The debate on the forecasting ability in economics of non-linear models has a long history, and the Great Recession provides us with an opportunity for a re-assessment of the forecasting performance of several classes of non-linear models, widely used in applied macroeconomic research. In this paper, we carry out an extensive analysis over a large quarterly database consisting of major real, nominal and financial variables for a large panel of OECD member countries. It turns out that, on average, non-linear models do not outperform standard linear specifications, even during the Great Recession period. In spite of this result, non-linear models enable to improve forecast accuracy in almost 40% of cases. Especially some countries and/or variables appear to be more adapted to nonlinear forecasting.

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Monitoring changes in financial conditions provides valuable information on the contribution of financial risks to future economic growth. For that purpose, central banks need real-time indicators to adjust promptly the stance of their policy. We extend the quarterly Growth-at-Risk (GaR) approach of Adrian et al. ( 2019) by accounting for the high-frequency nature of financial conditions indicators. Specifically, we use Bayesian mixed data sampling (MIDAS) quantile regressions to exploit the information content of both a financial stress index and a financial conditions index leading to real-time high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) provides an early signal of GDP downturns and (ii) allows day-to-day assessment of the effects of monetary policies. During the first six months of the Covid-19 pandemic period, it has provided a timely measure of tail risks on euro area GDP.

Monitoring changes in financial conditions provides valuable information on the contribution of financial risks to future economic growth. For that purpose, central banks need real-time indicators to adjust promptly the stance of their policy. We extend the quarterly Growth-at-Risk (GaR) approach of Adrian et al. ( 2019) by accounting for the high-frequency nature of financial conditions indicators. Specifically, we use Bayesian mixed data sampling (MIDAS) quantile regressions to exploit the information content of both a financial stress index and a financial conditions index leading to real-time high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) provides an early signal of GDP downturns and (ii) allows day-to-day assessment of the effects of monetary policies. During the first six months of the Covid-19 pandemic period, it has provided a timely measure of tail risks on euro area GDP.

We evaluate the policy implications of measuring the welfare cost of inflation accounting for instabilities in the long‐run money demand for the United States over the period 1900–2013. We extend the analysis and reassess the results reported in Lucas (2000) and Ireland (2009), also considering the recent theoretical contributions of Lucas and Nicolini (2015) and Berentsen, Huber, and Marchesiani (2015). Breaks in the long‐run money demand give rise to regime‐dependent welfare cost estimates. We find that the welfare cost is about 0.1% of annual income over 1976–2013, as compared to 0.8% over 1945–75. Overall, these values are substantially lower than those reported in the literature.