Economic data comes in several primary forms, each with a unique role in economic analysis. The three main types are time series data, cross-sectional data, and panel data. Time series data consist of observations collected over regular time intervals for a single entity, such as a country's GDP measured each year. Cross-sectional data capture information for multiple entities at a single point in time, such as the GDP of several countries in one specific year. Panel data (or longitudinal data) combine both dimensions, tracking multiple entities across several time periods, such as the GDP of several countries over a decade. Understanding these data types is crucial, as they determine the analytical methods you use and the insights you can draw about economic trends, comparisons, and dynamics.

Python's lists and dictionaries offer flexible ways to store and manage economic data. A list is great for ordered collections, such as a country's GDP over consecutive years, where the position of each value matches a specific year. However, lists don't label each value, so you need to remember or track which year each position represents. Dictionaries, on the other hand, map keys (such as years or country names) to values, making it easier to retrieve or update data for a specific year or country without worrying about order. Nested dictionaries allow you to represent panel data, where you can store and access GDP for several countries across multiple years. For instance, with the gdp_data dictionary above, you can quickly look up the GDP for Germany in 2019 or update Japan's GDP for 2020, making dictionaries especially useful for real-world economic datasets that are often irregular or incomplete.