Evaluating Econometric Forecasts of Economic and Financial by M. Clements

By M. Clements

Monetary econometrics is among the maximum on-going good fortune tales of contemporary a long time, because it has develop into the most lively parts of analysis in econometrics. during this e-book, Michael Clements provides a transparent and logical clarification of the most important strategies and ideas of forecasts of financial and monetary variables. He indicates that forecasts of the one probably final result of an monetary and monetary variable are of constrained worth. Forecasts that offer additional information at the anticipated most likely levels of results are extra proper. This ebook presents a complete remedy of the review of alternative varieties of forecasts and attracts out the parallels among the various techniques. It describes the tools of comparing those extra advanced forecasts which offer a fuller description of the variety of attainable destiny results.

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The dynamics of the model are therefore similar as yt−1 departs from c in either direction, but differ in a band around c. Such a transition function be useful in describing bands of inactivity around an equilibrium in the presence of adjustment costs: see, for example, Anderson (1997). The specification, estimation and evaluation of smooth transition models is described in Teräsvirta and Anderson (1992) and Teräsvirta (1994), inter alia. 4 Markov-switching models In MSAR processes the switch between regimes is determined by a stochastic process, whereby at each period t there is a constant probability of remaining in a given regime, say regime 1, which we denote by p11 , and therefore a probability of switching into the other regime (assuming a two-regime model) of p12 = 1 − p11 .

Assuming squared-error loss, time-varying conditional variances are shown not to affect the optimal point forecast. 3 shows that for more general loss functions the optimal point forecast may depend on the forecast variance. 4. Assessing the adequacy of conditional variance forecasts raises a number of difficulties because the ‘actual’ conditional variance is not observed. 5. 6. 2 Changing conditional-variances and optimal point forecasts Our discussion of point forecasting has been based on evaluating the optimal forecast, which equates to the conditional expectation under squared-error loss.

At turning points in the economy). This has led to the practice of reporting MSFEs for specific regimes, where the regimes are determined by the model designation at the time the forecast is made. g. Granger and Teräsvirta (1993, p. 164). Third, the forecast user may often be interested in how well the direction of change of a variable is forecast, and less interested in the magnitude of the forecast error per se. Tests of sign predictability were developed by Henriksson and Merton (1981), inter alia, and are sometimes known as market-timing tests.

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