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Joan Paredes
Economics
- Division
Prices & Costs
- Current Position
-
Principal Economist
- Fields of interest
-
Mathematical and Quantitative Methods,Macroeconomics and Monetary Economics,Public Economics
- 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
- 2024
Principal Economist - Prices and Costs Division, Directorate General Economics, European Central Bank
- 2020-2023
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
- 1 August 2024
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2024Details
- Abstract
- Available surveys of selling price expectations of firms for the euro area point to price dynamics that have substantially moderated after the recent large inflationary shock, albeit with a more sluggish adjustment for services than for goods. This box focuses on services and investigates the leading indicator properties of the forward-looking survey data provided by the European Commission. These data tend to anticipate notable turning points in services inflation. In addition, in a forecasting exercise, we find that their leading properties manifest in a non-linear way and are particularly useful in unusual times, such as during the recent inflation spike.
- 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
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
- 20 March 2024
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 2, 2024Details
- Abstract
- Food price inflation was one of the main contributors to the strong rise in euro area headline inflation in 2022 and to the period of disinflation that followed. An extraordinary surge in energy costs was the main factor behind higher consumer food inflation in 2021 and 2022. Increases in global food commodity prices and euro area farm gate prices also contributed significantly. In the high inflation environment, domestic factors – such as wage and profit developments – have gradually emerged as increasingly important factors in keeping food inflation elevated. The exceptional, largely externally driven shocks to energy, global food commodity and euro area farm gate prices are fading, and this is expected to contribute to a moderation in food inflation in the course of 2024. However, strong wage growth implies only a gradual unwinding going forward.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
Q02 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→General→Global Commodity Markets
Q43 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy→Energy and the Macroeconomy
- 15 March 2024
- OCCASIONAL PAPER SERIES - No. 344Details
- Abstract
- This paper takes stock of the ECB’s macroeconometric modelling strategy by focusing on the models and applications used in the Forecasting and Policy Modelling Division. We focus on the guiding principles underpinning the current portfolio of the main macroeconomic models and illustrate how they can in principle be used for economic forecasting, scenario and risk analyses. We also discuss the modelling agenda which is currently under development, focusing notably on heterogeneity, machine learning, expectation formation and climate change. The paper makes it clear that the large macroeconometric models typically developed in central banks remain stylised descriptions of our modern economies and can fail to predict or assess the nature of economic events (especially when big crises arise). But even in highly uncertain economic conditions, they can still provide a meaningful contribution to policy preparation. We conclude the paper with a roadmap which will allow the ECB and the Eurosystem to exploit technological advances and cooperation across institutions as a useful means of ensuring that the modelling framework is not only resilient to disruptive events but also innovative.
- JEL Code
- C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 30 October 2023
- WORKING PAPER SERIES - No. 2857Details
- Abstract
- Official estimates of economic growth are regularly revised and therefore forecasts for GDP growth are done on the basis of ever-changing data. The economic literature has intensively studied the properties of those revisions and their implications for forecasting models. However, it is much less known about the reasons for Statistical Agencies (SAs) to revise their estimates. In order to be timely and reliable, SAs have an explicit interest in not revising their initial GDP estimates too much, while they are much more open to revise GDP components over time. More than a curiosity, we exploit this resulting cross-correlation of GDP components revisions to build a model to better forecast GDP.
- JEL Code
- C01 : Mathematical and Quantitative Methods→General→Econometrics
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E01 : Macroeconomics and Monetary Economics→General→Measurement and Data on National Income and Product Accounts and Wealth, Environmental Accounts
- 17 October 2023
- RESEARCH BULLETIN - No. 112Details
- Abstract
- Inflation forecasts and their risks are key for monetary policy decisions. The strategy review concluded in 2021 highlighted how most Eurosystem models used to forecast inflation are linear. Linear models assume that changes in, for example, wages, always have the same fixed, proportional effect on inflation. A new machine learning model, recently developed at the ECB, captures very general forms of non-linearity, such as a changing sensitivity of inflation dynamics to prevailing economic circumstances. Forecasts from this machine learning model closely track Eurosystem staff inflation projections, suggesting that these projections capture mild non-linearity in inflation dynamics – likely owing to expert judgement – and are in line with state-of-the-art econometric methodologies.
- 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
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
- 4 August 2023
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2023Details
- Abstract
- This box describes some key measures of underlying inflation and reassesses their predictive power for euro area headline inflation over a medium-term horizon. It discusses recent developments in underlying inflation and implications for the inflation outlook. It examines how underlying inflation measures can be adjusted to filter out some of the recent extraordinary shocks to inflation. Lastly, it analyses goods and services inflation individually.
- JEL Code
- C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
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
- 14 July 2023
- WORKING PAPER SERIES - No. 2830Details
- Abstract
- Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large set of determinants, is competitive with state-of-the-art linear benchmarks and judgemental survey forecasts. The median forecasts of the quantile regression forest are very collinear with the ECB point inflation forecasts, displaying similar deviations from “linearity”. Given that the ECB modelling toolbox is overwhelmingly linear, this finding suggests that the expert judgement embedded in the ECB forecast may be characterized by some mild non-linearity.
- 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
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
- 6 December 2022
- WORKING PAPER SERIES - No. 2754Details
- Abstract
- This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target distributions the researcher has access to. As an example, this paper shows how to implement the algorithm by conditioning the forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results show that increased asymmetric upside risks to oil prices result in upside risks to inflation as well as higher core-inflation over the considered forecasting horizon. Finally, a real-time forecasting exercise yields that introducing market-based information on the oil price improves inflation and GDP forecasts during crises times such as the COVID pandemic.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
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
- 21 September 2021
- OCCASIONAL PAPER SERIES - No. 267Details
- 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. 264Details
- 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. 2543Details
- 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. 2227Details
- 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. 2094Details
- 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. 1834Details
- 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. 1550Details
- 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. 1133Details
- 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. 1132Details
- 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
- 2024
- SUERF Policy Brief
- 2024
- Handbook on Macroeconomic ForecastingForecasting inflation in the US and in the euro area
- 2024
- VoxEU article
- 2023
- Journal of Applied Econometrics
- 2023
- SUERF Policy Brief
- 2019
- International Journal of Forecasting
- 2018
- The Scandinavian Journal of Economics
- 2016
- ECB Research Bulletin article
- 2015
- VoxEU article
- 2014
- Journal of Policy Modeling
- 2013
- IMF Working Paper
- 2010
- Fiscal Studies