Forecasting economic growth in the euro area during the Great Moderation and the Great Recession

We evaluate forecasts for the euro area in data-rich and ‘data-lean’ environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data). We estimate backcasts, nowcasts and forecasts for GDP, components of GDP, and GDP of all individual euro area members, and examine forecasts for periods of low and high economic volatility (more specifically, we consider 2002-2007, which falls into the ‘Great Moderation’, and the ‘Great Recession’ 2008-2009). We find that all models consistently beat naive AR benchmarks, and overall, the dynamic factor model tends to outperform the PMI model (at times by a wide margin). However, accuracy of the dynamic factor model can be uneven (forecasts for some countries have large errors), with the PMI model dominating clearly for some countries or over some horizons. This is particularly pronounced over the Great Recession, where the dynamic factor model dominates the PMI model for backcasts, but has considerable difficulties beating the PMI model for nowcasts. This suggests that survey-based measures can have considerable advantages in responding to changes during very volatile periods, whereas factor models tend to be more sluggish to adjust. JEL Classification: C50, C53, E37, E47dynamic factor model, forecasting, PMI model

(Un)naturally low? Sequential Monte Carlo tracking of the US natural interest rate

Following the 2000 stockmarket crash, have US interest rates been held "too low" in relation to their natural level? Most likely, yes. Using a structural neo-Keynesian model, this paper attempts a real-time evaluation of the US monetary policy stance while ensuring consistency between the specification of price adjustments and the evolution of the econ- omy under flexible prices. To do this, the model's likelihood function is evaluated using a Sequential Monte Carlo algorithm providing inference about the time-varying distribution of structural parameters and unobservable, nonstationary state variables. Tracking down the evolution of underlying stochastic processes in real time is found crucial (i) to explain postwar Fed's policy and (ii) to replicate salient features of the data. JEL Classification: E43, C11, C15Bayesian Analysis, DSGE Models, Natural Interest Rate, Particle Filters

‘Lean’ versus ‘Rich’ Data Sets: Forecasting during the Great Moderation and the Great Recession

We evaluate forecasts for the euro area in data-rich and ‘data-lean’ environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data). We estimate backcasts, nowcasts and forecasts for GDP, components of GDP, and GDP of all individual euro area members, and examine forecasts for the ‘Great Moderation’ (2000-2007) and the ‘Great Recession’ (2008-2009) separately. All models consistently beat naïve AR benchmarks. More data does not necessarily improve forecasting accuracy: For the factor model, adding monthly indicators from national economies can lead to more uneven forecasting accuracy, notably when forecasting components of euro area GDP during the Great Recession. This suggests that the merits of national data may reside in better estimation of heterogeneity across GDP components, rather than in improving headline GDP forecasts for individual euro area countries. Comparing factor models to the much simpler PMI model, we find that the dynamic factor model dominates the latter during the Great Moderation. However, during the Great Recession, the PMI model has the advantage that survey-based measures respond faster to changes in the outlook, whereas factor models are more sluggish in adjusting. Consequently, the dynamic factor model has relatively more difficulties beating the PMI model, with relatively large errors in forecasting some countries or components of euro area GDP.Econometric and statistical methods; International topics

External shocks and international inflation linkages: a global VAR analysis

Amid the recent commodity price gyrations, policy makers have become increasingly concerned in assessing to what extent oil and food price shocks transmit to the inflationary outlook and the real economy. In this paper, we try to tackle this issue by means of a Global Vector Autoregressive (GVAR) model. We first examine the short-run inflationary effects of oil and food price shocks on a given set of countries. Secondly, we assess the importance of inflation linkages among countries, by dis-entangling the geographical sources of inflationary pressures for each region. Generalized impulse response functions reveal that the direct inflationary effects of oil price shocks affect mostly developed countries while less sizeable effects are observed for emerging economies. Food price increases also have significative inflationary direct effects, but especially for emerging economies. Moreover, significant second-round effects are observed in some countries. Generalized forecast error variance decompositions indicate that considerable linkages through which inflationary pressures spill over exist among regions. In addition, a considerable part of the observed headline inflation rises is attributable to foreign sources for the vast majority of the regions. JEL Classification: C32, E31commodity prices, Global VAR, inflation, oil shock, second-round effects

A new method to unveil embedded stellar clusters

In this paper we present a novel method to identify and characterize stellar clusters deeply embedded in a dark molecular cloud. The method is based on measuring stellar surface density in wide-field infrared images using star counting techniques. It takes advantage of the differing $H$-band luminosity functions (HLFs) of field stars and young stellar populations and is able to statistically associate each star in an image as a member of either the background stellar population or a young stellar population projected on or near the cloud. Moreover, the technique corrects for the effects of differential extinction toward each individual star. We have tested this method against simulations as well as observations. In particular, we have applied the method to 2MASS point sources observed in the Orion A and B complexes, and the results obtained compare very well with those obtained from deep Spitzer and Chandra observations where presence of infrared excess or X-ray emission directly determines membership status for every star. Additionally, our method also identifies unobscured clusters and a low resolution version of the Orion stellar surface density map shows clearly the relatively unobscured and diffuse OB 1a and 1b sub-groups and provides useful insights on their spatial distribution.Comment: A&A, in press; 13 pages, multi-layer figures can be displayed with Adobe Acrobat Reade

Molecular clouds have power-law probability distribution functions

In this Letter we investigate the shape of the probability distribution of column densities (PDF) in molecular clouds. Through the use of low-noise, extinction-calibrated \textit{Herschel}/\textit{Planck} emission data for eight molecular clouds, we demonstrate that, contrary to common belief, the PDFs of molecular clouds are not described well by log-normal functions, but are instead power laws with exponents close to two and with breaks between $A_K \simeq 0.1$ and $0.2\,\mathrm{mag}$, so close to the CO self-shielding limit and not far from the transition between molecular and atomic gas. Additionally, we argue that the intrinsic functional form of the PDF cannot be securely determined below $A_K \simeq 0.1\,\mathrm{mag}$, limiting our ability to investigate more complex models for the shape of the cloud PDF.Comment: Letter to the Editor, to appear in A&

Do financial investors destabilize the oil price?

In this paper, we assess whether and to what extent financial activity in the oil futures markets has contributed to destabilize oil prices in recent years. We define a destabilizing financial shock as a shift in oil prices that is not related to current and expected fundamentals, and thereby distorts efficient pricing in the oil market. Using a structural VAR model identified with sign restrictions, we disentangle this non-fundamental financial shock from fundamental shocks to oil supply and demand to determine their relative importance. We find that financial investors in the futures market can destabilize oil spot prices, although only in the short run. Moreover, financial activity appears to have exacerbated the volatility in the oil market over the past decade, particularly in 2007-2008. However, shocks to oil demand and supply remain the main drivers of oil price swings. JEL Classification: C32, Q41, Q31Oil Price, sign restrictions, Speculation, Structural VAR

On the Star Formation Rates in Molecular Clouds

In this paper we investigate the level of star formation activity within nearby molecular clouds. We employ a uniform set of infrared extinction maps to provide accurate assessments of cloud mass and structure and compare these with inventories of young stellar objects within the clouds. We present evidence indicating that both the yield and rate of star formation can vary considerably in local clouds, independent of their mass and size. We find that the surface density structure of such clouds appears to be important in controlling both these factors. In particular, we find that the star formation rate (SFR) in molecular clouds is linearly proportional to the cloud mass (M_{0.8}) above an extinction threshold of A_K approximately equal to 0.8 magnitudes, corresponding to a gas surface density threshold of approximaely 116 solar masses per square pc. We argue that this surface density threshold corresponds to a gas volume density threshold which we estimate to be n(H_2) approximately equal to 10^4\cc. Specifically we find SFR (solar masses per yr) = 4.6 +/- 2.6 x 10^{-8} M_{0.8} (solar masses) for the clouds in our sample. This relation between the rate of star formation and the amount of dense gas in molecular clouds appears to be in excellent agreement with previous observations of both galactic and extragalactic star forming activity. It is likely the underlying physical relationship or empirical law that most directly connects star formation activity with interstellar gas over many spatial scales within and between individual galaxies. These results suggest that the key to obtaining a predictive understanding of the star formation rates in molecular clouds and galaxies is to understand those physical factors which give rise to the dense components of these clouds.Comment: accepted for publicaton in the Astrophysical Journal; 22 pages, 4 figure

The role of financial variables in predicting economic activity

Previous research has shown that the US business cycle leads the European cycle by a few quarters, and can therefore help predicting euro area GDP. We investigate whether financial variables provide additional predictive power. We use a VAR model of the US and the euro area GDPs and extend it to take into account common global shocks and information provided by selected combinations of financial variables. In-sample analysis shows that shocks to financial variables influence real activity with a peak around 4 to 6 quarters after the shock. Out-of-sample Root-Mean- Squared Forecast Error (RMFE) shows that adding financial variables yields smaller errors in fore-casting US economic activity, especially at a five- quarter horizon, but the gain is overall tiny in economic terms. This link is even less prominent in the euro area, where financial indicators do not improve short and medium term GDP forecasts even when their timely availability, relative to a given GDP release, is exploited. The same conclusion is reached with a dataset of quarterly industrial production indices, although financial variables marginally improve fore- casts of monthly industrial production. We argue that the findings that financial variables have no predictive power for future activity in the euro area relate to the unconditional nature of the RMFE metric. When forecasting ability is assessed as if in real time (i.e. conditionally on the information available at the time when forecasts are made), we find that models using financial variables would have been preferred in many episodes, and in particular between 1999 and 2002. Results from the historical decomposition of a VAR model indeed suggest that in that period shocks were predominantly of financial nature. JEL Classification: F30, F42, F47conditional forecast, Financial Variables, international linkages, VAR

Global commodity cycles and linkages a FAVAR approach

In this paper we examine linkages across non-energy commodity price developments by means of a factor-augmented VAR model (FAVAR). From a set of non-energy commodity price series, we extract two factors, which we identify as common trends in metals and a food prices. These factors are included in a FAVAR model together with selected macroeconomic variables, which have been associated with developments in commodity prices. Impulse response functions confirm that exchange rates and of economic activity affect individual nonenergy commodity prices, but we fail to find strong spillovers from oil to non-oil commodity prices or an impact of the interest rate. In addition, we find that individual commodity prices are affected by common trends captured by the food and metals factors. JEL Classification: E3, F3commodity prices, Exchange Rates, FAVAR, Globalisation, Oil Price