Ei ole eesti keeles kättesaadav
Marco J. Lombardi
- 20 March 2012
- WORKING PAPER SERIES - No. 1428Details
- Abstract
- While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers' Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models.
- JEL Code
- C50 : Mathematical and Quantitative Methods→Econometric Modeling→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
- 12 September 2011
- WORKING PAPER SERIES - No. 1379Details
- Abstract
- 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 Code
- C50 : Mathematical and Quantitative Methods→Econometric Modeling→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
- 1 June 2011
- WORKING PAPER SERIES - No. 1346Details
- Abstract
- 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 Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
Q41 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Demand and Supply, Prices
Q31 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Nonrenewable Resources and Conservation→Demand and Supply, Prices
- 12 January 2011
- WORKING PAPER SERIES - No. 1289Details
- Abstract
- Bayesian approaches to the estimation of DSGE models are becoming increasingly popular. Prior knowledge is normally formalized either be information concerning deep parameters
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
- 5 August 2010
- WORKING PAPER SERIES - No. 1232Details
- Abstract
- Global monetary conditions have often been cited as a driving factor of commodity prices. This paper investigates the empirical relationship between US monetary policy and commodity prices by means of a standard VAR system, commonly used in analysing the effects of monetary policy shocks. The results suggest that expansionary US monetary policy shocks drove up the broad commodity price index and all of its components. While these effects are significant, they however do not appear to be overwhelmingly large. This finding is also confirmed under different identification strategies for the monetary policy shock.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E40 : Macroeconomics and Monetary Economics→Money and Interest Rates→General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
- 16 June 2010
- OCCASIONAL PAPER SERIES - No. 113Details
- Abstract
- This report aims to analyse euro area energy markets and the impact of energy price changes on the macroeconomy from a monetary policy perspective. The core task of the report is to analyse the impact of energy price developments on output and consumer prices. Nevertheless, understanding the link between energy price fluctuations, inflationary pressures and the role of monetary policy in reacting to such pressure requires a deeper look at the structure of the economy. Energy prices have presented a challenge for the Eurosystem, as the volatility of the energy component of consumer prices has been high since the creation of EMU. At the same time, a look back into the past may not necessarily be very informative for gauging the likely impact of energy price changes on overall inflation in the future. For instance, the reaction of HICP inflation to energy price fluctuations seems to have been more muted during the past decade than in earlier periods such as the 1970s.
- JEL Code
- E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy - Network
- Eurosystem Monetary Transmission Network
- 14 April 2010
- WORKING PAPER SERIES - No. 1170Details
- Abstract
- 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 Code
- E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
F3 : International Economics→International Finance
- 24 November 2009
- WORKING PAPER SERIES - No. 1108Details
- Abstract
- 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 Code
- F30 : International Economics→International Finance→General
F42 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Policy Coordination and Transmission
F47 : International Economics→Macroeconomic Aspects of International Trade and Finance→Forecasting and Simulation: Models and Applications
- 17 June 2009
- WORKING PAPER SERIES - No. 1062Details
- Abstract
- 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 Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 16 August 2007
- WORKING PAPER SERIES - No. 794Details
- Abstract
- 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 Code
- E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C15 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Statistical Simulation Methods: General