Time series decomposition seasonality, skonometria, error. Stationary and non stationary time series.

The least-squares method in the matrix notation, properties of the MNK estimators. Beck, Warszawa, Welfe A. Input-output table in static approach and balance equations.

Ekonometria praktyczna – Jan Bogusław Gajda – Google Books

Evaluation is based on tutorial exercises and individually prepared project at the end of the semester. Placet, Warszawa 5. Neural networks in forecasting. Single-equation descriptive models 2. Additional information registration calendar, class eionometria, localization and schedules of classesmight be available in the USOSweb system: Structural and non-structural models.

  KABBALISTIC CYCLES AND THE MASTERY OF LIFE PDF

Analysis with basic statistic. The main objective of the course is acquainting students with the simulation and forecasts methods. Passing exercises based on the project, a written work consisting of a task test and activity in class – participation in solving practical problems classes 15h, current work 15h, preparation for passing 30h – 60h. The main aim of the laboratory is to familiarize students with practice of econometric modelling. Wide using of computer programs to built econometric models e.

On-line services of the University of Warsaw.

Part I by Clopper Almon A. Descriptive econometric models – general characteristics and examples of applications.

Time series forecasting rules. Generating values from a statistical distribution.

Time series analysis — deterministic and stochastic trends in the time series models. Introduction to discrete event simulation — simple simulation, simulation on the crate. Moving average forecasting method. Using formulas in Excel — overview. Time series forecasting ekonometfia.

Jan Gajda Ekonometria Pdf Download | inuphspearmap

Sampling from probability distributions — ekonoemtria transform method. You are not logged in log in. The projects should involve three appropriate methods and justification of the best one chosen. Discrete event simulation — steady-state models. Factors of material consumption, labor consumption and their interpretation.

  JUSTINE ELYOT PDF

Introduction to optimization with the Excel Solver tool. Additional information gauda calendar, class conductors, localization and schedules of classesmight be available in the USOSweb system: Faculty of Economics and Sociology.

There will be also theoretical written exam. Showing them examples of practical use of econometric methods. Input-output models – input-output table in terms of quantity and value – technical factors and basket factors – Leontief’s model and its solutions in terms of quantity and value – price model. Methods of estimation of econometric models, conditions of their applicability.

Statistical evaluation of the econometric model verification of appropriate statistical hypotheses, methods for assessing the goodness of model estimation. Assumptions of the stochastic structure of the model, examination of the properties of the random component, selection of estimators, selection of the estimation method.

Using dynamic simulation to improve production. Discrete event simulation — dynamic jzn model changes in a system in response to input signals. Written report should be submitted.