Mr. Francis X. Diebold and Mr. Peter F. Christoffersen
Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.
For the past quarter century, Switzerland’s real per capita growth rate has been substantially slower than in most other industrial countries. Unemployment has declined rapidly to 2.4 percent of the labor force in November 1999 from its 1997 peak of more than 5 percent. Inflation has edged up from exceptionally low levels. Switzerland’s longstanding current account surplus was above 9 percent of gross domestic product in 1998 and is estimated to have risen further in 1999. The franc has shadowed the euro closely since January 1999, fluctuating narrowly around 1.6 Sw F/euro.
Consensus forecasts are inefficient, over-weighting older information already in the public domain at the expense of new private information, when individual forecasters have different information sets. Using a cross-country panel of growth forecasts and new methodological insights, this paper finds that: consensus forecasts are inefficient as predicted; this is not due to individual forecaster irrationality; forecasters appear unaware of this inefficiency; and a simple adjustment reduces forecast errors by 5 percent. Similar results are found using US nominal GDP forecasts. The paper also discusses the result’s implications for users of forecaster surveys and for the literature on information aggregation.
Jonas Dovern, Mr. Ulrich Fritsche, Mr. Prakash Loungani, and Ms. Natalia T. Tamirisa
We examine the behavior of forecasts for real GDP growth using a large panel of individual forecasts from 30 advanced and emerging economies during 1989–2010. Our main findings are as follows. First, our evidence does not support the validity of the sticky information model (Mankiw and Reis, 2002) for describing the dynamics of professional growth forecasts. Instead, the empirical evidence is more in line with implications of "noisy" information models (Woodford, 2002; Sims, 2003). Second, we find that information rigidities are more pronounced in emerging economies than advanced economies. Third, there is evidence of nonlinearities in forecast smoothing. It is less pronounced in the tails of the distribution of individual forecast revisions than in the central part of the distribution.
This paper develops an approach for forecasting in Thailand core inflation. The key innovation is to anchor the projections derived from the short-term time-series properties of core inflation to its longer-run evolution. This involves combining a short-term model, which attempts to distill the forecasting power of a variety of monthly indicators purely on goodness-of-fit criteria, with an equilibrium-correction model that pins down the convergence of core inflation to its longer-run structural determinants. The result is a promising model for forecasting Thai core inflation over horizons up to 10, 24, and 55 months, based on a root mean-squared error criterion as well as a mean absolute error criterion.
This paper develops a new forecasting framework for GDP growth in Korea to complement and further enhance existing forecasting approaches. First, a range of forecast models, including indicator- and pure time-series models, are evaluated for their forecasting performance. Based on the evaluation results, a new forecasting framework is developed for GDP projections. The framework also generates a data-driven reference band for the projections, and is therefore convenient to update. The framework is applied to the current World Economic Outlook (WEO) forecast period and the Great Recession to compare its performance to past projections. Results show that the performance of the new framework often improves the forecasts, especially at quarterly frequency, and the forecasting exercise will be better informed by cross-checking with the new data-driven framework projections.