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This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward non-linear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE) model, a Bayesian vector autoregression (BVAR) using optimized priors as well as Greenbook and SPF forecasts. Model estimation and forecasting is based on an expanding window scheme using quarterly U.S. real-time data (1964Q2:2020Q3) for 8 macroeconomic time series (GDP, inflation, federal funds rate, spread, consumption, investment, wage, hours worked), allowing for up to 8 quarter ahead forecasts. The results show that the BVAR improves forecasts compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or including them in a weighted average. Especially, ANN-based inflation forecasts improve other predictions by up to 50%. These results indicate that nonlinear data-driven ANNs are a useful method when it comes to macroeconomic forecasting.
Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. The author introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects.
Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.
In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries.
The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.