{"id":871,"date":"2015-08-10T23:10:48","date_gmt":"2015-08-10T23:10:48","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=871"},"modified":"2015-08-10T23:10:48","modified_gmt":"2015-08-10T23:10:48","slug":"true-models-trace-optimisation-and-parameter-shrinkage-2","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2015\/08\/10\/true-models-trace-optimisation-and-parameter-shrinkage-2\/","title":{"rendered":"True models, trace optimisation and parameter shrinkage"},"content":{"rendered":"

This is a talk that I gave at Monash University, where I am currently visiting. The topic of this research is exploring ways to avoid the assumption that the postulated model we are using is true for the data generating process of the time series we want to forecast. From this starting point we proceed to develop alternative cost functions based on the idea of trace forecasts<\/em>.<\/p>\n

Amongst the various interesting findings of this work, the one that I am most excited about is that we show that the new cost functions are shrinkage estimators of the univariate information, where the amount of shrinkage is controlled by the forecast horizon. In contrast to conventional shrinkage estimators, such as LASSO, we do not need to estimate a shrinkage parameter, as this is drawn from the forecasting problem directly. Furthermore, shrinkage for both AR and MA processes is feasible within this framework.<\/p>\n

You can download the talk here<\/a>.<\/p>\n

A working paper of this work will be released here shortly.
\nI would also like to thank the audience at Monash University for their many interesting comments.<\/p>\n

Related Posts<\/H3>