Business and Economics > Forecasting

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Euihyun Bae, Andrew Hodge, and Anke Weber
This paper studies how and why inflation expectations have changed since the emergence of Covid-19. Using micro-level data from the University of Michigan Survey of Consumers, we show that the distribution of consumer expectations at one-year and five-ten year horizons has widened since the surge of inflation during 2021, along with the mean. Persistently high and heterogeneous expectations of consumers with less education and lower income are mainly responsible. A simple model of adaptive learning is able to mimic the change in inflation expectations over time for different demographic groups. The inflation expectations of low income and female consumers are consistent with using less complex forecasting models and are more backward-looking. A medium-scale DSGE model with adaptive learning, estimated during 1965-2022, has a time-varying solution that produces lower forecast errors for inflation than a variant with rational expectations. The estimated model interprets the surge of inflation in 2021 mainly as the result of a price markup shock, which is more persistent and requires a larger and more persistent monetary policy response than under rational expectations.
International Monetary Fund. Monetary and Capital Markets Department
This Technical Assistance (TA) report analyzes expanding the nowcasting toolbox at the National Bank of Rwanda (NBR). The mission built on the progress made during the March 2022 mission, which focused on improving the nowcasting framework for the key domestic variables and building tools for analyzing new data releases and assessing the nowcasting systems. The TA should continue to focus on the developments of the nowcasting framework for inflation and gross domestic product (GDP). Specifically, the new consumer price index (CPI) and GDP Near-Term Forecast (NTF) tools should be used on a monthly basis as part of the forecasting process and Forecasting and Policy Analysis System work at the NBR. The new CPI NTF system now includes the monthly forecasts of ten subgroups of the core CPI inflation as well as two subgroups of food inflation, thus enabling the assessment of the key drivers of inflation as well as the nature of inflation shocks. It also allows for the ‘real time’ monitoring of monthly inflation outcomes relative to the forecast.
Mr. Luis Brandão-Marques, Mr. Gaston Gelos, Mr. David J Hofman, Ms. Julia Otten, Gurnain Kaur Pasricha, and Zoe Strauss
We examine whether changes in the distribution of household inflation expectations contain information on future inflation. We first discuss recent shifts in micro data from the US, UK, Germany, and Canada. We then zoom in on the US to explore econometrically whether distributional characteristics help predict future inflation. We find that the shape of the distribution of household expectations does indeed help predict one-year-ahead CPI inflation. Variance and skewness of household expectations’ distributions add predictive power beyond and above the median, especially in periods of high inflation. Remarkably, qualitatively, these results hold when including market-based measures and moments of the distribution of professional forecasts.
Omer Faruk Akbal, Mr. Seung M Choi, Mr. Futoshi Narita, and Jiaxiong Yao
Quarterly GDP statistics facilitate timely economic assessment, but the availability of such data are limited for more than 60 developing economies, including about 20 countries in sub-Saharan Africa as well as more than two-thirds of fragile and conflict-affected states. To address this limited data availablity, this paper proposes a panel approach that utilizes a statistical relationship estimated from countries where data are available, to estimate quarterly GDP statistics for countries that do not publish such statistics by leveraging the indicators readily available for many countries. This framework demonstrates potential, especially when applied for similar country groups, and could provide valuable real-time insights into economic conditions supported by empirical evidence.
Mr. Sam Ouliaris and Ms. Celine Rochon
Nowcasting enables policymakers to obtain forecasts of key macroeconomic indicators using higher frequency data, resulting in more timely information to guide proposed policy changes. A significant shortcoming of nowcasting estimators is their “reduced-form” nature, which means they cannot be used to assess the impact of policy changes, for example, on the baseline nowcast of real GDP. This paper outlines two separate methodologies to address this problem. The first is a partial equilibrium approach that uses an existing baseline nowcasting regression and single-equation forecasting models for the high-frequency data in that regression. The second approach uses a non-parametric structural VAR estimator recently introduced in Ouliaris and Pagan (2022) that imposes minimal identifying restrictions on the data to estimate the impact of structural shocks. Each approach is illustrated using a country-specific example.