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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.

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Additional information registration calendar, class conductors, localization and schedules of classesmight be available in the USOSweb system:. The projects should involve three appropriate methods and justification of the best one chosen.

Building a worksheet-based simple simulation. Discrete event simulation — dynamic simulations model changes in a system in response to input signals. Concept and classification of multipliers 3. Forecasting based on an econometric model. The main aim of the laboratory is to familiarize students with practice of econometric modelling. Descriptive econometric models – selection of variables for the model and approximation function, construction, estimation of MNK, interpretation, evaluation and application in logistic decisions.

Beck, Warszawa, Welfe A. Time series forecasting models. Total for the subject: Part I by Clopper Almon A. Additional information registration calendar, class conductors, localization and schedules of classesmight be gajra in the USOSweb system:.


Business Forecasting – University of Warsaw

Modeling factors and objectives 2. Input-output table in static approach and balance equations.

Analysis with basic statistic. Metody i ich zastosowanie, wyd. Time series forecasting rules. The project requires the theory, described during classes, to be applied to a specific problem in finance or economics.

Ekonometria: z programem DEMS autorstwa Wojciecha Zatonia – Jan Bogusław Gajda – Google Books

Time series decomposition seasonality, trend, error. 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 rkonometria price model. Methods of estimation of ekonomdtria models, conditions of their applicability. Measurement of forecasting error ex ante and ex post. Descriptive econometric models – general characteristics and examples of applications.

Heteroscedasticity and autocorrelation of a random component, testing of appropriate hypotheses. The main objective of the course is acquainting students with the simulation and forecasts methods.

Generating values from a statistical distribution.

An example of the seasonality of economic phenomena. Modeling of economic phenomena – introductory issues 1. Using dynamic simulation to improve production. Ekonometfia i ich zastosowanie, PWE, Warszawa Skills of building and estimating econometric models and using them in practice.

Gajda, Jan Bogusław

Wide using of computer programs to built econometric models e. Definition of forecasts and simulation. Structural and non-structural models. Student is able to: Introduction to optimization with the Excel Solver tool.


Statistical evaluation of the econometric model verification of appropriate statistical hypotheses, methods for assessing the goodness of model estimation. Eknoometria to discrete event simulation — simple simulation, simulation on the crate. Additional information registration calendar, class conductors, localization and schedules of classesmight be available in the USOSweb system: On-line services of the University of Warsaw.

Showing them examples of practical use of econometric methods. Factors of material consumption, labor consumption and their interpretation. Non-measurable jam in econometric models.

Moving average forecasting method. Ability of analysing input-output models. You are not logged in log in. Deterministic and stochastic simulation. Stages of econometric analysis. Introduction to econometrics goals of econometrics, the concept of an econometric model, classification of econometric models.

The implementations and limitations of naive models. Sampling from probability distributions — inverse transform method.