This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The paper is released in order to make the research of CompNet generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect those of the ECB, the ESCB, and of other organisations associated with the Network. Terms of use: Documents in AcknowledgementsThe authors would like to thank the participants in the Competitiveness Research Network meeting held at the European Central Bank in July 2014, where a preliminary version of the paper was presented, and an anonymous referee. Elena BobeicaEuropean Central Bank; e-mail: elena.bobeica@ecb.int Paulo Soares EstevesBanco de Portugal; e-mail: pmesteves@bportugal.pt António RuaBanco de Portugal, Universidade Nova de Lisboa; e-mail: antonio.rua@bportugal.pt Karsten StaehrTallinn University of Technology, Eesti Pank; e-mail: karsten.staehr@eestipank.ee Abstract The paper investigates the link between domestic demand pressure and exports by considering an error correction dynamic panel model for eleven euro area countries over the last two decades. The results suggest that there is a statistically significant substitution effect between domestic and foreign sales. Furthermore, this relationship appears to be asymmetric, as the link is much stronger when domestic demand falls than when it increases. Weakness in the domestic market translates into increased efforts to serve markets abroad, but, conversely, during times of boom, exports are not negatively affected by increasing domestic sales. This reorientation towards foreign markets was particularly important during the crisis period, and thus could represent a new adjustment channel to strong negative domestic shocks. The results have important policy implications, as this substitution effect between domestic and external markets might allow the euro area countries under stress to improve their trade outcomes with a relatively small downward pressure on domestic prices.
We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area inflation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. We illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability.
No 181 / January 2017Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.
We document the impact of COVID-19 on inflation modelling within a Vector Autoregression (VAR) model and provide guidance for forecasting euro area inflation during the pandemic. We show that estimated parameters are strongly affected, leading to different and sometimes implausible projections. As a solution, we propose to augment the VAR by allowing the residuals to have a fat-tailed distribution instead of a Gaussian one. This also outperforms with respect to unconditional forecasts. Yet, what brings sizeable forecast gains during the pandemic is adding meaningful off-model information, such as that entailed in the Survey of Professional Forecasters. The fat-tailed VAR looses part, but not all of its relative advantage compared to the Gaussian version when producing conditional inflation forecasts in a real-time setup. It is the joint fat-tailed errors and multi-equation modelling that manages to robustify models against extreme observations, in a single-equation model the same solution is less effective.
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