Research

WORKING PAPERS

We test for bias and efficiency of the ECB inflation forecasts using a confidential dataset of ECB macroeconomic quarterly projections. We investigate whether the properties of the forecasts depend on the level of inflation, by distinguishing whether the inflation observed by the ECB at the time of forecasting is above or below the target. The forecasts are unbiased and efficient on average, however there is evidence of state dependence. In particular, the ECB tends to overpredict (underpredict) inflation at intermediate forecast horizons when inflation is below (above) target. The magnitude of the bias is larger when inflation is above the target. These results hold even after accounting for errors in the external assumptions. We also find evidence of inefficiency, in the form of underreaction to news, but only when inflation is above the target. Our findings bear important implications for the ECB forecasting process and ultimately for its communication strategy.

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We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of the first quarter of 2020, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic.

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WORK IN PROGRESS

PUBLISHED PAPERS

We propose a model in which sticky expectations concerning short-term interest rates generate joint predictability patterns in bond and currency markets. Using our calibrated model, we quantify the effect of this channel and find that it largely explains why short rates and yield spreads predict bond and currency returns. The model also creates the downward sloping term structure of carry trade returns documented by Lustig et al. (2019), difficult to replicate in a rational expectations framework. Consistent with the model, we find that variables that predict bond and currency returns also predict survey-based expectational errors concerning interest and FX rates. The model explains why monetary policy induces drift patterns in bond and currency markets and predicts that long-term rates are a better gauge of market's short rate expectations than previously thought. 

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We study how phases of the business, credit and interest rate cycles affect the transmission of monetary policy using state-dependent local projection methods and data from 18 advanced economies. We find that the impact of monetary policy shocks on output and other macroeconomic and financial variables is weaker during periods of economic downturns, high household debt, and high interest rates. We then build a small-scale theoretical model to rationalize these facts. The model points to the presence of collateral and debt-service constraints on household borrowing and refinancing as potential drivers of state dependence of monetary policy with respect to the business, credit, and interest rate cycles. Our findings bear significant implications for the transmission of monetary policy and highlight potentially important features to be considered in models used to inform monetary policy decisions.

The relative performance of forecasting models is known to be unstable over time. However, it is not well understood why the forecasting performance of economic models change. We propose to address this question by evaluating the predictive ability of a wide range of economic variables for key U.S. macroeconomic aggregates: output growth and inflation. We take a conditional view on this issue, identifying situations where particular kind of models perform better than simple benchmarks. We, therefore, test whether the relative forecasting performance of models depend on the state of the business cycle, financial conditions, uncertainty or measures of past relative performance. We then investigate whether the conditioning variables help us predict the more accurate forecasting model for a specific future date. In particular, we analyze whether using the conditional performance as a model selection or model averaging criteria can improve the accuracy of the predictions. The proposed strategies deliver sizable improvements especially when the relative performance is predicted using financial variables.

There is a fast growing literature that set-identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). Most methods that have been used to construct error bands for impulse responses of sign-restricted SVARs are justified only from a Bayesian perspective. This paper demonstrates how to formulate the inference problem for sign-restricted SVARs within a moment-inequality framework. In particular, it develops methods of constructing error bands for impulse response functions of sign-restricted SVARs that are valid from a frequentist perspective. The paper also provides a comparison of frequentist and Bayesian error bands in the context of an empirical application - the former can be substantially wider than the latter.

Can monetary policy be used to promote financial stability? We answer this question by estimating the impact of a monetary policy shock on the private sector leverage and the likelihood of a financial crisis. Impulse responses obtained from a panel VAR of eighteen advanced countries suggest that the debt-to-GDP ratio rises in the short run following an unexpected tightening in monetary policy. As a consequence, the likelihood of a financial crisis increases, as estimated from a panel logit regression. However, in the long run output recovers and higher borrowing costs discourage new lending, leading to a deleveraging of the private sector. A lower debt-to-GDP ratio in turn reduces the likelihood of a financial crisis. These results suggest that monetary policy can achieve a less risky financial system in the long run but could fuel financial instability in the short run. We also find that the ultimate effects of a monetary policy tightening on the probability of a financial crisis depend on the leverage of the private sector: the higher the initial value of the debt-to-GDP ratio, the more beneficial the monetary policy intervention in the long run, but the more destabilizing in the short run.

We investigate whether expectations that are not fully rational have the potential to explain the evolution of house prices and the price-to-rent ratio in the United States. First, a stylized asset-pricing model solved under rational expectations is used to derive a fundamental value for house prices and the price-rent ratio. Although the model can explain the sample average of the price-rent ratio, it does not generate the volatility and persistence observed in the data. Then, we consider a rational bubble solution, an extrapolative expectations solution and a near rational bubble solution. In this last solution, agents extrapolate the future from the latest realization and the degree of extrapolation is stronger in good times than in bad times, generating waves of over-optimism. We show that under this solution the model not only is able to match key moments of the data but can also replicate the run up in the U.S. house prices observed over the 2000-2006 period and the subsequent sharp downturn.

We introduce quasi-likelihood ratio tests for one sided multivariate hypotheses to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. The limiting distributions of the test statistics are non-standard. For critical values we consider: (i) bootstrapping and (ii) simulations assuming normality of the mean square prediction error difference. The proposed tests have good size and power properties compared with existing equal and superior predictive ability tests for multiple model comparison. We apply our tests to study the predictive ability of a Phillips curve type for the US core inflation.