Tests of equal predictive ability with real-time data

Tests of equal predictive ability with real-time data

Join Readfeed freeFind clubs reading this bookBrowse readable books
About
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work -- including West (1996), Clark and McCracken (2001, 2005),and McCracken (2006) -- our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with an examination of the real-time predictive content of various measures of economic activity for inflation.

Discuss Tests of equal predictive ability with real-time data with other readers

Join or start a book club for Tests of equal predictive ability with real-time data on Readfeed. Live chat, shared reading progress, and AI discussion questions — free to get started.

Sign up freeBrowse book clubs

Frequently asked questions

How do I join a book club for Tests of equal predictive ability with real-time data?

Sign up free on Readfeed, then browse public clubs or start your own club with Tests of equal predictive ability with real-time data as the current read. Invite friends with a share link and discuss together with live chat and AI discussion questions.

Can I discuss Tests of equal predictive ability with real-time data with other readers online?

Yes. Readfeed book clubs let you chat live, share progress, and join discussions about Tests of equal predictive ability with real-time data with readers worldwide — whether your club is virtual, in-person, or hybrid.

Is Readfeed free?

Yes. Creating an account and joining book clubs is free. Sign up to find readers who love the same books and start discussing today.