Econometric Forecasting And High-Frequency Data Analysis by Roberto S. Mariano, Yiu-Kuen Tse PDF

By Roberto S. Mariano, Yiu-Kuen Tse

ISBN-10: 9812778950

ISBN-13: 9789812778956

ISBN-10: 9812778969

ISBN-13: 9789812778963

This significant booklet comprises surveys of high-frequency monetary facts research and econometric forecasting, written via pioneers in those components together with Nobel laureate Lawrence Klein. many of the chapters have been awarded as tutorials to an viewers within the Econometric Forecasting and High-Frequency facts research Workshop on the Institute for Mathematical technological know-how, nationwide collage of Singapore in might 2006. they are going to be of curiosity to researchers operating in macroeconometrics in addition to monetary econometrics. furthermore, readers will locate those chapters worthwhile as a advisor to the literature in addition to feedback for destiny examine. Contents: Forecasting Uncertainty, Its illustration and overview (K F Wallis) The collage of Pennsylvania types for High-Frequency Macroeconomic Modeling (L R Klein & S Ozmucur) Forecasting Seasonal Time sequence (P H Franses) automobile and Affine procedures (C Gourieroux) Multivariate Time sequence research and Forecasting (M Deistler)

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An assessment of Bank of England and National Institute inflation forecast uncertainties. 189, 64-71. F. (2005). Combining density and interval forecasts: a modest proposal. Oxford Bulletin of Economics and Statistics, 67, 983-994. Zarnowitz, V. (1969). The new ASA-NBER survey of forecasts by economic statisticians. American Statistician, 23(1), 12-16. Zarnowitz, V. A. (1987). Consensus and uncertainty in economic prediction. Journal of Political Economy, 95, 591-621. This page intentionally left blank THE UNIVERSITY OF PENNSYLVANIA MODELS FOR HIGH-FREQUENCY MACROECONOMIC MODELING Lawrence R.

Again replacing E by a sample average, but without transforming the data, a likelihood ratio test of equal forecast performance can be based on the sample average of log f 2 ( yt ) − log f1 ( yt ) . Amisano and Giacomini (2007) develop the same test by starting from the logarithmic score as a comparative measure of forecast performance. Using outcomes { y} rather than transformed data {z} (or {u}) is preferred, because the need to specify and/or estimate the density of z (or u) is avoided. This is not an issue in comparing goodness-of-fit statistics of competing forecasts based on transforms, such as statistics assessing departures from uniformity of {u}.

Evaluating density forecasts with applications to financial risk management. International Economic Review, 39, 863-883. S. F. (1999). Evaluating density forecasts of inflation: the Survey of Professional Forecasters. J. F. Engle and H. 76-90. Oxford: Oxford University Press. H. (2001). Forecasting in macroeconomics: a practitioner’s view. De Economist, 149, 155-175. H. and Shin, Y. (2003). Forecast uncertainties in macroeconometric modeling: an application to the UK economy. Journal of the American Statistical Association, 98, 829-838.

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Econometric Forecasting And High-Frequency Data Analysis (Lecture Notes Seres, Institute for Mathematical Sciences National University of Singapore) by Roberto S. Mariano, Yiu-Kuen Tse

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