Forecasting and predicting for future is a key factor for planning and development of any organization in this running world. As now, world has become a global village so our market has become much advance as compared to previous decades. Now the market demand is to planning for future based on existing and previous knowledge. In these conditions empirical evidences are of much importance. With the passage of time, world is changing to amazing extent. Changing in variables due to time is a prominent aspect now. The aim of this course is to impart basic and applied knowledge about Time Series and its applications in different fields with an emphasis on practical aspects of the interpretation of statistically based conclusions in research. It deals with the method of data collection, description measures of data interpretation of results, model selection, decision making and Forecasting. This course is designed for the advance modeling and forecasting of time series data. This focuses on advance time series techniques and forecasting methods.Forecasting and predicting for future is a key factor for planning and development of any organization in this running world. As now, world has become a global village so our market has become much advance as compared to previous decades. Now the market demand is to planning for future based on existing and previous knowledge. In these conditions empirical evidences are of much importance. With the passage of time, world is changing to amazing extent. Changing in variables due to time is a prominent aspect now. The aim of this course is to impart basic and applied knowledge about Time Series and its applications in different fields with an emphasis on practical aspects of the interpretation of statistically based conclusions in research. It deals with the method of data collection, description measures of data interpretation of results, model selection, decision making and Forecasting. This course is designed for the advance modeling and forecasting of time series data. This focuses on advance time series techniques and forecasting methods.

Objectives

• To impart basic and applied knowledge about Time Series and its applications in different fields.
• To impart skills on the data collection, description measures of data interpretation of results, model selection , decision making and Forecasting.

Learning Outcomes

• To produce students, that has applicable knowledge about time series data analysis, which they apply in different fields of study. 
• The goal is to help the students to develop a skills on the modelling and forecasting of time series data, model selection and decision making.

Contents

Time Series Types of data, components of time series data, Stochastic processes, Stationary and non-stationary processes, Forms and tests of nonstationarity, Purely random processes, Random walk models, Lag operator, Difference equations and their solutions, Smoothing and decomposition methods, Univariate time series analysis (ARMA, ARIMA, Box-Jenkins approach, ARCH, GARCH etc.), Time series modeling and diagnostic checking, State space models and use of Kalman filter, Multivariate time series analysis: Granger causality, Vector Autoregressive Models. Transfer function and intervention analysis, Time series forecasting, Co-integration analysis, Vector error correction model and Johansen approach.

Readings

•         Chatfield, C. (1996). “The Analysis of Time Series: An Introduction”, Chapman and Hall, London.
•          Brock well P.J. and Davis R.A.(1991).”Time Series Theory and Methods”, Springer Verlag New York.
•         Anderson, T. (1976). The Statistical Analysis of Time-Series. John Wiley and Sons.
•         Box, G.E.P. and Jenkins G.M. (1994). Time-Series Analysis: Forecasting and Control. 3rd Ed., Prentice Hall, Englewood Cliffs, N.J. USA
•          Enders, W. (1995). Applied Econometric Time Series. New York: John Willy & Sons Inc.
•          Enders and Walter (1995). Applied Econometric Time Series. John Wiley and Sons, Inc. USA
•          Jonathan D. C. and Kung-Sik C. (2008). Time Series Analysis with Applications in R. Springer, USA.
•         Gujarati, N. D. and Sangeetha (2007). Basic Econometrics. TATA McGraw – Hill, Companies 4th – Edition

Distribution of Marks:
Mid Exam:           30
Final exam:         50

Sessional (Assignment,Presentation,Participation,Attendance,Quizes)    20
Scheduled on:      

Mphil:           Thrusday (10:00-01:00)