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By M. Clements

Financial econometrics is without doubt one of the maximum on-going good fortune tales of contemporary a long time, because it has develop into essentially the most lively components of analysis in econometrics. during this e-book, Michael Clements offers a transparent and logical clarification of the main options and ideas of forecasts of financial and monetary variables. He indicates that forecasts of the only probably consequence of an monetary and monetary variable are of constrained price. Forecasts that offer additional info at the anticipated most likely levels of results are extra suitable. This booklet offers a finished remedy of the review of alternative different types of forecasts and attracts out the parallels among different methods. It describes the equipment of comparing those extra advanced forecasts which offer a fuller description of the diversity of attainable destiny outcomes.

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Example text

In the case of the AR(1), for example: h−1 yt+h = φh yt + φi εt+h−i i=0 so that: V(yt+h | Yt ) ≡ E[(yt+h − E(yt+h ))2 | yt ] ⎤ ⎡ 2 = E⎣ h−1 φi εt+h−i Yt ⎦ i=0 h−1 = φ2i E(ε2t+h−i | Yt ) i=0 = σε2 1 − φ2h . 1) The third line follows because independence of the {εt } implies they are serially uncorrelated, so that the covariance terms from the expected square of the sum in the second line are all zero. The ‘identically’ distributed part of the assumption on the disturbances gives E(ε2s ) = σε2 for all s.

1998) argue that forecast-error distributions are liable to be heavytailed if very large absolute errors are occasionally observed. They show analytically that in this case the standard t-test will be over-sized, and they analyse by Monte Carlo the usefulness of HCSEs and of the other tests of forecast encompassing described above. The second column of figures in the table records size estimates for heavy-tailed forecast errors. Following Harvey et al. d. N(0, 1) variables, as before, and χν,t is a chi-squared random variable with ν = 5 degrees of freedom.

That is, whether a combination of the two sets of forecasts may be more accurate than the best individual set of forecasts. We then consider a number of issues that arise when the forecasts are model-based. The discussion of testing for unbiasedness, equal predictive ability and forecast encompassing up to this point applies equally well to survey-based forecasts. Tests of predictive accuracy are described, which essentially compare an estimate of the forecast-error variance, based on the past fit of the model to the observed squared forecast errors.

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