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The predictive accuracy of various econometrics models, including random walks, vector autoregressive and vector error-correction models, are investigated using daily futures prices of 4 commodities (the S&P500 index, treasury bonds, gold and crude oil).
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A class of nonlinear processes which have a root that is not constant, but is stochastic, and varying around unity is introduced. Th eprocess can be stationary for some periods, and mildly explosive for others.
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Based on Monte Carlo simulations using both stationary and nonstationary data, a model selection approach which uses the SIC to select a "best" group of forecasts in the context of forecast combination regressions dominates a number of other techniques, including the standard t-statistic...
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Nine macroeconomic variables are forecast in a real-time scenario using a variety of adaptive, nonadaptive, linear and nonlinear econometric models.
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A number of variations of seven causality tests of the absence of causal ordering are examined. The various of the tests considered account not only for stationarity, but also for integratedness and/or contegratedness among the variables in the model.
Persistent link: https://www.econbiz.de/10005631516
First reported monthly and quarterly time series data on nine macroeconomic variables from 1960-1993 are given. Features of this so called "unrevised" or "first reported data" are discussed, and the data is compared with standard "fully revised" data using Granger causality tests.
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