Modelling Expectations Formation With Parameter‐adaptive Filters: An Empirical Application to the Livingston Forecasts

A COMPARISON OF ALTERNATIVE REAL RATE ESTIMATES†

“…Pearce (1979), Brown and Maital (1981), Figlewski and Wachtel (1981), Lahiri (1981) and others, have also rejected the rationality hypothesis for the Livingston forecasts. Struth (1984) has demonstrated that the Livingston forecasts are considerably less accurate than forecasts generated with a simple Kalman filter using past price information only. The relative efficiency of inflation forecasts based on interest rate models was first demonstrated by Fama and Gibbons (1984).…”

Survey Evidence on the Muthian Rationality of the Inflation Forecasts of Us Consumers

“…This measure, while restrictive to some extent, is perhaps more appealing, since it is the most widely reported figure in the US mass media. and Bryan and Gavin, 1 986a) and by the forecast from a simple Kalman filter using past price information (see Struth, 1984). Putting together all these findings indicates that the SRC survey data contain more predictive information than the Livingston survey data, which perhaps makes it interesting to further investigate the predictive information content of the SRC survey forecast.…”

THE INFORMATION EFFICIENCY OF TRADERS' PRICE EXPECTATIONS IN A BANGLADESH RICE MARKET*

“…For example, if price forecasts are observed for one time unit ahead then one can estimate the following equation (letting P denote the expectation held at time t of P, the price to prevail at time t): (1) for each trader and test the joint hypothesis that (a0, a1) = (0, 1) and e is a zero mean white noise error. This procedure seems to have been first suggested by Theil (1966) and has since been used by Friedman (1980), Bailey et al (1984), de Leeuw andMcKelvey (1984) and Struth (1984), amongst others. To see why this is so, let denote a forecast of the price P, to prevail at time t conditional on the information set 't-n available at t -n. The corresponding forecasting bias t_nBt is defined as the mean forecasting error conditional on I _,2:…”