Capturing changing time trends Suppose that you have ten years of monthly sales data and are interested in forecasting future sales. Consider the simple model
$$\begin{equation*} y_t = \alpha_m + \beta a_t + y_{t-1} + \epsilon_t, \end{equation*}$$
where $y_t$ is log monthly sales, $\alpha_m$ is a monthly fixed effect and $a_t$ is the year at time $t$ (2011, say).
The time trend $\beta$ is not constant, however; it is getting smaller over time and heading toward 0. How can I model this?
I tried adding time-squared and log-time terms, but these send forecasts crashing out-of-sample (that is, the growth rates become negative, rather than limiting to 0).
I could create discrete blocks of time and have time period-year interactions, but


*

*This seems like an ad hoc approach and

*There aren't obvious breaks in the time trend, but rather a more-or-less linear decline that then slows and asymptotes at 0.


I could model the year-on-year growth rates instead of log sales. But the year-on-year difference is strongly correlated with a 12 period lag of itself (because log sales is not a unit root process). I would have to throw away two years of data to estimate this model with the necessary lags, which I am disinclined to do.
Are there any other suggestions that you could offer? Thanks!
 A: You could set it up as a state space model and use the Kalman filter to account for time-varying regression parameters.
A: You could model this with a dampened trend, where $$a_t=\sum_{i=0}^t \varphi^t$$ for some $\varphi\leq 1$. The limit case $\varphi=1$ corresponds to an undampened trend. In this formulation, you can simply feed $\varphi$ into your modeling algorithm and estimate it via maximum likelihood.
Dampened trends are very common in Exponential Smoothing methods. See here or here.
A: One can detect the number of trends and their length via Intervention Detection. This is done by simply expanding the kinds of Intervention Series to include Pulses , Level/step Shifts and Time Trends. In addition there could be needed ARIMA structure. One needs to control for and allow changes in ARIMA parameters over time and changes in the error variance over time. It is prudent to allow the data to speak to these forms rather than assume a structure/ Estimation does not overcome Model Specification Bias. You can find out more about Intervention Detection at http://www.unc.edu/~jbhill/tsay.pdf. We have been able to routinely implement the detection of the length and the timing of time trends , each with their own slope and duration length.
Time series analysis: Determine if trend is deterministic fluctuating/stable or stochastic is a quick read ...with @Aksakal contributing salient reflections.
