Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

Abstract

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.

Forecasting UK consumer price inflation using inflation forecasts

“…Here, the authors found the DMA technique to be significantly better than benchmark regression models and selected time varying coefficient models. In [2], both the DMA technique and Dynamic Model Selection (DMS) were used to improve inflation forecasts for USA and the Euro area. In contrast, the Bayesian Model Averaging (BMA) technique is yet another popular form of forecast combination and was utilized in [5] for forecasting US inflation forecasting.…”

“…Nevertheless, a review of the relevant literature points out towards the existence of a strong heterogeneity in the predictive performances of the models used for inflation forecasting [2]. Thus, our interest delves into the search for a new and improved approach for improving the accuracy of inflation forecasts.…”

“…At the outset, it is noteworthy that forecast combination techniques extract multiple forecasts from various models and then combine same using some weights. For example, the DMA technique allows several models to hold at given points in time and then averages their respective forecasts [2]. In contrast, the proposed method takes an entirely different approach and presents an alternative form of forecast combination for time series analysis and forecasting.…”

“…Especially in countries like UK, where inflation targeting has prevailed since 1992, causing policy makers to anticipate inflation [1]. Regardless of its importance over a long time period, inflation forecasting remains a key challenge for Central Banks [2]. As…”

Adaptive learning from model space

“…First, the existence of many potential predictors can result in a huge number of potential models. Studies that use DMA to forecast a variety of different economic time series include: Buncic and Moretto (2015), Drachal (2016), and Naser (2016), forecasting commodities; Bruyn, Gupta, and Eyden (2015), Beckmann and Schüssler (2016), and Byrne, Korobilis, and Ribeiro (2018), forecasting exchange rates; Liu, Ma, and Wang (2015), forecasting stock returns; Gupta, Hammoudeh, Kim, and Simo-Kengne (2014), forecasting foreign exchange reserves; Bork and Moller (2015), Risse and Kern (2016), and Wei and Cao (2017), forecasting house price growth; Aye, Gupta, Hammoudeh, and Joong (2015) and Baur, Beckmann, and Czudaj (2016) forecasting gold prices; Koop and Korobilis (2011) and Filippo (2015), forecasting inflation; and Wang, Ma, Wei, and Wu (2016) and Liu, Wei, Y., Ma, F., and Wahab (2017), forecasting realized volatility. This leads to the need for model selection strategies.…”