November - 2018
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Tantárgy adatlap

Econometrics II.

Tantárgy adatlap letöltése: Letöltés

A tantárgy kódja: 4ST14NAK32B
A tantárgy megnevezése (magyarul): Econometrics II.
A tantárgy neve (angolul): Econometrics II.
A tanóra száma (Előadás + szeminárium + gyakorlat + egyéb): 13 lectures + 13 seminars
Kreditérték: 6
A tantárgy meghirdetésének gyakorisága: once every fall semester
Az oktatás nyelve: English
Előtanulmányi kötelezettségek: Econometrics I.
A tantárgy típusa: core
Tantárgyfelelős tanszék: Statisztika Tanszék
A tantárgyfelelős neve: Keresztély Tibor

A tantárgy szakmai tartalma: The course, covering various core topics in econometrics, is composed of two parts. The first part presents discrete dependent variable models which can be used for explaining and predicting categorical outcomes in social sciences. Additionally, a concise treatment of Maximum Likelihood estimation and hypothesis testing is provided. The second part covers the basics of time series analysis, starting with the structure of time series data, discussing the distinction between stable and unstable processes, showing how to calculate probability moments and use them for characterizing the data generation process, and providing an overview of several widely used time series models. Two major objectives of this part are to (1) present and discuss the properties of a variety of time series models applicable for different types of phenomena in social sciences, and (2) explain and practice appropriate methods for forecasting the variable(s) of interest in the afore mentioned models.

Évközi tanulmányi követelmények: Regular attendance of lectures and seminars is strongly recommended.

Vizsgakövetelmény: final exam for 55 points

Az értékelés módszere: End-term grade is based on the total score obtained on (1) the final exam, and (2) three out of four mid-term quizzes.

Tananyag leírása: (1) Revisiting the general linear model. (2) Discrete valued dependent variable models. (3) Maximum Likelhood estimation and testing. (4) Structure of time series data. Deterministic time series analysis and forecasting. (5) Statistical properties of stochastic processes. White noise. (6) Stationary AR, MA, and ARMA processes. (7) I(1) processes: random walk, ARIMA. Unit root tests. Geometric processes. (8) Transformations to achieve stationarity. Box-Jenkins modeling method. (9) Forecasting of stationarizable processes. (10) Time series models containing exogeneous variables. Small and large sample estimation. (11) Residual autocorrelation. (12) Distributed lags models: FDL, GDL, ARDL. (13) VAR models. Granger causality. (14) Time series modeling in practice.

Órarendi beosztás: lectures on Friday, 9:50 – 11:20; seminars (2 groups) on Friday, 11:40 – 13:10 and 13:40 – 15:10.

Kompetencia leírása: By the end of the course, students will have gained competence in analyzing and predicting categorical outcomes in economics and social sciences; analyzing the temporal dynamics of economic phenomena; building econometric models which adequately capture the interrelation among multiple time series; exploring, quantifying, and testing for causal relationships among multiple time series; forecasting time series in economics and social sciences based on their interrelation with exogenous variables.

Félévközi ellenőrzések: 4 mid-term quizzes for 15 points each

A hallgató egyéni munkával megoldandó feladatai: none

Szak neve: Applied Economics

Irodalomjegyzék:
Kötelező irodalom:

  • Detailed lecture notes will be provided on-line.

Ajánlott irodalom:

  • Jeffrey M. Wooldridge: Introductory Econometrics – A Modern Approach (4th Edition)
Ajánlott irodalmak:
Kötelező irodalmak:

 
A tantárgy oktatói:

Ruzsa Gábor Imre

Utolsó módosítás: 2018-09-11 11:35:30

Kurzusok

Kurzus kódTipusFélévOktatói
EElmélet2018/19/1Ruzsa Gábor Imre
G1Gyakorlat2018/19/1Ruzsa Gábor Imre
G2Gyakorlat2018/19/1Ruzsa Gábor Imre
EElmélet2018/19/1Ruzsa Gábor Imre


A "Tantárgyfelelős tanszék", a tantárgyfelelős neve a tantárgy oktatói és a kurzusinformációk automatikusan frissülnek a tanulmányi rendszerünk alapján.