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Frauke Skudelny

1 May 2001
WORKING PAPER SERIES - No. 64
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Abstract
This paper estimates an import demand function for the euro area vis--vis its main extra-area trading partners which takes into account the possible impact of both intra- and extra-euro area exchange rate uncertainty. We derive a theoretical model which captures various mechanisms by which exchange rate volatility may influence the demand for extra-euro area imports. If importers are risk averse, the model predicts not only a negative effect of exchange rate volatility, but also substitution possibilities between extra- and intra-area imports due to differences in the degree of extra- and intra-area exchange rate volatility. The magnitude of these impacts also depends on the share of trade invoiced in foreign currency (and not hedged) as well as the degree of substitutability between the imports of different suppliers. Using quarterly data for the past eleven years, panel estimates suggest that extra-area exchange rate volatility may have decreased extra-euro area imports by around 10 per cent. Although such quantitative estimates should be treated with caution, the magnitude of our estimate is similar to other studies which find a statistically significant impact of exchange rate volatility on trade flows. Finally, we also provide some limited evidence that differences in extra- and intra-area exchange rate volatility may have resulted in substitution between extra- and intra-area imports.
JEL Code
F15 : International Economics→Trade→Economic Integration
F31 : International Economics→International Finance→Foreign Exchange
22 July 2004
WORKING PAPER SERIES - No. 374
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Abstract
In this paper we investigate whether the forecast of the HICP components (indirect approach) improves upon the forecast of overall HICP (direct approach) and whether the aggregation of country forecasts improves upon the forecast of the euro-area as a whole, considering the four largest euro area countries. The direct approach provides clearly better results than the indirect approach for 12 and 18 steps ahead for the overall HICP, while for shorter horizons the results are mixed. For the euro area HICP excluding unprocessed food and energy(HICPX), the indirect forecast outperforms the direct whereas the differences are only marginal for the countries. The aggregation of country forecasts does not seem to improve upon the forecast of the euro area HICP and HICPX. This result has however to be taken with caution as differences appear to be rather small and due to the limited country coverage.
JEL Code
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
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
28 February 2005
WORKING PAPER SERIES - No. 446
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Abstract
This paper contributes to the literature on the impact of EMU on trade, adding two new elements. First, we propose a theoretical model for explaining how the euro could have increased trade by the large amounts found in the empirical literature. Second, we propose a sectoral dataset to test the insights from the theory. Our theoretical model shows that in a monopolistic competition set-up, the effect of exchange rate uncertainty on trade has nonlinear features, suggesting that EMU and a standard measure for exchange rate uncertainty should be jointly significant. Our empirical results confirm this finding, with a trade creating effect between 108 and 140% in a pooled regression, and between 54 to 88% when sectors are estimated individually. Importantly, we find evidence for a trade creating effect also for trade with third countries.
JEL Code
F12 : International Economics→Trade→Models of Trade with Imperfect Competition and Scale Economies, Fragmentation
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
E0 : Macroeconomics and Monetary Economics→General
16 October 2005
WORKING PAPER SERIES - No. 532
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Abstract
The aim of this paper is to improve our understanding of the key determinants of intra- and extra-euro area imports. Using a simultaneous equation estimation framework, and pooling the data across nine euro area countries as an approximation of the euro area, we estimate intra- and extra-euro area import demand functions and impose various restrictions within and across equations. We find that there are significant substitution effects between intra- and extra-euro area imports due to changes in their relative prices, while exchange rate volatility decreases trade vis-à-vis regions characterised by volatility and leads to substitution of trade away from higher-volatility regions towards lower-volatility 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
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
E00 : Macroeconomics and Monetary Economics→General→General
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
O3 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights
10 December 2008
WORKING PAPER SERIES - No. 975
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Abstract
This paper derives forecasts for euro area real GDP growth based on a bottom up approach from the production side. That is, GDP is forecast via the forecasts of value added across the different branches of activity, which is quite new in the literature. Linear regression models in the form of bridge equations are applied. In these models earlier available monthly indicators are used to bridge the gap of missing GDP data. The process of selecting the best performing equations is accomplished as a pseudo real time forecasting exercise, i.e. due account is taken of the pattern of available monthly variables over the forecast cycle. Moreover, by applying a very systematic procedure the best performing equations are selected from a pool of thousands of test bridge equations. Our modelling approach, finally, includes a further novelty which should be of particular interest to practitioners. In practice, forecasts for a particular quarter of GDP generally spread over a prolonged period of several months. We explore whether over this forecast cycle, where GDP is repeatedly forecast, the same set of equations or different ones should be used. Changing the set of bridge equations over the forecast cycle could be superior to keeping the same set of equations, as the relative merit of the included monthly indictors may shift over time owing to differences in their data characteristics. Overall, the models derived in this forecast exercise clearly outperform the benchmark models. The variables selected in the best equations for different situations over the forecast cycle vary substantially and the achieved results confirm the conjecture that allowing the variables in the bridge equations to differ over the forecast cycle can lead to substantial improvements in the forecast accuracy.
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
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications
9 January 2009
OCCASIONAL PAPER SERIES - No. 100
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Abstract
The first part of this paper provides a brief survey of the recent literature that employs survey data on household finance and consumption. Given the breadth of the topic, it focuses on issues that are particularly relevant for policy, namely: i) wealth effects on consumption, ii) housing prices and household indebtedness, iii) retirement income, consumption and pension reforms, iv) access to credit and credit constraints, v) financial innovation, consumption smoothing and portfolio selection and vi) wealth inequality. The second part uses concrete examples to summarise how results from such surveys feed into policy-making within the central banks that already conduct such surveys.
JEL Code
C42 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Survey Methods
D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
Network
Eurosystem Monetary Transmission Network
26 May 2009
WORKING PAPER SERIES - No. 1057
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Abstract
This paper adds to the literature on wealth effects on consumption by disentangling financial wealth effects from housing wealth effects for the euro area. We use two macro-datasets for our estimations, one on the aggregate euro area for the period 1980-2006, and one on the individual euro area countries from1995-2006, using panel data techniques. The impact of all wealth variables on euro area consumption is significant and positive in most specifications for both datasets. The marginal propensity to consume (MPC) out of financial wealth is roughly in line with the literature, with 2.4 to 3.6 cents per euro of financial wealth spent on consumption according to the estimations with euro area aggregate data. However, the panel estimation yields somewhat lower results (0.6 to 1.1 cents). The MPC out of nominal housing wealth lies between 0.7 to 0.9 cents per euro for both datasets. When specifying housing wealth in real terms, i.e. when taking out the effect of volatile house prices, we find similar effects in the times series estimation while the MPC is larger in the panel estimation (2.5 cents).
JEL Code
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
11 November 2009
WORKING PAPER SERIES - No. 1104
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Abstract
In this paper we analyse in a mark-up framework the pass-through of commodity price and exchange rate shocks to the main components of producer and consumer prices. Thereby we link movements in prices at the different production stages as firms set their prices as a mark-up over production costs. The empirical results reveal significant linkages between different price stages in the euro area. The overall results are roughly in line with the literature and provide insight into the effects at different stages of the production chain. Non-energy commodity prices turn out to be important determinants of euro area prices.
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
16 June 2010
OCCASIONAL PAPER SERIES - No. 113
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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
15 September 2016
OCCASIONAL PAPER SERIES - No. 178
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Abstract
Global trade has been exceptionally weak over the past four years. While global trade grew at approximately twice the rate of GDP prior to the Great Recession, the ratio of global trade to GDP growth has declined to about unity since 2012. This paper assesses to what extent the change in the relationship between global trade and global economic activity is a temporary phenomenon or constitutes a lasting change. It finds that global trade growth has been primarily dampened by two factors. First, compositional factors, including geographical shifts in economic activity and changes in the composition of aggregate demand, have weighed on the sensitivity of trade to economic activity. Second, structural developments, such as waning growth in global value chains, a rise in non-tariff protectionist measures and a declining marginal impact of financial deepening, are dampening the support from factors that boosted global trade in the past. Notwithstanding the particularly pronounced weakness in 2015 that is assessed to be mostly a temporary phenomenon owing to a number of country-specific adverse shocks, the upside potential for trade over the medium term appears to be limited. The
JEL Code
F10 : International Economics→Trade→General
F13 : International Economics→Trade→Trade Policy, International Trade Organizations
F14 : International Economics→Trade→Empirical Studies of Trade
F15 : International Economics→Trade→Economic Integration
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
25 October 2016
WORKING PAPER SERIES - No. 1971
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Abstract
In this paper we analyse the role of the international trade network for the strength of the global recession across countries. The novelty of our paper is the use of value-added trade data to capture the importance of trade network structure. We estimate with BMA techniques how far network indicators measuring interlinkages in terms of value added trade has explanatory power both for the length and the depth of the recent crisis once we control for pre-crisis macroeconomic fundamentals. Our main findings are that the macroeconomic control variables with the strongest explanatory power for the length and the depth of the crisis are the growth rates of credit and of the real effective exchange rate in the pre-crisis period and, though to a lesser extent, GDP and inflation growth over the same period and pre-crisis foreign exchange reserves. Government debt, the GVC participation index and net foreign assets have very little explanatory power in the BMA estimations. The models
JEL Code
F14 : International Economics→Trade→Empirical Studies of Trade
C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C67 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Input?Output Models
Network
Competitiveness Research Network
28 December 2017
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 8, 2017
21 March 2018
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 2, 2018
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Abstract
Oil prices increased from around USD 45 per barrel at end-June 2017 to about USD 65 per barrel at the beginning of March 2018. The main drivers of this increase were stronger than expected growth in global demand, the strategy adopted by the Organization of the Petroleum Exporting Countries (OPEC) and some non-OPEC countries to adjust their production – partly offset by rising US production – and geopolitical events. This box analyses these factors, based on a structural vector autoregressive (SVAR) model, and assesses whether they are likely to persist.
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
F53 : International Economics→International Relations, National Security, and International Political Economy→International Agreements and Observance, International Organizations
Q41 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Demand and Supply, Prices
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
30 April 2019
OCCASIONAL PAPER SERIES - No. 221
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Abstract
The studies summarised in this paper focus on the economic implications of euro area firms’ participation in global value chains (GVCs). They show how, and to what extent, a large set of economic variables and inter-linkages have been affected by international production sharing. The core conclusion is that GVC participation has major implications for the euro area economy. Consequently, there is a case for making adjustments to standard macroeconomic analysis and forecasting for the euro area, taking due account of data availability and constraints.
JEL Code
F6 : International Economics→Economic Impacts of Globalization
F10 : International Economics→Trade→General
F14 : International Economics→Trade→Empirical Studies of Trade
F16 : International Economics→Trade→Trade and Labor Market Interactions
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles