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The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.

The fact that perfect foresight is possible in a random environment of an exchange economy seems to have been overlooked in the literature. This chapter demonstrates that forecasting rules which generate perfect foresight exist under standard assumptions. Using the methodology of Chapter 5, these socalled perfect forecasting rules can successfully be estimated from historical data by means of nonparametric estimations. The censoring of the approximated forecasting rules assures that the system is kept stable at all stages of the estimation procedure and that the resulting process satisfies the necessary stochastic properties. It should be noted that the method required neither the existence of a perfect forecasting rule nor its contraction property.