**What we process, we learn. If we are not processing life, we are not living it. Live Life !**

There are broadly three aspects that one needs to spend time on,while learning about any statistical model. Intuition behind a model in describing a problem/phenomenon/scenario. Nitty gritty of the model : The assumptions of the model, its functional form, the math behind parameter estimation, the way to plot the data and perform diagnostics, do forecasting based on the model and do structural analysis with the model.

The book starts off by stating, Time series econometrics is concerned with the estimation of difference equations containing stochastic components. Hence the book naturally begins with a full-fledged chapter on difference equations. Difference Equations A few examples of difference equations are given such as Random walk model, Structural equation, Reduced form equation, Error correction model to show the reader that difference equations are everywhere in econometrics.Any time series model indeed is trying to explain a univariate variable or a multivariate vector in terms of lagged values, lagged differences, exogenous variables, seasonality variables etc.

Via Hyndsight :

Mathematics is not just solving for X. It’s also figuring out Y (Why). - Arthur Benjamin