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Time Series and Forecasting Lecture 3 Forecast …

time Series and ForecastingLecture 3 Forecast Intervals, multi - step ForecastingBruce E. HansenSummer School in Economics and EconometricsUniversity of CreteJuly 23-27, 2012 Bruce Hansen (University of Wisconsin)ForecastingJuly 23-27, 20121 / 102 Today s ScheduleReviewForecast IntervalsForecast DistributionsMulti- step Direct ForecastsFan ChartsIterated ForecastsBruce Hansen (University of Wisconsin)ForecastingJuly 23-27, 20122 / 102 ReviewOptimal point Forecast ofyn+1given informationInis the conditionalmeanE(yn+1jIn)Estimate linear approximations by least-squaresCombine point forecasts to reduce MSFES elect estimators and combination weights by cross-validationEstimate GARCH models for conditional varianceBruce Hansen (University of Wisconsin)ForecastingJuly 23-27, 20123 / 102 interval ForecastsTake the form[a,b]Should containyn+1with probability 1 2 1 2 =Pn(yn+12[a,b])=Pn(yn+1 b) Pn(yn+1 a)=Fn(b) Fn(a)whereFn(y)is the Forecast distributionIt follows thata=qn( )b=qn(1 )

Time Series and Forecasting Lecture 3 Forecast Intervals, Multi-Step Forecasting Bruce E. Hansen Summer School in Economics and Econometrics University of Crete

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  Lecture, Series, Multi, Time, Step, Forecast, Forecasting, Interval, Time series and forecasting lecture 3 forecast, Time series and forecasting lecture 3 forecast intervals, Multi step forecasting

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