डॉ. बी. आर. अम्बेडकर विश्‍वविद्यालय दिल्ली
Dr. B. R. Ambedkar University Delhi
Established by The Government of NCT of Delhi

Course coordinator and team:  Dr. Krishna Ram, krishna@aud.ac.in

1. Does the course connect to, build on or overlap with any other courses offered in AUD?

This course builds on the MA Economics courses “Statistics and Data Exploration” and “Econometrics and Data Analysis”. It draws examples and applications from the courses “Macroeconomics I” and “Macroeconomics II”

2. Specific requirements on the part of students who can be admitted to this course:

(Pre-requisites; prior knowledge level; any others – please specify)

The MA Economics course “Econometrics and Data Analysis” or equivalent knowledge of econometrics.

3. No. of students to be admitted (with justification if lower than usual cohort size is proposed):

As per SLS norms.

4. Course scheduling (semester; semester-long/half-semester course; workshop mode; seminar mode; any other – please specify):

As per course scheduling norms for MA economics program.

5. How does the course link with the vision of AUD?

The course will help students to implement the spirit of reasoned enquiry that is part of the vision of AUD. It will add to the set of techniques available to them to bring empirical evidence to bear on social scientific questions.

6. How does the course link with the specific programme(s) where it is being offered?

This course complements the macroeconomics core courses offered in MA economics program at AUD by providing tools for empirically studying many of the questions raised in those courses. It will add to the basket of electives in the econometrics area.

7. Course Details:
   1. Summary:

This course will provide an introduction to time series analysis. The course will cover both univariate and multivariate time series analysis. Under univariate analysis the students will learn about the assumptions of stationarity, stationarity tests, autoregressive moving average (ARMA) models, autoregressive integrated moving average (ARIMA) models, including autocorrelation (ACF) and partial autocorrelation (PACF) functions. Under multivariate time series analysis the course discuss vector autoregression (VAR), vector error correction (VECM), and structural vector autoregression (SVAR) models. The course will also be cover the use of software packages to implement the methods studied.

1. 2. Objectives:
1. To familiarize students with the basic algebra and statistics of time series.
2. To acquaint students with the most important time series models used in economics.
3. To teach the art of investigating questions in economics using these models. 
4. 3. Expected learning outcomes:

At the end of the course students should be able to:

1. 1. 1. Define key concepts in time series econometrics.
      2. Prove all theoretical results used except for a few that are too techincal to be included in a course at this level.
      3. Apply time series methods to analyse issues in macroeconomics and finance problems, relations between variables and their effects on each other.
      4. Use time-series software packages.
2. 4. Overall structure (course organisation, rationale of organisation; outline of each module):

The course will begin with univariate models and then subsequently generalize the results to multivariate models. The broad modules are:

1. 1. 1. Basic properties of univariate time series.
      2. Examples of univariate models: ARIMA, ARCH and GARCH. Models with unit roots.
      3. The basic algebraic and statistical properties of VARs and VECMs.
      4. Estimation of VARs and VECMs.
      5. Strategies for identifying structural effects in multivariate time-series models.

Readings:

[C] Cochrane, J.H. “Time Series for Macroeconomics and Finance”, online lecture notes, http://econ.lse.ac.uk/staff/wdenhaan/teach/cochrane.pdf.

[B] Brooks, C. Introductory Econometrics for Finance, Second Edition. Cambridge University Press.2008

[E] Enders W. Applied Econometric Time Series. Second Edition. Wiley India ,2008

[L] Levendis, J. D. (2018). Time series econometrics: Learning Through Replication, Second Edition, Springer International Publishing

[WB] Wichmann, R., & Brooks, C. (2019). R Guide to Accompany Introductory Econometrics for Finance.

[TB] Tao, R., & Brooks, C. (2019). Python Guide to Accompany Introductory Econometrics for Finance. Available at SSRN 3475303.

[MSD] Martin V., Stan Hurn and David Harris. Econometric Modelling with Time Series: Specification, Estimation and Testing. Cambridge University Press.2013

[L] Lutkepohl, H. New Introduction to Multiple Time Series Analysis. Springer. 2005

[KL] KIlian, L and H Lutkepohl. Structural Vector Autoregressive Analysis. Cambridge University Press. 2017

Assessment Plan:

Assessment	Objective	Weight
Class Tests	To assess their understanding of the concepts and lectures taken	One test 50%
Empirical Project	To test their ability to identify economic problems, modeling it using appropriate tools and interpretation of results	50%

 

 

 

 

 

 

 

 

1.  

Contents (week wise plan with readings):

Week	Topic	Reading
	Univariate Time Series
1.	Stochastics Process: Autoregressive (AR),  Moving Average (MA) and Autoregressive Moving Average (ARMA) processes – their properties, conditions for stationary, autocorrelation function (ACF), partial autocorrelation function (PACF),	[E], [C] and [B]
2.	ARIMA models, determination of the order of integration, trend stationary and difference stationary processes, ARCH and GARH	[E],[C] and [B]
3	Unit root tests or tests of nonstationarity – Dickey-Fuller (DF) test, augmented Dickey-Fuller (ADF test) , Phillips-Perron test, KPSS test.	[E] and [C]
	Multivariate Time Series Models
4.	Vector Autoregressive Models	[L], [KL], [MSD] and [B]
5.	Structural VAR	[L], [KL] and [MSD]
6.	Identification by Short-Run Restrictions	[L], [KL] and [MSD]
7.	Estimation Subject to Short-Run Restrictions	[L], [KL] and [MSD]
8.	Identification by Long-Run Restrictions	[L], [KL] and [MSD]
9.	Estimation Subject to Long-Run Restrictions	[L], [KL] and [MSD]
10.	Inference in Models Identified by Short-Run or Long-Run Restrictions	[L], [KL] and [MSD]
11.	Cointegration and Vector Error Correction Models	[L], [KL] and [MSD]
12.	Granger Causality	[L], [KL] and [MSD]

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Pedagogy:

• Instructional strategies: Classroom lectures, presentations.
• Special needs (facilities, requirements in terms of software, studio, lab, clinic, library, classroom/others instructional space; any other – please specify): Classroom with a projector and sound system. Computer lab with recent version of STATA and at least one computer for every two students.
• Expertise in AUD faculty or outside : AUD Faculty
• Linkages with external agencies (e.g., with field-based organizations, hospital; any others)