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Ordinary Least Squares (OLS) estimation of monetary policy rules produces potentially inconsistent estimates of policy parameters. The reason is that central banks react to variables, such as in ation and the output gap, which are endogenous to monetary policy shocks. Endogeneity implies a correlation between regressors and the error term, and hence, an asymptotic bias. In principle, Instrumental Variables (IV) estimation can solve this endogeneity problem. In practice, IV estimation poses challenges, as the validity of potential instruments depends on various unobserved features of the economic environment. We argue in favor of OLS estimation of monetary policy rules. To that end, we show analytically in the three-equation New Keynesian model that the asymptotic OLS bias is proportional to the fraction of the variance of regressors accounted for by monetary policy shocks. Using Monte Carlo simulation, we then show that this relationship also holds in a quantitative model of the U.S. economy. As monetary policy shocks explain only a small fraction of the variance of regressors typically included in monetary policy rules, the endogeneity bias is small. Using simulations, we show that, for realistic sample sizes, the OLS estimator of monetary policy parameters outperforms IV estimators.

In this paper we analyze how skewness and heavy tails a ect the estimated relationship between the real economy and the corporate bond-yield spread, a popular predictor of real activity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the disturbances. In-sample, we find that after controlling for stochastic volatility innovations in GDP growth can be well-described by a Gaussian distribution. In contrast, both the unemployment rate and the yield spread appear to benefit from being modelled using non-Gaussian innovations. When it comes to real-time forecasting performance, we find that the yield spread is an important predictor of GDP growth, and that accounting for stochastic volatility matters, mainly for density forecasts. Incremental improvements from non-Gaussian innovations are limited to forecasts of the unemployment rate. Our results suggest that stochastic volatility is of first order importance when modelling the relationship between yield spread and real variables; allowing for non-Gaussian innovations is less important.

This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into variables' marginal contribution are also provided to shed light on the main drivers of the ECB's reaction function at every point in time. In out-of-sample nowcasting experiments, the model provides an accurate tracking of the ECB monetary policy stance and decisions. The inclusion of textual variables contributes signifi- cantly to the gradual improvement of the model performance.

This study attempts to identify uncertainty in the long-term rate of interest based on the controversial interest rate theories of Keynes and Kalecki. While Keynes stated that the future of the rate of interest is uncertain because it is numerically incalculable, Kalecki was convinced that it could be predicted. The theories are empirically tested using a reduced-form GARCH-in-mean model assigned to six globally leading financial markets. The obtained results support Keynes's theory - the long-term rate of interest is a nonergodic financial phenomenon. Analyses of the relation between the interest rate and macroeconomic variables without interest uncertainty are thus seriously incomplete.

In order to understand why there is a continuous increase in euro banknote circulation even though the use of cash for transactions is decreasing in the euro area - a phenomenon known as the paradox of banknotes - the members of the Overseas workstream of the Eurosystem Research Network on Cash (EURECA) have conducted a study on the foreign demand for euro banknotes. The results of this study are based on desk research using data collected in the Eurosystem and from other organisations, and using both proven and innovative techniques.

This paper discusses the role of economic and monetary analysis in the monetary policy strategy of the European Central Bank (ECB). Both areas of analysis have evolved since the 2003 strategy review. Economic analysis has assigned an increasingly relevant role to the Eurosystem and ECB staff macroeconomic projections in forming a view on the medium-term outlook for economic activity and inflation. Furthermore, its focus has strengthened with regard to structural trends in shaping key economic relationships. Similarly, monetary analysis has shifted in focus: while the 2003 review emphasised the information value of monetary dynamics for detecting risks to price stability over medium-term to longer-term horizons, the focus of monetary analysis has increasingly been redirected to the assessment of monetary policy transmission. This evolution has opened a gap between the formal description of the strategy following the 2003 review and the practice of economic and monetary analysis in informing the ECB's policy deliberations. This paper concludes by presenting options for closing this gap and aligning the strategy formulation with the evolved role of economic and monetary analysis.

This paper provides an assessment of the macroeconomic models regularly used for forecasting and policy analysis in the Eurosystem. These include semi-structural, structural and time-series models covering specific jurisdictions and the euro area within a closed economy, small open economy, multi-country or global setting. Models are used as analytical frameworks for building baseline projections and for supporting the preparation of monetary policy decisions. The paper delivers four main contributions. First, it provides a survey of the macroeconomic modelling portfolios currently used or under development within the Eurosystem. Second, it explores the analytical gaps in the Eurosystem models and investigates the scope for further enhancement of the main projection and policy models, and the creation of new models. Third, it reviews current practices in model-based analysis for monetary policy preparation and forecasting and provides recommendations and suggestions for improvement. Finally, it reviews existing cooperation modalities on model development and proposes alternative sourcing and organisational strategies to remedy any knowledge or analytical gaps identified.

We evaluate and compare alternative monetary policy rules, namely average inflation targeting, price level targeting, and traditional inflation targeting rules, in a standard New Keynesian model that features recurring, transient zero lower bound regimes. We use determinacy and expectational stability (E-stability) of equilibrium as the criteria for stabilization policy. We find that price level targeting policy, including nominal income targeting as a special case, most effectively promotes determinacy and E-stability among the policy frameworks, whereas standard inflation targeting rules are prone to indeterminacy. Average inflation targeting can induce determinacy and E-stability effectively, provided the averaging window is sufficiently long.

This paper summarises the work done by Eurosystem staff in the context of the Strategy Review Seminar on Monetary Policy Instruments. More specifically, it focuses on the efficacy, efficiency and potential side effects of the key monetary policy instruments employed by the European Central Bank since 2014. The following main findings emerge from the analysis. First, instruments have been effective in easing financing conditions and supporting economic growth, employment and inflation. Second, considering the effective lower bound on policy rates, a combination of instruments is generally more efficient than relying on a single tool. Third, side effects have been generally contained so far, but they are found to vary over time and need to be closely monitored on an ongoing basis. Fourth, the monetary policy toolkit needs to remain innovative, diversified, and flexible, i.e. reviewed regularly to ensure that it remains fit for purpose against the backdrop of evolving financial and macroeconomic conditions.

In this study, we consider the construction of through-the-cycle (“TTC”) PD models designed for credit underwriting uses and point-in-time (“PIT”) PD models suitable for early warning uses, considering which validation elements should be emphasized in each case. We build PD models using a long history of large corporate firms sourced from Moody’s, with a large number of financial, equity market and macroeconomic variables as candidate explanatory variables. We construct a Merton model-style distance-to-default (“DTD”) measure and build hybrid structural reduced-form models to compare with the financial ratio and macroeconomic variable-only models. In the hybrid models, the financial and macroeconomic explanatory variables still enter significantly and improve the predictive accuracy of the TTC models, which generally lag behind the PIT models in that performance measure. We conclude that care must be taken to judiciously choose the manner in which we validate TTC vs. PIT models, as criteria may be rather different and be apart from standards such as discriminatory power. This study contributes to the literature by providing expert guidance to credit risk modeling, model validation and supervisory practitioners in controlling the model risk associated with such modeling efforts.

This study compares and contrasts the multiple characterizations of mean reversion in financial time series as regards the restrictions they imply. This is accomplished by translating them into statements about an alternative measure, the “Average Crossing Time” or ACT. We argue that the ACT measure, per se, provides not only a useful benchmark for the degree of mean reversion/aversion, but also an intuitive, and easily quantified sense of one time series being “more strongly mean‐reverting/averting” than another. We conclude our discussion by deriving the ACT measure for a wide class of stochastic processes and detailing its statistical characteristics. Our analysis is principally undertaken within a class of well‐understood production based asset pricing models.

I analyze time series momentum along the Treasury term structure. Past bond returns predict future returns both due to autocorrelation in bond risk premia and because unexpected bond return shocks increase the premium. Yield curve momentum is primarily due to autocorrelation in yield changes rather than autocorrelation in bond carry and can largely be captured using a single bond return or yield change factor. Because yield changes are partly induced by changes in the federal funds rate, yield curve momentum is related to post-FOMC announcement drift. The momentum factor is unspanned by the information in the term structure today and is hence inconsistent with standard term structure, macrofinance and behavioral models. I argue that the results are consistent with a model with unpriced longer term dependencies.

While corporate credit losses have been low since the start of the Covid-19 pandemic, their future evolution is quite uncertain. Using a forecasting model with a solid track record, we find that the baseline scenario ("expected losses") is benign up to 2024. This is due to policy support measures that have kept debt service costs low. However, high indebtedness, built up when the pandemic impaired real activity, suggests increased tail risks: plausible deviations from the baseline scenario ("unexpected losses") feature ballooning corporate insolvencies. Taken at face value, the low expected loss forecasts are consistent with low bank provisions, whereas the high unexpected loss forecasts call for substantial capital.

Debido a la incertidumbre al respecto, en este artículo se analiza cuál podría ser la postura de política monetaria más adecuada para México: una que mantenga el objetivo de inflación sin afectar negativamente el crecimiento del producto. Se estima el valor de la tasa de interés neutral en México para los próximos cinco años con base en la regla de Taylor y un modelo vectorial autorregresivo (VAR) cointegrado (CVAR). Los resultados sugieren que la tasa de política monetaria se encuentra por arriba de la tasa neutral de interés, lo que, combinado con el potencial recorte de tasas a escala internacional, podría conceder al banco central durante casi todo el sexenio grados de libertad para mantener su tasa por debajo del nivel actual; en el corto plazo se podría recortar la tasa de política monetaria actual en 25 puntos al menos sin arriesgar la convergencia a la meta de inflación.

This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016&ndash, 04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017&ndash, 24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.

The Current Expected Credit Loss (CECL) revised accounting standard for credit loss provisioning is the most important change to United States (US) accounting standards in recent history. In this study, we survey and assess practices in the validation of models that support CECL, across dimensions of both model development and model implementation. On the development side, this entails the usual SR 11-7 aspects of model validation, however, highlighted in the CECL context is the impact of several key modeling assumptions upon loan loss provisions. We also consider the validation of CECL model implementation or execution elements, which assumes heightened focus in CECL given the financial reporting implications. As an example of CECL model development validation, we investigate a modeling framework that we believe to be very close to that being contemplated by institutions, which projects loan losses using time-series econometric models, for an aggregated &ldquo, average&rdquo, bank using Federal Deposit Insurance Corporation (FDIC) Call Report data. In this example, we assess the accuracy of 14 alternative CECL modeling approaches, and we further quantify the level of model risk using the principle of relative entropy. Apart from the illustration of several model validation issues and practices that are of particular relevance to CECL, the empirical analysis has some potentially profound policy and model risk management implications. Specifically, implementation of the CECL standard may lead to under-prediction of credit losses, furthermore, coupled with the assumption that we are at an end to the favorable phase of the credit cycle, this may be interpreted as evidence that the goal of mitigating the procyclicality in the provisioning process that motivated CECL may fail to materialize.

This paper introduces the reader into the apparatus behind the popular New Keynesian Phillips (NKPC) curve. It derives several log-linear versions of this curve and recursive formulations of the Calvo-Yun price staggeringmodel that is behind this curve. These formulations can be used for higher-order approximations of the NKPC or for implementations that use other non-linear solution techniques, as, e.g., projection methods.

This paper compares different implementations of monetary policy in a new-Keynesian setting. We can show that a shift from Ramsey optimal policy under short-term commitment (based on a negative feedback mechanism) to a Taylor rule (based on a positive feedback mechanism) corresponds to a Hopf bifurcation with opposite policy advice and a change of the dynamic properties. This bifurcation occurs because of the ad hoc assumption that interest rate is a forward-looking variable when policy targets (inflation and output gap) are forward-looking variables in the new-Keynesian theory., Macroeconomic Dynamics, Cambridge University Press (CUP), 2020

Background The overexpression of (basic)helix-loop-helix ((b)HLH) transcription factors (TFs) is frequent in malignant glioma. We investigated molecular effects upon disruption of the (b)HLH network by a dominant-negative variant of the E47 protein (dnE47). Our goal was to identify novel molecular subgroup-specific therapeutic strategies. Methods Glioma cell lines LN229, LNZ308, and GS-2/GS-9 were lentivirally transduced. Functional characterization included immunocytochemistry, immunoblots, cytotoxic, and clonogenic survival assays in vitro, and latency until neurological symptoms in vivo. Results of cap analysis gene expression and RNA-sequencing were further validated by immunoblot, flow cytometry, and functional assays in vitro. Results The induction of dnE47-RFP led to cytoplasmic sequestration of (b)HLH TFs and antiglioma activity in vitro and in vivo. Downstream molecular events, ie, alterations in transcription start site usage and in the transcriptome revealed enrichment of cancer-relevant pathways, particularly of the DNA damage response (DDR) pathway. Pharmacologic validation of this result using ataxia telangiectasia and Rad3 related (ATR) inhibition led to a significantly enhanced early and late apoptotic effect compared with temozolomide alone. Conclusions Gliomas overexpressing (b)HLH TFs are sensitive toward inhibition of the ATR kinase. The combination of ATR inhibition plus temozolomide or radiation therapy in this molecular subgroup are warranted.

Referenzzinssätze spielen als Grundlage für zahlreiche Finanzprodukte eine wichtige Rolle in der Funktionsweise der weltweiten Finanzmärkte. Im Zuge des LIBOR-Skandals ist das Vertrauen in die Robustheit der Sätze erheblich gesunken. Infolgedessen haben Finanzregulierungsbehörden Prinzipien zur zukünftigen Gestaltung der Indizes entwickelt. Darauf basierend sind die bestehenden Benchmarks von Arbeitsgruppen der Zentralbanken in verschiedenen wichtigen Währungsräumen überarbeitet bzw. durch alternative Referenzzinssätze abgelöst worden. €STR, SONIA und SOFR ersetzen in der Eurozone, Großbritannien und den USA, die bis dato gültigen Tagesgeld-Benchmarks. Als Nachfolger für den ursprünglichen EURIBOR hat das European Money Markets Institute eine hybride Variante der Benchmark entwickelt. Der Arbeitsbericht diskutiert die Bestimmungsmethodik der neuen Referenzzinssätze. Um das implizite Kreditniveau der neuen Benchmarksätze zu verstehen, werden die zugrunde liegenden Berechnungen und Märkte ausführlich dargestellt. Der größte Unterschied im Euroraum liegt bei Übergang vom Overnight-Zins EONIA zu €STR, der als Einlagenzins jetzt 0,085% tiefer liegt.

Two approaches are considered to incorporate judgment in DSGE models. First, Bayesian estimation indirectly imposes judgment via priors on model parameters, which are then mapped into a judgmental interest rate decision. Standard priors are shown to be associated with highly unrealistic judgmental decisions. Second, judgmental interest rate decisions are directly provided by the decision maker, and incorporated into a formal statistical decision rule using frequentist procedures. When the observed interest rates are interpreted as judgmental decisions, they are found to be consistent with DSGE models for long stretches of time, but excessively tight in the 1980s and late 1990s and excessively loose in the late 1970s and early 2000s.

Negative interest rates were once seen as impossible outside the realm of economic theory. However, several central banks have recently adopted negative policy rates. The Federal Reserve is coming under increasing pressure to follow suit in the wake of the coronavirus crisis. This paper investigates the actual effects of negative interest rates using the Swedish experience from 2015 to 2019. The Swedish Riksbank was one of the first central banks to introduce a negative interest rate in 2015 and the first central bank to abandon a negative rate in 2019. We find that negative rates had a modest effect on consumer price inflation due to globalization, but significant effects on the exchange rate and domestic asset prices, thus fostering financial imbalances. We conclude by discussing the implications of our results for larger economies such as the United States.

In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets andWouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures effectively reduced the GDP deflator within three years (i.e. by -3.06 units until 1982Q3). While the empirical analysis largely conditions ona small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).

Four specifications of an affine model with risk aversion and no arbitrage conditions are estimated for the Mexican Term Structure of Interest Rates, contrasting their empirical properties and the accuracy of their in and out of sample forecasts. The traditional models are extended by adding macroeconomic variables to analyze if the latter provide sufficient information to improve the adjustment and the forecast of interest rates. Using monthly data of the Zero Coupon Bonds, VIX, WTI, exchange rate, inflation and growth in the period 2002-2017, it is found that, although there is no superiority of a single model for the in and/or out of sample forecast of the yield curve, adding macroeconomic variables helps to improve the short and medium term forecasts independently of the type of factors used.

In this paper, we analyse the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigate the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data ranging from January 1954 to September 2019, the properties of the models are assessed both within- and out-of-sample. We find strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.

This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that articial neural networks are able to predict the nominal interest rate better than linear and nonlinear Taylor rule models as well as univariate processes. While in-sample measures usually imply a forward-looking behaviour of the central bank, using nowcasts of the explanatory variables seems to be better suited for forecasting purposes. Overall, evidence suggests that U.S. monetary policy behaviour between 1987-2012 is nonlinear.

International audience; With non-controllable auto-regressive shocks, the welfare of Ramsey optimal policy is the solution of a single Riccati equation of a linear quadratic regulator. The existing theory by Hansen and Sargent (2007) refers to an additional Sylvester equation but miss another equation for computing the block matrix weighting the square of non-controllable variables in the welfare function. There is no need to simulate impulse response functions over a long period, to compute period loss functions and to sum their discounted value over this long period, as currently done so far. Welfare is computed for the case of the new-Keynesian Phillips curve with an auto-regressive cost-push shock. JEL classi…cation numbers: C61, C62, C73, E47, E52, E61, E63.

In response to the COVID-19 health crisis, the French government has imposed drastic lockdown measures for a period of 55 days. This paper provides a quantitative assessment of the economic and environmental impacts of these measures in the short and long term. We use a Computable General Equilibrium model designed to assess environmental and energy policies impacts at the macroeconomic and sectoral levels. We find that the lockdown has led to a significant decrease in economic output of 5% of GDP, but a positive environmental impact with a 6.6% reduction in CO2 emissions in 2020. Both decreases are temporary: economic and environmental indicators return to their baseline trajectory after a few years. CO2 emissions even end up significantly higher after the COVID-19 crisis when we account for persistently low oil prices. We then investigate whether implementing carbon pricing can still yield positive macroeconomic dividends in the post-COVID recovery. We find that implementing ambitious carbon pricing speeds up economic recovery while significantly reducing CO2 emissions. By maintaining high fossil fuel prices, carbon taxation reduces the imports of fossil energy and stimulates energy efficiency investments while the full redistribution of tax proceeds does not hamper the recovery. Electronic supplementary material The online version of this article (10.1007/s10640-020-00488-z) contains supplementary material, which is available to authorized users.

Forward guidance operates via the expectations formation process of the agents in the economy. In standard quantitative macroeconomic models, the expectations are unobserved state variables and little scrutiny is devoted to analysing the dynamic behaviour of these expectations. We show that the introduction of survey and financial market-based forecasts in the estimation of the model disciplines the expectations formation process in DSGE models. When the model-implied expectations are matched to observed expectations, the additional information of the forecasts restrains the agents' expectations formation. We argue that the reduced volatility of the agents' expectations dampens the model reactions to forward guidance shocks and improves the out-of-sample forecast accuracy of the model. Furthermore, we evaluate the case for introducing a discount factor as a reduced form proxy for a variety of microfounded approaches, proposed to mitigate the forward guidance puzzle. Once data on expectations is considered, the empirical support to introduce a discount factor dissipates.

We estimate a modified version of the "Financial Business Cycles" model originally developed by Iacoviello (2015) in order to investigate the role played by financial factors in driving the business cycle in the euro area. In the model, financial shocks such as borrower defaults, collateral shocks and credit supply effects amplify economic downturns by reducing the flow of credit from banks to the real sector. In this novel application to the euro area, we introduce capital reallocation inefficiency, an innovation to the original set-up which allows for more realistic effects of entrepreneur defaults on economic activity. Our results suggest that financial factors, as captured by this model, played a smaller role in the euro area throughout the double-dip recession than in the United States during the 2008-09 global financial crisis. In a scenario on second-round effects implied by potential NFC loan losses due to the COVID-19 pandemic, we find large financial amplification risks to real economic activity

In this paper the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of cryptocurrencies; however, in the short-run, the validity of Metcalfe’s law for Bitcoin is questionable. According to the bidirectional causality between the price and the network size, the expected price increase is a driver for more investors to join the Bitcoin network, which may lead in the end to a super-exponential price growth, possibly due to a herding behaviour of investors. The authors then used LPPL models to capture the behaviour of cryptocurrencies exchange rates during an endogenous bubble and to predict the most probable time of the regime switching. The main conclusion of this paper is that Metcalfe’s law may be valid in the long-run, however in the short-run, on various data regimes, its validity is highly debatable.

A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model.

European interest rates movements are affected by various internal and external factors. This paper studies the link between European and American short- and long-term interest rates. In particular, we consider the forward interest rates coming from euro and dollar IRS term structures. The econometric techniques employed are co-integration, Granger-causality, OLS and GMM. Our results indicate that European remote settlement forward and long-term interest rates are primarily driven by US rates and confirm that the causality acts mainly from the US to the Eurozone. This was true even during the recent periods of European Central Bank quantitative easing. These factors weaken the ECB’s ability to intervene. In fact, we found the impact of American monetary policy on long-term interest rates to be also relevant for European bonds.

Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.This study aims at assessing and deriving a set of unconditional forecasts for euro area inflation based on several specifications which take into account the information content of, inter alia, monetary and credit variables. The models are ordered and based on their in-sample performance and the “best” model is selected accordingly. The results indicate that the inclusion of money and credit variables in the information set improves the quality of the forecasts over a horizon of one to eight quarters. This supports the view that central banks should regularly monitor developments in money and credit.