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Kirstin Hubrich

1 August 2003
WORKING PAPER SERIES - No. 247
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Abstract
Monitoring and forecasting price developments in the euro area is essential in the light of the second pillar of the ECB's monetary policy strategy. This study analyses whether the forecasting accuracy of forecasting aggregate euro area inflation can be improved by aggregating forecasts of subindices of the Harmonized Index of Consumer Prices (HICP) as opposed to forecasting the aggregate HICP directly. The analysis includes univariate and multivariate linear time series models and distinguishes between different forecast horizons, HICP components and inflation measures. Various model selection procedures are employed to select models for the aggregate and the disaggregate components. The results indicate that aggregating forecasts by component does not necessarily help forecast year-on-year inflation twelve months ahead.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
22 February 2006
WORKING PAPER SERIES - No. 589
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Abstract
We suggest an alternative use of disaggregate information to forecast the aggregate variable of interest, that is to include disaggregate information or disaggregate variables in the aggregate model as opposed to first forecasting the disaggregate variables separately and then aggregating those forecasts or, alternatively, using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others.
JEL Code
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
13 October 2006
WORKING PAPER SERIES - No. 681
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Abstract
We investigate co-movements and heterogeneity in inflation dynamics of different regions within and across euro area countries using a novel disaggregate dataset to improve the understanding of inflation differentials in the European Monetary Union. We employ a model where regional inflation dynamics are explained by common euro area and country specific factors as well as an idiosyncratic regional component. Our findings indicate a substantial common area wide component, that can be related to the common monetary policy in the euro area and to external developments, in particular exchange rate movements and changes in oil prices. The effects of the area wide factors differ across regions, however. We relate these differences to structural economic characteristics of the various regions. We also find a substantial national component. Our findings do not differ substantially before and after the formal introduction of the euro in 1999, suggesting that convergence has largely taken place before the mid 90s. Analysing US regional inflation developments yields similar results regarding the relevance of common US factors. Finally, we find that disaggregate regional inflation information, as summarised by the area wide factors, is important in explaining aggregate euro area and US inflation rates, even after conditioning on macroeconomic variables. Therefore, monitoring regional inflation rates within euro area countries can enhance the monetary policy maker's understanding of aggregate area wide inflation dynamics.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
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
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
19 March 2009
WORKING PAPER SERIES - No. 1030
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Abstract
We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the bench-mark to all the alternative models simultaneously rather than sequentially, and do not require re-estimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different models for U.S. inflation.
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
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
25 February 2010
WORKING PAPER SERIES - No. 1155
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Abstract
To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecast origin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data.
JEL Code
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
16 March 2010
OCCASIONAL PAPER SERIES - No. 108
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Abstract
The Eurosystem macroeconomic projection exercises are part of the input prepared for the Governing Council
JEL Code
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
D92 : Microeconomics→Intertemporal Choice→Intertemporal Firm Choice, Investment, Capacity, and Financing
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G31 : Financial Economics→Corporate Finance and Governance→Capital Budgeting, Fixed Investment and Inventory Studies, Capacity
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
Network
Eurosystem Monetary Transmission Network
2 May 2011
WORKING PAPER SERIES - No. 1334
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Abstract
We use a novel disaggregate sectoral euro area data set with a regional breakdown to investigate price changes and suggest a new method to extract factors from over-lapping data blocks. This allows us to separately estimate aggregate, sectoral, country-specific and regional components of price changes. We thereby provide an improved estimate of the sectoral factor in comparison with previous literature, which decomposes price changes into an aggregate and idiosyncratic component only, and interprets the latter as sectoral. We find that the sectoral component explains much less of the variation in sectoral regional inflation rates and exhibits much less volatility than previous findings for the US indicate. We further contribute to the literature on price setting by providing evidence that country- and region-specific factors play an important role in addition to the sector-specific factors. We conclude that sectoral price changes have a “geographical” dimension, that leads to new insights regarding the properties of sectoral price changes.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
D4 : Microeconomics→Market Structure and Pricing
F4 : International Economics→Macroeconomic Aspects of International Trade and Finance
20 July 2011
WORKING PAPER SERIES - No. 1363
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Abstract
We propose a new method for medium-term forecasting using exogenous information. We first show how a shifting-mean autoregressive model can be used to describe characteristic features in inflation series. This implies that we decompose the inflation process into a slowly moving nonstationary component and dynamic short-run fluctuations around it. An important feature of our model is that it provides a way of combining the information in the sample and exogenous information about the quantity to be forecast. This makes it possible to form a single model-based inflation forecast that also incorporates the exogenous information. We demonstrate, both theoretically and by simulations, how this is done by using the penalised likelihood for estimating the model parameters. In forecasting inflation, the central bank inflation target, if it exists, is a natural example of such exogenous information. We illustrate the application of our method by an out-of-sample forecasting experiment for euro area and UK inflation. We find that for euro area inflation taking the exogenous information into account improves the forecasting accuracy compared to that of a number of relevant benchmark models but this is not so for the UK. Explanations to these outcomes are discussed.
JEL Code
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
25 February 2013
OCCASIONAL PAPER SERIES - No. 143
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Abstract
This paper analyses the transmission of financial shocks to the macro-economy. The role of macro-financial linkages is investigated from an empirical perspective for the euro area as a whole, for individual euro area member countries and for other EU and OECD countries. The following key economic questions are addressed: 1) Which financial shocks have the largest impact on output over the full sample on average? 2) Are financial developments leading real activity? 3) Is there heterogeneity or a common pattern in macro-financial linkages across the euro area and do these linkages vary over time? 4) Do cross-country spillovers matter? 5) Is the transmission of financial shocks different during episodes of high stress than it is in normal times, i.e. is there evidence of non-linearities? In summary, it is found that real asset prices are significant leading indicators of real activity whereas the latter leads loan developments. Furthermore, evidence is presented that macro-financial linkages are heterogeneous across countries
JEL Code
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
D11 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Theory
26 August 2013
WORKING PAPER SERIES - No. 1580
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Abstract
In this paper 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. We show that the limiting distributions of the test statistics are non standard. For critical values we consider two approaches: (i) bootstrapping and (ii) simulations assuming normality of the mean square prediction error (MSPE) difference. The size and the power performance of the tests are compared via Monte Carlo experiments with existing equal and superior predictive ability tests for multiple model comparison. We find that our proposed tests are well sized for one step ahead as well as for multi-step ahead forecasts when critical values are bootstrapped. The experiments on the power reveal that the superior predictive ability test performs last while the ranking between the quasi likelihood-ratio test and the other equal predictive ability tests depends on the simulation settings. Last, we apply our test to draw conclusions about the predictive ability of a Phillips type curve for the US core inflation.
JEL Code
B23 : History of Economic Thought, Methodology, and Heterodox Approaches→History of Economic Thought since 1925→Econometrics, Quantitative and Mathematical Studies
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
1 September 2014
WORKING PAPER SERIES - No. 1728
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Abstract
A financial stress index for the United States is introduced
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: 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
25 October 2016
WORKING PAPER SERIES - No. 1972
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Abstract
The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short-term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance-based forecast combination methods, in particular one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time-varying weights assign weights to the economic interpretations of the forecast stemming from different models. We also include a number of benchmark models in our analysis. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre-crisis times, the period after the global financial crisis and the full evaluation period including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that forecast combination helps hedge against bad forecast performance and that performance-based weighting outperforms simple averaging.
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
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
22 December 2017
WORKING PAPER SERIES - No. 2119
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Abstract
We investigate whether the response of the macro-economy to oil price shocks undergoes episodic changes. Employing a regime-switching vector autoregressive model we identify two regimes that are characterized by qualitatively different patterns in economic activity and inflation following oil price shocks in the euro area. In the normal regime, oil price shocks trigger only limited and short-lived adjustments in these variables. In the adverse regime, by contrast, oil price shocks are followed by sizeable and sustained macroeconomic fluctuations, with inflation and economic activity moving in the same direction as the oil price. The responses of inflation expectations and wage growth point to second-round effects as a potential driver of the dynamics characterising the adverse regime. The systematic response of monetary policy works against such second-round effects in the adverse regime but is insufficient to fully offset them. The model also delivers (conditional) probabilities for being (staying) in either regime, which may help interpret oil price fluctuations – and inform deliberations on the adequate policy response – in real-time.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes