Which type of model is best suited for forecasting financial and economic variables over time?

A time series model is best suited for forecasting financial and economic variables over time because it specifically analyzes data collected at different points in time. Unlike other models, time series models leverage historical data to identify trends, seasonal patterns, and cyclical behavior, allowing for more accurate predictions. These models are particularly useful when the primary interest is in understanding how a variable changes over time, which is a common requirement in financial and economic analyses.

In contrast, multi-factor models typically focus on relationships between multiple variables, providing insights into risk factors but may not exclusively cater to time-based forecasting. Regression models can also predict outcomes based on independent variables, but they often assume a fixed relationship between those variables rather than focusing on how those relationships evolve over time. Cross-sectional models analyze data at a single point in time across different entities, making them less effective for forecasting future values based on trends.

Therefore, the strengths of time series models in dealing with temporal data make them the most appropriate choice for forecasting financial and economic variables.