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.
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.
Mr. Ales Bulir, Jaromír Hurník, and Katerina Smidkova
We offer a novel methodology for assessing the quality of inflation reports. In contrast to the existing literature, which mostly evaluates the formal quality of these reports, we evaluate their economic content by comparing inflation factors reported by the central banks with ex-post model-identified factors. Regarding the former, we use verbal analysis and coding of inflation reports to describe inflation factors communicated by central banks in real time. Regarding the latter, we use reduced-form, new Keynesian models and revised data to approximate the true inflation factors. Positive correlations indicate that the reported inflation factors were similar to the true, model-identified ones and hence mark high-quality inflation reports. Although central bank reports on average identify inflation factors correctly, the degree of forward-looking reporting varies across factors, time, and countries.
This paper attempts to explain short- and long-term dynamics of-and forecast-inflation in Tajikistan using the Vector Error Correction Model (VECM) and Autoregressive Moving Average Model (ARMA). By analyzing different transmission channels through the VECM, we were able to evaluate their relative dominance, magnitude, and speed of transition to the equilibrium price level, with the view of identifying those policy tools that will enhance the effectiveness of monetary policy. We found that excess supply of broad money is inflationary in both the short and long term. The dynamic analysis also demonstrates that the exchange rate and international inflation have a strong impact on local prices. Available monetary instruments, such as the refinancing rate, have proven to be ineffective. Therefore, the Tajik monetary authority could greatly benefit from enhancing its monetary instruments toolkit, including by developing the interest rate channel, to improve its monetary policy execution and to achieve stable inflationary conditions.
The transparency of monetary policy in South Africa has increased substantially since the end of the 1990s; but little empirical work has been done to examine the economic benefits of the increased transparency. This paper shows that, in recent years, South African private sector forecasters have become better able to forecast interest rates, are less surprised by reserve bank policy announcements, and are less diverse in the cross-sectional variety of their interest rate forecasts. In addition, there is some evidence that the accuracy of inflation forecasts has increased. The improvements in interest rate and inflation forecasts have exceeded those in real output forecasts, suggesting that increases in reserve bank transparency are likely to have played a role.
The paper evaluates the 24-month ahead inflation forecasting performance of various indicators of underlying inflation and structural models. The inflation forecast errors resulting from model misspecification are larger than the errors resulting from forecasting of exogenous variables. Also, measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation. Trimmed means, although weaker than GDFM indicators, have good forecasting performance, while indicators by permanent exclusion underperform but provide useful information about short-term dynamics. The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM. A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.
Adequate modeling of the seasonal structure of consumer prices is essential for inflation forecasting. This paper suggests a new econometric approach for jointly determining inflation forecasts and monetary policy stances, particularly where seasonal fluctuations of economic activity and prices are pronounced. In an application of the framework, the paper characterizes and investigates the stability of the seasonal pattern of consumer prices in the Kyrgyz Republic and estimates optimal money growth and implied exchange rate paths along with a jointly determined inflation forecast. The approach uses two broad specifications of an augmented error-correction model-with and without seasonal components. Findings from the paper confirm empirical superiority (in terms of information content and contributions to policymaking) of augmented error-correction models of inflation over single-equation, Box-Jenkins-type general autoregressive seasonal models. Simulations of the estimated errorcorrection models yield optimal monetary policy paths for achieving inflation targets and demonstrate the empirical significance of seasonality and monetary policy in inflation forecasting.
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.