52 research outputs found

    What are we learning about the long-run?

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    An attempt is made to link together earlier definitions of the long-run found in micro and macro economics with recent developments in econometrics; specifically cointegration. It is suggested that the links are not strong and that most of the previous work in econometric theory has been unnecessarily over-precise. Unit root processes can be replaced by processes that approximate them without loss of interpretation. The possibility of embedding cointegration theory into a very general non linear theory is suggested. An example uses a nonIinear relationship between UK short and long run interest rate proposed by Frank Paish

    What are we learning about the long-run?.

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    An attempt is made to link together earlier definitions of the long-run found in micro and macro economics with recent developments in econometrics; specifically cointegration. It is suggested that the links are not strong and that most of the previous work in econometric theory has been unnecessarily over-precise. Unit root processes can be replaced by processes that approximate them without loss of interpretation. The possibility of embedding cointegration theory into a very general non linear theory is suggested. An example uses a nonIinear relationship between UK short and long run interest rate proposed by Frank Paish.The long-run in microeconomics; The long-run in macroeconomics; Cointegration; Approximating unit roots; Cointegration in nonlinear models;

    Investigating the relationship between gold and silver prices

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    This paper analyze the long-run relationship between gold and silver prices. The three main questions addressed are: the influence of a large bubble from 1979:9 to 1980:3 on the cointegration relationship, the extent to which by including error correction terms in a nonlinear way we can beat the random walk model out-of sample and, the existence of a strong simultaneous relationship between the rates of return of gold and silver. Different efficient single equation estimation techniques are required for each of the three questions and this is explained within a simple bivariante cointegration system. With monthly data from 1971 to 1990, it is found that cointegration could have occurred during some periods and specially during the bubble and post-bubble periodo However, dummy variables for the intercept of the long-ron relationships are needed during the full sample. For the price of gold the nonlinear models perform better than the random walk in-sample and out-of-sample. In-sample nonlinear models for the price of silver perform better than the random walk but this predictive capacity is lost out-of sample, mainly due to the structural change that occurs (reduction) in the variance of the out-of sample models. The in-sample and out-of sample predictive capacity of the nonlinear models is reduced when the variables are in logs. Clear and strong evidence is found for a simultaneous relationship between the rates of return of gold and silver. In the three type of relationships that we have analyzed between the prices of gold and silver, the dependence is less out-of sample, possibly meaning that the two markets are becoming separated

    The impact of the use of forecasts in information sets

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    We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing the viewsexpressed by other forecasters the previous period is influencing individual current forecasts in the form of an attraction to conform to the mean forecast. There are two implications: one is that the forecasts polled in a multiperiod framework cannot be seen as independent from one another and hence the practice of using standard deviations from the forecasts' distribution as if they were standard errors of the estimated mean is not warranted. The second is that the forecasting performance of these groups may be severely affected by the detected imitation behavior and lead to convergence to a value which is not the right target (either the first available figure or some final values available at a later time). --multistep forecast,consensus forecast,preliminary data

    Investigating the Relationship between Gold and Silver Prices

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    This paper analyses the long-run relationship between gold and silver prices. The three main questions addressed are: the influence of a large bubble from 1979:9 to 1980:3 on the cointegration relationship, the extent to which by including error-correction terms in a non-linear way we can beat the random walk model out-of-sample, and the existence of a strong simultaneous relationship between the rates of return of gold and silver. Different efficient single-equation estimation techniques are required for each of the three questions and this is explained within a simple bivariate cointegrating system. With monthly data from 1971 to 1990, it is found that cointegration could have occurred during some periods and especially during the bubble and post-bubble periods. However, dummy variables for the intercept of the long-run relationships are needed during the full sample. For the price of gold the non-linear models perform better than the random walk in-sample and out-of-sample. In-sample non-linear models for the price of silver perform better than the random walk but this predictive capacity is lost out-of-sample, mainly due to the structural change that occurs (reduction) in the variance of the out-of-sample models. The in-sample and out-of-sample predictive capacity of the non-linear models is reduced when the variables are in logs. Clear and strong evidence is found for a simultaneous relationship between the rates of return of gold and silver. In the three type of relationships that we have analysed between the prices of gold and silver, the dependence is less out-of-sample, possibly meaning that the two markets are becoming separated.Publicad

    The correlogram of a long memory process plus a simple noise

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    A frequent property of data, particularly in the financial area, is that the correlogram is low but remains positive for many lags. A plausible explanation for this is that the process consists of a stationary, long memory component plus a white noise component of much larger variance. The implications of such a composition are explored including the consequences for estimation of the long memory parameter

    Discurso de investidura como Doctor Honoris Causa del Profesor Doctor Clive W. J. Granger

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    Nombrado Doctor Honoris Causa el día 25 de enero de 199

    Estimation of common long-memory components in cointegrated systems

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    The analysis of cointegration in large systems requires a reduction of their dimensionality. To achieve this, an analysis proposes to obtain the integrated of order one - I(1) - factors in every subsystem and then analyze cointegration among them. A new way of estimating common long-memory components of a cointegrated system is proposed. The identification of these I(1) common factors is achieved by imposing that they be linear combinations of the original variables and that the error-correction terms do not cause the common factors at low frequencies. Estimation is done from a fully specified error-correction model, which makes it possible to test hypotheses on the common factors using standard chi-squared tests. Several empirical examples are presented to illustrate the procedure.Publicad
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