By Roberto S. Mariano, Yiu-Kuen Tse
This crucial publication contains surveys of high-frequency monetary information research and econometric forecasting, written through pioneers in those parts together with Nobel laureate Lawrence Klein. the various chapters have been provided as tutorials to an viewers within the Econometric Forecasting and High-Frequency information research Workshop on the Institute for Mathematical technological know-how, nationwide college of Singapore in might 2006. they are going to be of curiosity to researchers operating in macroeconometrics in addition to monetary econometrics. additionally, readers will locate those chapters necessary as a consultant to the literature in addition to feedback for destiny study. Contents: Forecasting Uncertainty, Its illustration and review (K F Wallis) The collage of Pennsylvania versions for High-Frequency Macroeconomic Modeling (L R Klein & S Ozmucur) Forecasting Seasonal Time sequence (P H Franses) automobile and Affine approaches (C Gourieroux) Multivariate Time sequence research and Forecasting (M Deistler)
Read or Download Econometric Forecasting And High-Frequency Data Analysis (Lecture Notes Seres, Institute for Mathematical Sciences National University of Singapore) PDF
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Extra resources for Econometric Forecasting And High-Frequency Data Analysis (Lecture Notes Seres, Institute for Mathematical Sciences National University of Singapore)
34. With respect to the asymmetry of the forecast densities, it is seen that 13 of them exhibit positive skewness, with the mean exceeding the mode, whereas five are symmetric and four are negatively skewed. 075, is small. Evaluations of point forecasts typically focus on the conditional expectation, the mean of the forecast density, and the Inflation Report forecast analyses follow suit, despite the focus on the mode, the most likely outcome, in the MPC’s forecast commentary and press releases.
G. (1986). The importance of non-linearities in large forecasting models with stochastic error processes. Journal of Forecasting, 5, 205-215. Huizinga, F. (2001). Economic outlook 2003-2006. CPB Report, 2001/4, 16-22. M. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6, 255-259. 50 Kenneth F. Wallis John, S. (1982). The three-parameter two-piece normal family of distributions and its fitting. Communications in Statistics – Theory and Methods, 11, 879-885.
The advantages of this second transformation are that there are more tests available for normality, it is easier to test autocorrelation under normality than uniformity, and the normal likelihood can be used to construct likelihood ratio tests. We note that in cases where the density forecast is explicitly based on the normal distribution, centred on a point forecast yˆ t with standard deviation σ t , as in some examples discussed above, then the double transformation returns the standardised value of the outcome ( yt − yˆ t ) σ t , which could be calculated directly.