What are we learning about the long-run?

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?.

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

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

Economic and Statistical Measures of Forecast Accuracy

This paper argues in favour of a closer link between decision and forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used in meteorological forecasts, it is hardly mentioned in standard academic textbooks on economic forecasting. Some of the main issues involved are illustrated in the context of a two-state, two-action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and useful links between Kuipers score, used as a measure of forecast accuracy in the meteorology literature, and the market timing tests used in finance, are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be exploited in the presence of transaction costs are discussed.Decision theory, Forecast evaluation, Probabilistsic forecasts, Economic and statistical measures of forecast accuracy, Stock market predictability

Investigating the Relationship between Gold and Silver Prices

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

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

Nombrado Doctor Honoris Causa el día 25 de enero de 199

Tourism income and economic growth in Greece: Empirical evidence from their cyclical components

This paper examines the relationship between the cyclical components of Greek GDP and international tourism income for Greece for the period 1976–2004. Using spectral analysis the authors find that cyclical fluctuations of GDP have a length of about nine years and that international tourism income has a cycle of about seven years. The volatility of tourism income is more than eight times the volatility of the Greek GDP cycle. VAR analysis shows that the cyclical component of tourism income is significantly influencing the cyclical component of GDP in Greece. The findings support the tourism-led economic growth hypothesis and are of particular interest and importance to policy makers, financial analysts and investors dealing with the Greek tourism industry