Comments on: Is Regression/ Causal Modeling for Forecasting Underutilized?

By: prashant Telang

prashant Telang — Sat, 20 Nov 2010 17:27:42 +0000

ForecasterOnline is an experimental web application with Time series analysis

By: Mark Lawless

Mark Lawless — Wed, 04 Nov 2009 13:06:02 +0000

Since univariate modeling is most often supplemented by event adjustments for short-term forecasting and planning, regression modeling represents an excellent opportunity to get more reliable empirical estimates of events and factors used for these adjustments. This would include promotional lift, price elasticities, new product introduction effects, competitive response, market conditions, economic conditions, etc.

Of course, regression models are much more important in long-term forecasting and planning where the “certeris paribus” assumptions implicit in time series models do not hold. This would encompass annual bugeting, strategic planning, long-term business planning, and market development planning.

Many (perhaps most) companies would have benefited substantially in the last couple of years from regression modeling given the effects of the recession on their businesses and the dramatic departuture from patterns of behavior that were “baked into” the demand data in non-recessionary conditions. The relatvely higher operating costs and relatively higher working capital investments incurred due to the assumptions of unadjusted time series models have been difficult and costly for many companies.

Cause and effect modeling is certainly under-utilized in forecasting and planning, and can benefit both short-term and long-term forecasting and planning activities. It is more complex; and it requires more business knowledge, care and thought than time series modeling. But it can materially improve forecasts, plans, and business decisions when judiciously developed and applied.

Mark Lawless

By: Robin Parsons

Robin Parsons — Tue, 27 Oct 2009 17:40:24 +0000

I’m lucky in that I’m in a business that does have good access to causal data that is measured relatively consistently across companies.

My firm belief is that both causal and univariate models need to be compared and contrasted against each other when developing a statistical forecast. As per Nicholas Nassem Taleb – there is the risk when using causal analysis that you don’t really have data on all of the causal factors (was it weather, competitor availability, promotions, price, the economy, footfall, distribution, market share, etc. etc.).

The risk with univariate is you don’t always strip out underlying causes (e.g. distribution gains / losses etc.)

The next step in our one number forecasting process will be to incorporate both in a statistical model as well as having the option for account managers to over-write these calcs with their latest ROS/ Distribution and promotional plans.

By: John Dawson

John Dawson — Tue, 27 Oct 2009 11:03:15 +0000

I’ve been in many great organisations that rely on causal modelling to help them figure out strategies for the future. Missing data is rarely a good excuse and in my experience if you did, you’ll generally find what you (think) you need.

Causal forecasting is especially useful for forcasting consumer demand for products and services. You can automate the collection of data quite quickly these days and it’s always interesting to see the way people modify their decision process once they have better data.

My company is about to release a forecasting package to complement our modelling package and this will feature a simple comparison between time series and econometric methods so that the expert can compare the results. Many people have asked us for this feature and it will be interesting to see the results!

By: Kevin Gray

Kevin Gray — Tue, 20 Oct 2009 03:13:10 +0000

Further to Fred’s comment about data availability, when companies begin Data Mining the data are seldom all in one place and ready to go. They usually are scattered in various data bases in different parts of the company or exist externally. So there is an initial investment necessary but that’s business.

Kevin

By: Fred Andres

Fred Andres — Mon, 19 Oct 2009 21:30:24 +0000

In my 30+ years of experience in forecasting, I would have to agree that regression/causal models are way under utilized. My other observation is that such models are not just a little better, they are dramatically better. I have seen reductions of forecast error as high as 50% when moving to causal models. Later in my career, my standard approach was to start with a causal model and add time series components later. Unavailability of causal data is not an acceptable excuse for not using causal models. There is a plethora of causal data on demographics, weather, and the economy that are available for free or for a very reasonable price. Buy it. It’s worth it. Often internal causal variables like price are not available in a form that is amenable to forecasting, especially automatic forecasting. This is not an insurmountable problem. Demonstrate to your management how much better your forecast will be if you have this information in the correct form and then get together with your IT people to make it happen. If we sit back and wait until the world comes to us, it will never happen. We need to state what we want and fight to get it. In so doing, we not only improve our forecast, but become an integral part of managing the business.

By: admin

admin — Mon, 19 Oct 2009 07:51:05 +0000

In the more micro world of healthcare where one wants to predict who will be high cost patients, trend analysis doesn’t work because of regression to the mean. High cost this period tells you nothing about next period. So, things such as Markov models are more likely to be helpful as are data mining algorithms that can detect non-monotonic patterns. So, in addition to cases such as Ian mentions where time series (trends) may have worked at one time but no longer do, we have to add cases where trends never provided an adequate predictive analysis.
By Sam Kaplan

By: admin

admin — Mon, 19 Oct 2009 07:50:52 +0000

Our recent discussions with revenue managers who need sales forecasts to manage pricing and capacity release of airline tickets, hotel rooms and other ‘perishable’ goods suggest that:
– patterns of consumer buying behaviour have changed post-Lehman in response to the credit crunch/recession, increased use of the internet to seek out the best deals and the plethora of copntinuously changing deals and discounts on offer;
– forecasts based on historical patterns in time series have, not surprisingly, become less accurate and hence less useful to the business.

There are several possible responses to this, one being to switch to more sophisticated forecasting methods such as causal/regression modelling as is suggested; so I would expect interest in these techniques to increase. There are other possible responses, for example making the business less dependent on accurate sales forecasts; better understanding the drivers of consumer behaviour and how suppliers own actions may be exacerbating unncertainty in the market; increased use of auctions to probe the market. I’m sure there are many other possible responses to forecast uncertainty and wouild be interested to hear of any practical experinces.

By Ian Rowley

By: Piyush

Piyush — Mon, 19 Oct 2009 03:32:57 +0000

I am not sure if it would be right to say that casual models are ‘better’ than simple time series. Knowing the difficulty in getting the data and the inherent assumptions in regression, a casual model may be equally weak in forecasting.

The key is to use the right method for the right purpose. Casual models work best for aggregate planning and long term business mapping. Time series methods are great for day to day short term forecasting of individual product mix.

By: Kevin Gray

Kevin Gray — Sat, 17 Oct 2009 12:39:53 +0000

Thanks to all for your thoughtful comments. To follow up on Tom’s post, I’ve been in situations where, say, only 1-2 causal variables were readily available and the decision was made to abandon any kind of statistical forecasting.

Humans are prone to binary thinking and there is sometimes the notion that if statistical forecasts aren’t “perfect science” they are no better than gut feel.

Kevin