Forecasting Disclaimer

At the beginning of 2010, I'm publishing a collection of economic and environmental forecasts based on state-space time series models. Given that economists were unable to forecast the financial crisis of 2007-2010, I should probably explain why I think my approach is better.

First, I'm not sure my approach is better. That's part of the purpose for doing the forecasts in early 2010. At the start of 2011 (or any point in between), I can look back at the forecasts, see what went wrong (if anything) and try to figure out the reasons for any failures.

The state-space approaches are attractive because they address a number of known problems with existing forecasting models. State-space models forecast based on the "unobserved" state of the system being studied. The state variables are the minimum collection of independent, orthogonal variables that connect the model's input variables to the output variables. Typically, economic forecasting models have a rigid set of structural equations that are used to predict each output variable. Real human systems, however, are too flexible and too emergent to be modeled with a rigid set of equations.

A related attractive feature of state-space models is that there are two sources of error variation, one for the state-vectors and another for the output variables. Typically, economic forecasting models have one source of error for each output equation. And, typically the error distributions are modeled using a bell-shaped normal distribution. Nouriel Roubini, for one, has strongly criticized economic forecasting models both for their unrealistic structure, estimation techniques and assumptions about error terms. Rather than assuming that the functional form of the error term is known, state-space models can easily use a non-parametric bootstrapping technique to estimate confidence intervals around forecasts and the Kalman filter to compute the system state while forecasting (after an approximate system state is used to estimate the model).

Another interesting aspect of state-space models is the use of computer simulation to understand the forecasts. For example, one can estimate a state-space model for a based period (say 1950-1990 for the US economy) and then use simulation techniques to calculate the unobserved system state (using the Kalman filter) and the time path of the output variables to any point in the future. I'll present an example of the analysis in a later post.

For all these reasons, I find state-space models attractive for forecasting. For some of the answers to how well the models actually perform, we'll have to wait until 2011. However, we can also analyze two notorious historical episodes (the dot-com bubble and the financial crisis of 2007-2010) using a model estimated on US data up to 1990. Could these two crises have been predicted with state-space models? That will be the subject of my next post.