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We propose two specifications of a real-time mixed-frequency semi-structural time series model for evaluating the output potential, output gap, Phillips curve, and Okun's law for the US. The baseline model uses minimal theory-based multivariate identification restrictions to inform trend-cycle decomposition, while the alternative model adds the CBO's output gap measure as an observed variable. The latter model results in a smoother output potential and lower cyclical correlation between inflation and real variables but performs worse in forecasting beyond the short term. This methodology allows for the assessment and real-time monitoring of official trend and gap estimates.

We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks - the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology.

We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks – the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology.

Que se passe-t-il avec l’inflation et la productivité dans la zone euro ? La Banque centrale européenne semble avoir perdu la capacité de relever l’inflation et les anticipations de prix ont glissé depuis la dernière récession. Une grande partie du débat politique a porté sur l’aplatissement de la courbe de Phillips. Pourtant, les estimations du processus conjoint d’inflation et de production indiquent que les éléments les plus pertinents du puzzle sont la diminution du potentiel de production et de l’inflation tendancielle.

L’enquête de la BCE auprès des prévisionnistes professionnels confirme le point de vue de la BCE selon lequel l’inflation dans la zone euro se redressera et sera de nouveau dans la fourchette cible de la banque centrale en 2019. Ce billet discute et conteste ce diagnostic. En utilisant un modèle qui formalise l’idée selon laquelle la dynamique de l’inflation est fonction de trois composantes – les anticipations à long terme, la courbe de Phillips et le mouvement du prix du pétrole – elle prévoit une inflation de la zone euro de seulement 1,1% en 2019, un taux proche du taux implicite selon les marchés obligataires.

A view often expressed by the Fed is that three components matter in inflation dynamics: a trend anchored by long run inflation expectations; a cycle connecting nominal and real variables; and oil prices. This paper proposes an econometric structural model of inflation formalising this view. Our findings point to a stable expectational trend, a sizeable and well identified Phillips curve and an oil cycle which, contrary to the standard rational expectation model, affects inflation via expectations without being reflected in the output gap. The latter often overpowers the Phillips curve. In fact, the joint dynamics of the Phillips curve cycle and the oil cycles explain the inflation puzzles of the last ten years.