The Econometrics of Time Series Data: A Practical Guide using R

On December 3rd, 2024, the Department of Data Science, Christ (Deemed to be University), Pune Lavasa Campus, hosted a compelling session as part of the EconoPulse Guest Lecture Series. The event featured Dr. Prachi Bansal, an assistant professor at Jindal Global School and a Ph.D. graduate from Jawaharlal Nehru University, New Delhi. The lecture, titled “The Econometrics of Time Series Data: A Practical Guide Using R,” was designed for MSc. EA and BSc. EA students to deepen their understanding of econometric modeling and time series analysis.

The session provided an in-depth exploration of time series econometrics, emphasizing practical applications through the R programming language. Dr. Bansal began by introducing fundamental R syntax and functionality, ensuring participants were equipped for advanced topics. She explained key statistical concepts such as the Probability Mass Function (PMF) and Cumulative Distribution Function (CDF) with real-world examples, helping students understand their applications in modeling.

A significant portion of the session focused on simulation techniques. Students generated random variables, simulated binomial distributions, and analyzed key statistical properties such as expected value, variance, and standard deviation. These exercises bridged the gap between theoretical and empirical analysis, showcasing R's capabilities in large-scale data simulations.

The core of the lecture revolved around time series analysis, a cornerstone of econometrics. Participants explored essential concepts such as stationarity, autocorrelation, and data visualization, with guidance on handling challenges like trends, seasonality, and structural breaks. Using datasets such as AirPassengers, Dr. Bansal demonstrated how to transform non-stationary data into stationary series through techniques like differencing and logarithmic transformations. The session also emphasized the distinction between Ordinary Least Squares (OLS) regression and time series regression, highlighting their respective purposes in interpreting relationships and forecasting.

A key highlight was the discussion on ARIMA modeling for forecasting time series data. Dr. Bansal guided participants through building, diagnosing, and interpreting ARIMA models, stressing the importance of residual analysis and accuracy metrics for model validation. Students learned to simulate random walk models, demonstrating the implications of white noise and error distributions, and applied ARIMA models to manage and predict complex time series data.

The session was highly interactive, incorporating coding exercises in R that allowed participants to engage directly with real-world datasets. Students simulated random walks, visualized trends, and applied time series models, strengthening their analytical skills and understanding of statistical methods. The practical approach fostered confidence in handling time-dependent data for both academic research and professional applications.

The lecture successfully combined theory and practice, leaving students with a comprehensive understanding of time series modeling and econometric tools. Dr. Bansal’s dynamic teaching style and expertise made the session engaging and impactful, bridging the gap between theoretical concepts and practical implementation.

The Department of Data Science extends its gratitude to Dr. Prachi Bansal for her invaluable contribution to the EconoPulse Guest Lecture Series. The event marks a significant step forward in the university’s commitment to fostering analytical excellence and empowering students with skills to analyze and interpret time series data.