Final answer:
In statistical analysis, a model that includes a lagged value of the dependent variable is known as a dynamic model, which accounts for the effect of past values on current trends.
Step-by-step explanation:
In statistical analysis, when a model includes a lagged value of the dependent variable on the right-hand side, it is known as a dynamic model. This means that the current value of the dependent variable is explained not only by the current values of other explanatory variables but also by its own past values. Such models are valuable for examining the effect over time and are commonly used in fields like economics and finance where past information can influence current trends.
For example, when analyzing factors that determine your GPA, a dynamic model might take into account not just the current inputs like combined SAT scores, class attendance, and hours spent studying, but also your GPA from the previous semester. This lagged GPA can help to capture the carryover effects and momentum in academic performance.
This inclusion helps to quantify the inertia or momentum in the dependent variable, and it can improve the predictive power of the model by acknowledging that the effect of causes can spread over time.