E Economic Data Processing

This module delves into essential transformations for economic and financial data, including calculating growth rates, real measures, and per capita metrics, as well as techniques for aggregating data from higher to lower frequencies, such as converting monthly prices to yearly prices. To ensure clarity in application and interpretation, the module provides insights into common data categorizations, differentiating between stock and flow variables, interval and ratio scale variables, or cross-sectional versus time-series data. Furthermore, it gives an overview of essential statistical measures such as mean, maximum, minimum, median, and variance.

E.1 Overview

This module consists of the following chapters:

  • Chapter 12: “Data Categorization” delves into different ways to classify and categorize economic data.
  • Chapter 13: “Data Transformation” explores methods for modifying and manipulating data, to make it more suitable for analysis or to reveal hidden patterns.
  • Chapter 15: “Data Aggregation” explains how to compile data from a lower frequency to a higher one, such as aggregating monthly data into quarterly or annual data.

Moreover, the following DataCamp course supplements the chapters mentioned above:

  • Introduction to Statistics in R: This statistics course sharpens your acumen by exploring data analysis techniques, mastering averages, utilizing scatterplots for relationships, understanding correlation, and grasping the foundational role of probability in statistical reasoning.

The module culminates in the creation of a data report:

  • Data Report on Economic Growth: This tests students’ ability to align various economic indicators to the same frequency and infer economic growth trends through statistical measures and data visualizations.

E.2 Learning Objectives

By the end of this module, students should be able to:

  1. Categorize and classify economic data efficiently.
  2. Transform data effectively, making it apt for economic analysis.
  3. Understand and calculate various statistical measures relevant to Finance and Economics.
  4. Aggregate data across different frequencies.
  5. Use data transformation techniques to extract economic insights.

E.3 Learning Activities & Assessments

Throughout this module, you’ll engage in the following activities:

  • Textbook Engagement: Read the textbook chapters 12 to 15 and reproduce the content provided. Input the code from the chapters into RStudio and verify that your results match the chapter outputs.

  • DataCamp Training: Work through the Introduction to Statistics in R course on DataCamp. While progressing through the course, keep an R script handy and apply the learned functions to any of the datasets introduced in this module. This preserves the new functions of the course and potentially offers new insights from the chosen dataset.

  • Data Report Creation: To consolidate your learning, craft a Data Report on Economic Growth using R Markdown. Your report should clearly display data, provide meaningful analyses, and embody the module’s content.

Your assessment will be based on:

  • DataCamp Course Completion: Finish the designated DataCamp courses for this module. Your grade is based on course completion; thus, you receive full credit by completing all chapters and obtaining a minimum of 75% XPs (experience points) by the deadline. Hence, while the Take Hint and Show Answer options reduce your XPs, they won’t affect your module grade if you stay above the 75% threshold.

  • Data Report Evaluation: The quality of the data report is central to module assessment. It should be professional, suitable for clients, and reflect the module’s content. Ensure you adhere to the data report instructions.

For further guidance on maximizing your textbook and DataCamp experience, consult the Textbook Engagement and Learn R with DataCamp sections. For specifications on the data report’s format and content, consult the Data Report Instructions and the subsequent section below.

E.4 Data Report on Economic Growth

For this assignment, consolidate data from various economic indicators to the same frequency, drawing correlations between them, and presenting insights about the factors that influence economic growth. Using both visual and statistical tools, the report should clearly depict trends and provide reasoned interpretations of observed patterns.

As preparation for this assignment, please work through the module chapters and DataCamp courses listed under the overview section above. Ensure your report adheres to the guidelines detailed in Data Report Instructions, which dictates the creation of two versions: official and internal. Upon completion, submit both versions on Blackboard.