Maybe I jumped to conclusions yesterday. Maybe, in fact, a retrospective analysis of the collapse in US housing prices in the recent 2008-2010 recession has been accomplished – but by major metropolitan area.
The Yarui Li and David Leatham paper Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model focuses on out-of-sample forecasts for house price indices for 42 metropolitan areas. Forecast models are built with data from 1980:01 to 2007:12. These models – dynamic factor and Large-scale Bayesian Vector Autoregressive (LBVAR) models – are used to generate forecasts of the one- to twelve- months ahead price growth 2008:01 to 2010:12.
Judging from the graphics and other information, the dynamic factor model (DFM) produces impressive results.
For example, here are out-of-sample forecasts of the monthly growth of housing prices (click to enlarge).
The house price indices for the 42 metropolitan areas are from the Office of Federal Housing Enterprise Oversight (OFEO). The data for macroeconomic indicators in the dynamic factor and VAR models are from the DRI/McGraw Hill Basic Economics Database provided by IHS Global Insight.
I have seen forecasting models using Internet search activity which purportedly capture turning points in housing price series, but this is something different.
The claim here is that calculating dynamic or generalized principal components of some 141 macroeconomic time series can lead to forecasting models which accurately capture fluctuations in cumulative growth rates of metropolitan house price indices over a forecasting horizon of up to 12 months.
That’s pretty startling, and I for one would like to see further output of such models by city.
But where is the publication of the final paper? The PDF file linked above was presented at the Agricultural & Applied Economics Association’s 2011 Annual Meeting in Pittsburgh, Pennsylvania, July, 2011. A search under both authors does not turn up a final publication in a refereed journal, but does indicate there is great interest in this research. The presentation paper thus is available from quite a number of different sources which obligingly archive it.
Currently, the lead author, Yarui Li, Is a Decision Tech Analyst at JPMorgan Chase, according to LinkedIn, having received her PhD from Texas A&M University in 2013. The second author is Professor at Texas A&M, most recently publishing on VAR models applied to business failure in the US.
Dynamic Principal Components
It may be that dynamic principal components are the new ingredient accounting for an uncanny capability to identify turning points in these dynamic factor model forecasts.
The key research is associated with Forni and others, who originally documented dynamic factor models in the Review of Economics and Statistics in 2000. Subsequently, there have been two further publications by Forni on this topic:
Do financial variables help forecasting inflation and real activity in the euro area?
The Generalized Dynamic Factor Model, One Sided Estimation and Forecasting
Forni and associates present this method of dynamic prinicipal componets as an alternative to the Stock and Watson factor models based on many predictors – an alternative with superior forecasting performance.
Run-of-the-mill standard principal components are, according to Li and Leatham, based on contemporaneous covariances only. So they fail to exploit the potentially crucial information contained in the leading-lagging relations between the elements of the panel.
By contrast, the Forni dynamic component approach is used in this housing price study to
obtain estimates of common and idiosyncratic variance-covariance matrices at all leads and lags as inverse Fourier transforms of the corresponding estimated spectral density matrices, and thus overcome(s)[ing] the limitation of static PCA.
There is no question but that any further discussion of this technique must go into high mathematical dudgeon, so I leave that to another time, when I have had an opportunity to make computations of my own.
However, I will say that my explorations with forecasting principal components last year have led to me to wonder whether, in fact, it may be possible to pull out some turning points from factor models based on large panels of macroeconomic data.