We find that countries which are able to borrow at spreads that seem low given fundamentals (for example because investors take a bullish view on a country's future), are more likely to develop economic difficulties later on. We obtain this result through a two-stage procedure, where a first regression links sovereign spreads to fundamentals, after which residuals from this regression are deployed in a second stage to assess their impact on future outcomes (real GDP growth and the occurrence of fiscal crises). We confirm the relevance of past sovereign debt mispricing in several out-of-sample exercises, where they reduce the RMSE of real GDP growth forecasts by as much as 15 percent. This provides strong support for theories of sentiment affecting the business cycle. Our findings also suggest that countries shouldn't solely rely on spread levels when determining their fiscal strategy; underlying fundamentals should inform policy as well, since historical relationships between spreads and fundamentals often continue to apply in the medium-to-long run.
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.
Jin-Kyu Jung, Manasa Patnam, and Anna Ter-Martirosyan
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.
Most traditional forecasting models rely on fitting data to a pre-specified relationship between input
and output variables, thereby assuming a specific functional and stochastic process underlying that
process. We pursue a new approach to forecasting by employing a number of machine learning
algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true
relationship between input and output variables. We apply the Elastic Net, SuperLearner, and
Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and
emerging economies and find that these algorithms can outperform traditional statistical models,
thereby offering a relevant addition to the field of economic forecasting.
Inflation forecasts are modelled as monotonically diverging from an estimated long-run anchor point, or “implicit anchor”, towards actual inflation as the forecast horizon shortens. Fitting the model with forecasts by analysts, businesses and trade unions for South Africa, we find that inflation expectations have become increasingly strongly anchored. That is, the degree to which the estimated implicit anchor pins down inflation expectations at longer horizons has generally increased. Estimated inflation anchors of analysts lie within the 3–6 percent inflation target range of the central bank. However, the implicit anchors of businesses and trade unions, who are directly involved in the setting of wages and prices that drive the inflation process, have remained above the top end of the official target range. Possible explanations for these phenomena are discussed.
This study documents a semi-structural model developed for Sri Lanka. This model, extended with a fiscal sector block, is expected to serve as a core forecasting model in the process of the Central Bank of Sri Lanka’s move towards flexible inflation targeting. The model includes a forward-looking endogenous interest rate and foreign exchange rate policy rules allowing for flexible change in policy behavior. It is a gap model that allows for simultaneous identification of business cycle position and long-term equilibrium. The model was first calibrated and then its data-fit was improved using Bayesian estimation technique with relatively tight priors.
Mr. Sergi Lanau, Adrian Robles, and Mr. Frederik G Toscani
We study inflation dynamics in Colombia using a bottom-up Phillips curve approach. This
allows us to capture the different drivers of individual inflation components. We find that the
Phillips curve is relatively flat in Colombia but steeper than recent estimates for the U.S.
Supply side shocks play an important role for tradable and food prices, while indexation
dynamics are important for non-tradable goods. We show that besides allowing for a more
detailed understanding of inflation drivers, the bottom-up approach also improves on an
aggregate Phillips curve in terms of forecasting ability. In the baseline forecast scenario, both
headline and core inflation converge towards the Central Bank’s inflation target of 3 percent
by end-2018 but these favorable inflation dynamics are vulnerable to large supply shocks.
Macroeconomic forecasts are persistently too optimistic. This paper finds that common
factors related to general uncertainty about U.S. macrofinancial prospects and global demand
drive this overoptimism. These common factors matter most for advanced economies and G-
20 countries. The results suggest that an increase in uncertainty-driven overoptimism has
dampening effects on next-year real GDP growth rates. This implies that incorporating the
common structure governing forecast errors across countries can help improve subsequent
External headwinds, together with domestic vulnerabilities, have loomed over the prospects of
emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external
macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR
consisting of two blocks representing home and foreign factors, which is particularly useful for small
open economies. By exploiting the mixed-frequency nature of the model, we show how the toolbox
can be used for “nowcasting” the output growth. The conditional forecast results illustrate that regular
updates of external information, as well as domestic leading indicators, would significantly enhance
the accuracy of forecasts. Moreover, the analysis of variance decompositions shows that external
shocks are important drivers of the domestic business cycle.