The widespread availability of internet search data is a new source of high-frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints. This paper investigates whether travel-related online search queries enhance accuracy in the forecasting of tourist arrivals to The Bahamas from the U.S. The results indicate that the forecast model incorporating internet search data provides additional information about tourist flows over a univariate approach using the traditional autoregressive integrated moving average (ARIMA) model and multivariate models with macroeconomic indicators. The Google Trends-augmented model improves predictability of tourist arrivals by about 30 percent compared to the benchmark ARIMA model and more than 20 percent compared to the model extended only with income and relative prices.
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.
This paper presents three empirical approaches to forecasting inflation in Pakistan. The preferred approach is a leading indicators model in which broad money growth and private sector credit growth help forecast inflation. A univariate approach also yields reasonable forecasts, but seems less suited to capturing turning points. A vector autoregressive (VAR) model illustrates how monetary developments can be described by a Phillips-curve type relationship. We deal with potential parameter instability on account of fundamental changes in Pakistan's economic system by restricting our sample to more recent observations. Gregorian and Islamic calendar seasonality are addressed by using 12-month moving averages.
Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Assessing the magnitude of the output gap is critical to achieving an optimal policy mix. Unfortunately, the gap is an unobservable variable, which, in practice, has been estimated in a variety of ways, depending on the preferences of the modeler. This model selection problem leads to a substantial degree of uncertainty regarding the magnitude of the output gap, which can reduce its usefulness as a policy tool. To overcome this problem, in this paper we attempt to insert some discipline into this search by providing two metrics-inflation forecasting and business cycle dating-against which different options can be evaluated using aggregated euro-area GDP data. Our results suggest that Gali, Gertler, and Lopez-Salido's (2001) inefficiency wedge performs best in inflation forecasting and production function methodology dominates in the prediction of turning points. If, however, a unique methodology must be selected, the quadratic trend delivers the best overall results.
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.