Time Series Analysis: Simple and Log-linear Trend Models

Simple Time Series Models

This is basic trend modeling.

A simple trend model can be expressed as follows:

\tilde{y}_t = b_0 + b_1 t + \varepsilon_t

$b_0$ = the y-intercept; where t = 0.
$b_1$ = the slope coefficient of the time trend.
t = the time period.
$\tilde{y}_t$ = the estimated value for time t based on the model.
$\varepsilon_t$ = the random error of the time trend.

The big validity pit-fall for simple trend models is serial correlation; if this problem is present, then you will see an artificially high R2 and your slope coefficient may falsely appear to be significant.

There is a visual way to detect serial correlation (not shown) or you can perform a Dubin-Watson test.

Log-linear Trend Models

This applies to non-linear time series trends.

The structure is:

\ln y_t = b_0 + b_1 t + e_t

y_t = e^{b_0 + b_1 t + e_t}

Again, like the simple trend model, use a graph or Durbin Watson test to check for serial correlation, as this will be a big threat to validity.

Unlock Premium Content

Upgrade your account to access the full article, downloads, and exercises.

You'll get access to:

Access complete tutorials and examples
Download source code and resources
Follow along with practical exercises
Get in-depth explanations

Time Series Analysis: Simple and Log-linear Trend Models

Unlock Premium Content

You'll get access to:

Qualitative and Dummy Variables in Regression Modeling

Auto-Regressive (AR) Time Series Models

Topics

Related Groups