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.
I regress real GDP growth rates on the IMF’s growth forecasts and find that IMF forecasts behave similarly to those generated by overfitted models, placing too much weight on observable predictors and underestimating the forces of mean reversion. I identify several such variables that explain forecasts well but are not predictors of actual growth. I show that, at long horizons, IMF forecasts are little better than a forecasting rule that uses no information other than the historical global sample average growth rate (i.e., a constant). Given the large noise component in forecasts, particularly at longer horizons, the paper calls into question the usefulness of judgment-based medium and long-run forecasts for policy analysis, including for debt sustainability assessments, and points to statistical methods to improve forecast accuracy by taking into account the risk of overfitting.
Forecasters often predict continued rapid economic growth into the medium and long term for countries that have recently experienced strong growth. Using long-term forecasts of economic growth from the IMF/World Bank staff Debt Sustainability Analyses for a panel of countries, we show that the baseline forecasts are more optimistic than warranted by past international growth experience. Further, by comparing the IMF’s World Economic Outlook forecasts with actual growth outcomes, we show that optimism bias is greater the longer the forecast horizon.
This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models.
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.
We propose a new approach to test the full-information rational expectations hypothesis which can identify whether rejections of the arise from information rigidities. This approach quantifies the economic significance of departures from the and the underlying degree of information rigidity. Applying this approach to U.S. and international data of professional forecasters and other agents yields pervasive evidence consistent with the presence of information rigidities. These results therefore provide a set of stylized facts which can be used to calibrate imperfect information models. Finally, we document evidence of state-dependence in the expectations formation process.
This paper surveys dynamic stochastic general equilibrium models with financial frictions in use by central banks and discusses priorities for future development of such models for the purpose of monetary and financial stability analysis. It highlights the need to develop macrofinancial models which allow analysis of the macroeconomic effects of macroprudential policy tools and to evaluate elements of the Basel III reforms as a priority. The paper also reviews the main approaches to introducing financial frictions into general equilibrium models.
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.
This paper contributes to the debate on the role of money in monetary policy by analyzing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money contains relevant information for inflation in some model classes. Money-based New Keynesian DSGE models and VARs incorporating money perform better than their cashless counterparts. But there are also indications that the contribution of money has its limits. The marginal contribution of money to forecasting accuracy is often small, money adds little to dynamic factor models, and it worsens forecasting accuracy of partial equilibrium models. Finally, non-monetary models dominate monetary models in an all-out horserace.