A simple criterion based on the properties of the forecast error is presented to evaluate the accuracy of forecasts. The efficiency conditions of an optimization problem are used to show that under rational expectations the standard statistical conditions are necessary, but not sufficient to ensure efficiency. This criterion is used to examine the accuracy of the World Economic Outlook projections of growth and inflation for the seven major industrial countries. Time series models are then estimated and the efficiency of the World Economic Outlook projections relative to a benchmark time series model is examined. A number of empirical tests suggest that the year ahead projections of growth and inflation in the World Economic Outlook are unbiased after 1982.
The paper estimates two time-varying parameter models of Chilean inflation: a Phillips curve model and a small open economy model. Their out-of-sample forecasts are compared with those of simple Box-Jenkins models. The main findings are; forecasts that include the pre-announced inflation target as a regressor are relatively better; the Phillips curve model outperforms the small open economy model in out-of-sample forecasts; and although Box-Jenkins models outperform the two models for short-term out-of-sample forecasts, their superiority deteriorates in longer forecasts. Adding a Markov-switching process to the models does not explain much of the conditional variance of the forecast errors.
h 2 and σ t 2 are positive numbers. All parameters are significant at the usual significance levels. The Q-statistictests for serial correlation as well as the Kolmogorov-Smirnov periodogram test of the standardized forecast errors and the squared of the standardized forecast errors, cannot reject the white noise null hypothesis.
Table 2. Chile: Parameter Estimates of the Unobserved Components Model of Real GDP
coefficients on the monthly seasonal dummies are not reported. Adj. R 2 is the adjusted coefficient of determination; Durbin’s h-statistic is the alternative for the D-W statistic in the presence of a lagged dependent variable; the Q-statistic indicates the Ljung-Box Q-statistictest for first order autocorrelation. * denotes significance of the coefficient at the 99 percent level of confidence; # denotes significance at the 95 percent level of confidence.
The large coefficient for the stock adjustment variable implies that economic agents do not immediately and fully
Using the ARFIMA-FIGARCH model, this paper studies the efficiency of the Japanese equity market by examining the statistical properties of the return and volatility of the Nikkei 225. It shows that both follow a long range dependence, which stands against the efficient market hypothesis (EMH). The result is valid for all sample periods, suggesting that the recent equity market reform has not produced major efficiency gains.
This paper re-examines the cyclical behavior of prices using postwar quarterly data for the G-7. We confirm recent evidence that the price level is countercyclical. However, we find strong evidence that the inflation rate is procyclical in our sample. Our results show the importance of making a clear distinction between inflation and the cyclical component of the price level when reporting and interpreting stylized facts regarding business cycles.
.89 from Fuller (1976) . The Q-statistictests whether the regression residuals are white noise. A significance level higher than 0.10 for the Q-statistic indicates that the hypothesis that the residuals are white noise cannot be rejected at the 10 percent level of signifcance.
The absolute t-statistics from the DF regressions are all large enough to suggest that the hypothesis that there is a unit root in the inflation rate for the G-7 can be rejected at the 5 percent level. The ADF regressions with one lag confirm this result except for France where the ADF