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.
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.
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.
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.
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.
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 simulates out-of-sample inflation forecasting for Germany, the UK, and the US. In contrast to other studies, we use output gaps estimated with unrevised real-time GDP data. This exercise assumes an information set similar to that available to a policymaker at a given point in time since GDP data is subject to sometimes substantial revisions. In addition to using real-time datasets for the UK and the US, we employ a dataset for real-time German GDP data not used before. We find that Phillips curves based on ex post output gaps generally improve the accuracy of inflation forecasts compared to an AR(1) forecast but that real-time output gaps often do not help forecasting inflation. This raises the question how operationally useful certain output gap estimates are for forecasting inflation.
Ms. Katerina Smídková, Viktor Kotlán, David Navrátil, and Mr. Ales Bulir
Inflation-targeting central banks have a respectable track record at explaining their policy actions and corresponding inflation outturns. Using a simple forward-looking policy rule and an assessment of inflation reports, we provide a new methodology for the empirical evaluation of consistency in central bank communication. We find that the three communication tools-inflation targets, inflation forecasts, and verbal assessments of inflation factors contained in quarterly inflation reports-provided a consistent message in five out of six observations in our 2000-05 sample of Chile, the Czech Republic, Hungary, Poland, Thailand, and Sweden.
Mr. Martin Cihak, Ms. Katerina Smídková, and Mr. Ales Bulir
The paper presents a methodology for measuring the clarity of central bank communication, illustrating it with the case of the European Central Bank (ECB) in 1999-2007. The analysis identifies the ECB's written communication as clear about 95 percent of instances, which is comparable to, or even better than, other central banks for which a similar analysis is available. We also find that the additional information contained in the ECB's Monthly Bulletins helps to improve communication clarity compared to ECB's press releases. In particular, the Bulletins contain useful clarifying information on individual inflation factors and the overall forecast risk; in contrast, the bulletin's communication on monetary shocks has a negative, albeit small, impact on clarity.
The Czech National Bank has a respectable track record in terms of its policy actions and the corresponding inflation outturns. Using a simple forward-looking policy rule, we find that its main communication tools-inflation targets, inflation forecasts, verbal assessments of the inflation risks contained in quarterly inflation reports, and the voting within the CNB Board-provided a clear message in about three out of every four observations in our 2001- 2005 sample.