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.
Ms. Piyabha Kongsamut, Mr. Christian Mumssen, Anne-Charlotte Paret, and Mr. Thierry Tressel
How can information on financial conditions be used to better understand macroeconomic
developments and improve macroeconomic projections? We investigate this question for France
by constructing country-specific financial conditions indices (FCIs) that are tailored to movements
in GDP, investment, private consumption and exports respectively. We rely on a VAR approach to
estimate the weights of the financial components of each FCI, including equity market returns
(which turn out having a relatively strong weight across all FCIs), private sector risk premiums,
long-term interest rates, and banks’ credit standards. We find that the tailored FCIs are useful as
leading indicators of GDP, investment, and exports, and as a contemporaneous indicator of private
consumption. Credit volumes turn out to be lagging indicators of growth. The indices inform us on
macro-financial linkages in France and are used to improve the accuracy of quarterly forecasting
models and high-frequency “nowcast” models. We show that FCI-augmented models could have
significantly improved forecasts during and after the global financial crisis.
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.
I test whether inflation targeting (IT) enhances transparency using inflation forecast data for 11 IT adoption countries. IT adoption promotes convergence in forecast errors, suggesting that it enhances transparency. This effect is robust to dropping observations, is strengthened by using instrumental variable estimation to eliminate mean-reversion, and is absent in placebo regressions (where IT adoption is shifted by a year). This result supports Morris and Shin's (2002) contention that better public information is most beneficial for forecasters with bad private information. However, it does not support their hypothesis that better public information could make private forecasts less accurate.
The Monetary Authority of Singapore, instead of relying on short-term interest rates or monetary aggregates as its monetary policy instrument, conducts policy by managing the trade-weighted exchange rate index (TWI). This paper investigates how this operating procedure actually works. For empirical purposes, it assumes the authorities follow a reaction function that aims the TWI at stabilizing expected inflation and maintaining output at potential. A partial adjustment mechanism is included to dampen the actual changes in the exchange rate. The estimates confirm that the major focus of monetary policy in Singapore is controlling inflation. The estimated changes in the TWI track the actual change relatively well, and the estimated parameters are as expected. Accordingly, they support the hypothesis that monetary policy in Singapore can be described by a forward-looking policy rule that reacts to both inflation and output volatility. The results suggest that Singapore's monetary policy has mainly reacted to large deviations in the target variables, which is consistent with monetary policy's medium-term orientation.
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.