{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T17:43:45Z","timestamp":1698169425899},"reference-count":43,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2009,2,11]],"date-time":"2009-02-11T00:00:00Z","timestamp":1234310400000},"content-version":"unspecified","delay-in-days":6738,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[1990,9]]},"abstract":"<jats:p>Given observations on a stationary economic vector time series process we show that the best <jats:italic>h<\/jats:italic>-step ahead forecast (best in the sense of having minimal mean square forecast error) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens [7] with a modification to time series of the nonparametric kernel regression approach of Devroye and Wagner [16]. This approach is truly model-free, as no explicit specification of the distribution of the data generating process is needed.<\/jats:p>","DOI":"10.1017\/s0266466600005314","type":"journal-article","created":{"date-parts":[[2009,3,10]],"date-time":"2009-03-10T15:47:33Z","timestamp":1236700053000},"page":"348-383","source":"Crossref","is-referenced-by-count":4,"title":["Model-free Asymptotically Best Forecasting of Stationary Economic Time Series"],"prefix":"10.1017","volume":"6","author":[{"given":"Herman 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