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Joan Paredes

Economics

Division

Forecasting and Policy Modelling

Current Position

Senior Economist

Fields of interest

Mathematical and Quantitative Methods,Macroeconomics and Monetary Economics,Public Economics

Email

joan.paredes@ecb.europa.eu

Education
2007-2016

Doctorate in Economics, Goethe University Frankfurt (Adviser: Thomas Laubach)

2000-2003

Degree in Finance and Actuarial Science, University of Barcelona and Karlsruhe Institut of Technology

1997-2001

Degree in Economics, University of Barcelona

Professional experience
2020

Senior Economist - Forecast and Policy Modelling Division, Directorate General Economics, European Central Bank

2017-2019

Economist - Forecast and Policy Modelling Division, Directorate General Economics, European Central Bank

2013-2017

Economist - Monetary Policy Research Division, Directorate General Research, European Central Bank

2007-2013

Economist-Statistician - Fiscal Policies Division, Directorate General Economics, European Central Bank

2005-2007

Research analyst - Statistical Information Management and User Services Division, Directorate General Statistics, European Central Bank

2004-2005

Trainee - Monetary, Financial Institutions and Markets Statistics Division, Directorate General Statistics, European Central Bank

2004-2004

Trainee - Operations Evaluation unit, European Investment Bank

2003-2004

Trainee - Public Accounts and Taxation Department, Eurostat

21 September 2021
OCCASIONAL PAPER SERIES - No. 267
Details
Abstract
This paper provides an assessment of the macroeconomic models regularly used for forecasting and policy analysis in the Eurosystem. These include semi-structural, structural and time-series models covering specific jurisdictions and the euro area within a closed economy, small open economy, multi-country or global setting. Models are used as analytical frameworks for building baseline projections and for supporting the preparation of monetary policy decisions. The paper delivers four main contributions. First, it provides a survey of the macroeconomic modelling portfolios currently used or under development within the Eurosystem. Second, it explores the analytical gaps in the Eurosystem models and investigates the scope for further enhancement of the main projection and policy models, and the creation of new models. Third, it reviews current practices in model-based analysis for monetary policy preparation and forecasting and provides recommendations and suggestions for improvement. Finally, it reviews existing cooperation modalities on model development and proposes alternative sourcing and organisational strategies to remedy any knowledge or analytical gaps identified.
JEL Code
C5 : Mathematical and Quantitative Methods→Econometric Modeling
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
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
F4 : International Economics→Macroeconomic Aspects of International Trade and Finance
21 September 2021
OCCASIONAL PAPER SERIES - No. 264
Details
Abstract
This paper summarises the findings of the Eurosystem’s Expert Group on Inflation Expectations (EGIE), which was one of the 13 work streams conducting analysis that fed into the ECB’s monetary policy strategy review. The EGIE was tasked with (i) reviewing the nature and behaviour of inflation expectations, with a focus on the degree of anchoring, and (ii) exploring the role that measures of expectations can play in forecasting inflation. While it is households’ and firms’ inflation expectations that ultimately matter in the expectations channel, data limitations have meant that in practice the focus of analysis has been on surveys of professional forecasters and on market-based indicators. Regarding the anchoring of inflation expectations, this paper considers a number of metrics: the level of inflation expectations, the responsiveness of longer-term inflation expectations to shorter-term developments, and the degree of uncertainty. Different metrics can provide conflicting signals about the scale and timing of potential unanchoring, which underscores the importance of considering all of them. Overall, however, these metrics suggest that in the period since the global financial and European debt crises, longer-term inflation expectations in the euro area have become less well anchored. Regarding the role measures of inflation expectations can play in forecasting inflation, this paper finds that they are indicative for future inflationary developments. When it comes to their predictive power, both market-based and survey-based measures are found to be more accurate than statistical benchmarks, but do not systematically outperform each other. Beyond their role as standalone forecasts, inflation expectations bring forecast gains when included in forecasting models and can also inform scenario and risk analysis in projection exercises performed using structural models. ...
JEL Code
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
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
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
3 May 2021
WORKING PAPER SERIES - No. 2543
Details
Abstract
This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including survey forecasts for predicting euro area inflation and GDP growth at medium-term forecast horizons using both univariate and multivariate forecasting metrics. Results show that the Survey of Professional Forecasters (SPF) provides good point forecast performance, but also that SPF forecasts perform poorly in terms of densities for all variables and horizons. Accordingly, when the model combination or the individual models are tilted to SPF's first moments, point accuracy and calibration improve, whereas they worsen when SPF's second moments are included. We conclude that judgement incorporated in survey forecasts can considerably increase model forecasts accuracy, however, the way and the extent to which it is incorporated matters.
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
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
28 January 2019
WORKING PAPER SERIES - No. 2227
Details
Abstract
The Eurosystem staff forecasts are conditional on the financial markets, the global economy and fiscal policy outlook, and include expert judgement. We develop a multi-country BVAR for the four largest countries of the euro area and we show that it provides accurate conditional forecasts of policy relevant variables such as, for example, consumer prices and GDP. The forecasting accuracy and the ability to mimic the path of the Eurosystem projections suggest that the model is a valid benchmark to assess the consistency of the projections with the conditional assumptions. As such, the BVAR can be used to identify possible sources of judgement, based on the gaps between the Eurosystem projections and the historical regularities captured by the model.
JEL Code
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
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
23 August 2017
WORKING PAPER SERIES - No. 2094
Details
Abstract
Due to input-output linkages, an industry level shock can widely transmit to the rest of the economy. We identify government policies on the automobile industry, which change final prices and estimate their effect on sales and production. An example could be the scrappage schemes that many European governments introduced at the start of the Great Recession. In line with previous studies, we confirm that the effect on car sales is positive. More interestingly, we extend the literature that explores the effects of these policies on domestic and foreign production to disentangle the potential spill-overs.
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
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
E23 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Production
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
H25 : Public Economics→Taxation, Subsidies, and Revenue→Business Taxes and Subsidies
5 August 2015
WORKING PAPER SERIES - No. 1834
Details
Abstract
Should rational agents take into consideration government policy announcements? A skilled agent (an econometrician) could set up a model to combine the following two pieces of information in order to anticipate the future course of fiscal policy in real-time: (i) the ex-ante path of policy as published/announced by the government; (ii) incoming, observed data on the actual degree of implementation of ongoing plans. We formulate and estimate empirical models for a number of EU countries (Germany, France, Italy, and Spain) to show that government (consumption) targets convey useful information about ex-post policy developments when policy changes significantly (even if past credibility is low) and when there is limited information about the implementation of plans (e.g. at the beginning of a fiscal year). In addition, our models are instrumental to unveil the current course of policy in real-time. Our approach complements a well-established branch of the literature that finds politically-motivated biases in policy targets.
JEL Code
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
H30 : Public Economics→Fiscal Policies and Behavior of Economic Agents→General
H68 : Public Economics→National Budget, Deficit, and Debt→Forecasts of Budgets, Deficits, and Debt
E61 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Policy Objectives, Policy Designs and Consistency, Policy Coordination
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
22 May 2013
WORKING PAPER SERIES - No. 1550
Details
Abstract
Given the increased importance of fiscal monitoring, this study amends the existing literature in the …field of intra-annual fiscal data in two main dimensions. First, we use quarterly fiscal data to forecast a very disaggregated set of fiscal series at annual frequency. This makes the analysis useful in the typical forecasting environment of large institutions, which employ a "bottom-up" or disaggregated framework. Aside from this practical type of consideration, we find that forecasts for total revenues and expenditures via their subcomponents can actually result more accurate than a direct forecast of the aggregate. Second, we employ a Mixed Data Sampling (MiDaS) approach to analyze mixed frequency …fiscal data, which is a methodological novelty. It is shown that MiDaS is the best approach for the analysis of mixed frequency fiscal data compared to two alternative approaches. The results regarding the information content of quarterly fiscal data confirm previous work that such data should be taken into account as it becomes available throughout the year for improving the end-year forecast. For instance, once data for the third quarter is incorporated, the annual forecast becomes very accurate (very close to actual data). We also benchmark against the European Commission's forecast and find the results fare favorably, particularly when considering that they stem from a simple univariate framework.
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
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
H68 : Public Economics→National Budget, Deficit, and Debt→Forecasts of Budgets, Deficits, and Debt
11 December 2009
WORKING PAPER SERIES - No. 1133
Details
Abstract
We analyse the impact of fiscal policy shocks in the euro area as a whole, using a newly available quarterly dataset of fiscal variables for the period 1981-2007. To allow for comparability with previous results on euro area countries and the US, we use a standard structural VAR framework, and study the impact of aggregated and disaggregated government spending and net taxes shocks. In addition, to frame euro area results, we apply the same methodology for the same sample period to US data. We also explore the sensitivity of the provided results to the inclusion of variables aiming at measuring “financial stress” (increases in risk) and “fiscal stress” (sustainability concerns). Analysing US and euro area data with a common methodology provides some interesting insights on the interpretation of fiscal policy shocks.
JEL Code
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
H30 : Public Economics→Fiscal Policies and Behavior of Economic Agents→General
11 December 2009
WORKING PAPER SERIES - No. 1132
Details
Abstract
The analysis of the macroeconomic impact of fiscal policies in the euro area has been traditionally limited by the absence of quarterly fiscal data. To overcome this problem, we provide two new databases in this paper. Firstly, we construct a quarterly database of euro area fiscal variables for the period 1980-2008 for a quite disaggregated set of fiscal variables; secondly, we present a real-time fiscal database for a subset of fiscal variables, composed of biannual vintages of data for the euro area period (2000-2009). All models are multivariate, state space mixed- frequencies models estimated with available national accounts fiscal data (mostly annual) and, more importantly, monthly and quarterly information taken from the cash accounts of the governments. We provide not seasonally- and seasonally-adjusted data. Focusing solely on intra-annual fiscal information for interpolation purposes allows us to capture genuine intra-annual "fiscal" dynamics in the data. Thus, we provide fiscal data that avoid some problems likely to appear in studies using fiscal time series interpolated on the basis of general macroeconomic indicators, namely the well-known decoupling of tax collection from the evolution of standard macroeconomic tax bases (revenue windfalls/shortfalls).
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
H6 : Public Economics→National Budget, Deficit, and Debt
2019
International Journal of Forecasting
  • Angelini, E., Lalik, M., Lenza, M., Paredes, J.
2018
The Scandinavian Journal of Economics
  • Asimakopoulos, S., Paredes, J. & Warmedinger, T.
2014
Journal of Policy Modeling
  • Paredes, J., Pedregal, D. J. & Pérez, J. J.
2010
Fiscal Studies
  • Burriel, P., de Castro, F., Garrote, D., Gordo, E., Paredes, J. & Pérez, J. J.
2016
ECB Research Bulletin article
  • Paredes, J.
2015
VoxEU article
  • Paredes, J., Pérez, J.J. and Pérez-Quirós, G.
2013
IMF Working Paper
  • Gray, D., Groß, M., Paredes, J. and Sydow, M.