By M. Clements

ISBN-10: 0230596142

ISBN-13: 9780230596146

ISBN-10: 1403901724

ISBN-13: 9781403901729

ISBN-10: 1403901732

ISBN-13: 9781403901736

ISBN-10: 1403941572

ISBN-13: 9781403941572

Financial econometrics is among the maximum on-going luck tales of modern many years, because it has develop into the most energetic parts of study in econometrics. during this publication, Michael Clements offers a transparent and logical rationalization of the main strategies and ideas of forecasts of monetary and monetary variables. He indicates that forecasts of the one probably final result of an fiscal and fiscal variable are of restricted worth. Forecasts that offer additional information at the anticipated most likely levels of results are extra proper. This e-book offers a entire remedy of the assessment of other different types of forecasts and attracts out the parallels among the several ways. It describes the tools of comparing those extra advanced forecasts which supply a fuller description of the diversity of attainable destiny outcomes.

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**Additional info for Evaluating Econometric Forecasts of Economic and Financial Variables**

**Example text**

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. We also describe the impact of parameter estimation uncertainty on some of the tests and sketch the implications for testing for unbiasedness.

G. values around zero compared to values less than −2 or greater than +2, in the case of a normal random variable). 54). mc yt+2|t = 1 R R g(yt+1|t + zj ). j=1 Bootstrap Similar to Monte Carlo, except that the random variables {ˆεj } are drawings from the model’s estimated error terms, and may be preferred when F is unknown or cannot be easily sampled from. bs yt+2|t = 1 B B g(yt+1|t + εˆ j ). j=1 34 Evaluating Econometric Forecasts As the number of replications R is increased the Monte Carlo method (and the bootstrap) will provide an increasingly good approximation to the exact method, and is no more difﬁcult computationally to implement for longer horizon forecasts.

So that the simulated data have properties that match empirical processes. The Monte Carlo approach also allows us to explore other aspects of forecasting with non-linear models which have occupied recent investigators, such as the dependence on regime at which the forecast is made, and the impact on forecast performance of parameter estimation and model uncertainty. Here we assume that the SETAR model has the same speciﬁcation as the SETAR DGP and that the values of the parameters are known. 2) and compared to those of a linear AR model.

### Evaluating Econometric Forecasts of Economic and Financial Variables by M. Clements

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