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Person

James Mitchell

Vice President
Areas of Expertise empirical macroeconomics, econometrics, forecasting
Department Research
Education
  • BA,
  • Economics,
  • University of Durham,
  • 1994
  • MS,
  • Economics,
  • University of Bristol,
  • 1995
  • PhD,
  • Economics,
  • University of Cambridge,
  • 1999

James Mitchell is a vice president in the Research Department at the Federal Reserve Bank of Cleveland. He leads the macroeconomic forecasting group, which is responsible for developing models for economic forecasting and policy analysis. His research interests are in macroeconomic modeling and forecasting.

Before joining the Cleveland Reserve Bank in 2021, Dr. Mitchell was professor of economic modeling and forecasting at the University of Warwick, where from 2013 to 2018 he headed the economic modeling and forecasting group at Warwick Business School. In academic year 2018–2019, he was a visiting scholar at the University of Southern California. Prior to Warwick, he was professor of economics and finance at the University of Leicester, and from 1999 to 2011 he worked at the National Institute of Economic and Social Research in London. He currently serves as an associate editor of the Journal of Applied Econometrics and the International Journal of Forecasting. In 2022, Dr. Mitchell was elected as a fellow of the International Association of Applied Econometrics.

Dr. Mitchell holds a BA in economics from Durham University, an MSc in economics from the University of Bristol, and a PhD in economics from the University of Cambridge.

Featured Publications

    • Working Papers

      • Working Paper
        The Effects of Interest Rate Increases on Consumers' Inflation Expectations: The Roles of Informedness and Compliance
        Edward S. Knotek II James Mitchell Mathieu Pedemonte Taylor Shiroff
        01.03.2024 | WP 24-01
        We study how monetary policy communications associated with increasing the federal funds rate causally affect consumers' inflation expectations. In a large-scale, multi-wave randomized controlled trial (RCT), we find weak evidence on average that communicating policy changes lowers consumers' medium-term inflation expectations. However, information differs systematically across demographic groups, in terms of ex ante informedness about monetary policy and ex post compliance with the information treatment. Monetary policy communications have a much stronger effect on people who had not previously heard news about monetary policy and who take sufficient time to read the treatment, implying scope to increase the impact of communications by targeting specific groups of the general public. Our findings show that, in an inflationary environment, consumers expect that raising interest rates will lower inflation. More generally, our results emphasize the importance of measuring both respondents' information sets and their compliance with treatment when using RCTs in empirical macroeconomics, to better understand the well-documented evidence of heterogeneous treatment effects.

        Topics: Inflation, Monetary policy
      • Working Paper
        The Distributional Predictive Content of Measures of Inflation Expectations
        James Mitchell Saeed Zaman
        11.30.2023 | WP 23-31
        This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in the upper tail. The predictive ability of measures of inflation expectations is greatest when combined. We show that it is helpful to let the combination weights on different agents’ expectations of inflation vary by quantile when assessing inflationary pressures probabilistically.

        Topics: Econometrics modeling and forecasting, Inflation, Monetary policy
      • Working Paper
        Predictive Density Combination Using a Tree-Based Synthesis Function
        Tony Chernis Niko Hauzenberger Florian Huber Gary Koop James Mitchell
        11.21.2023 | WP 23-30
        Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area’s Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits – in terms of improved forecast accuracy and interpretability – of modeling the synthesis function nonparametrically.

        Topics: Econometrics modeling and forecasting
      • Working Paper
        The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?
        Ilias Filippou James Mitchell My T. Nguyen
        08.30.2023 | WP 23-20
        Using close to 40 years of textual data from FOMC transcripts and the Federal Reserve staff's Greenbook/Tealbook, we extend Romer and Romer (2008) to test if the FOMC adds information relative to its staff forecasts not via its own quantitative forecasts but via its words. We use methods from natural language processing to extract from both types of document text-based forecasts that capture attentiveness to and sentiment about the macroeconomy. We test whether these text-based forecasts provide value-added in explaining the distribution of outcomes for GDP growth, the unemployment rate, and inflation. We find that FOMC tales about macroeconomic risks do add value in the tails, especially for GDP growth and the unemployment rate. For inflation, we find value-added in both FOMC point forecasts and narrative, once we extract from the text a broader set of measures of macroeconomic sentiment and risk attentiveness.

        Topics: Monetary policy, Econometrics modeling and forecasting
      • Working Paper
        Deep Neural Network Estimation in Panel Data Models
        Ilias Chronopoulos Katerina Chrysikou George Kapetanios James Mitchell Aristeidis Raftapostolos
        07.05.2023 | WP 23-15
        In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the “black box” of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time variation in the effectiveness of specific containment policies.

        Topics: Econometrics modeling and forecasting, Public health crises
      • Working Paper
        Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting
        Gary Koop Stuart McIntyre James Mitchell Aubrey Poon Ping Wu
        05.08.2023 | WP 23-09
        Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we develop methods to include a wide range of short data into a regional mixed-frequency VAR model. These short data include hitherto unexploited regional VAT turnover data. We address the problem of the ragged edge at both the beginning and end of our sample by estimating regional factors using different missing data algorithms that we then incorporate into our mixed-frequency VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.

        Topics: Econometrics modeling and forecasting, State and local economies
      • Working Paper
        Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics
        James Mitchell Aubrey Poon Dan Zhu
        04.11.2023 | WP 22-12R
        Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions.

        Topics: Econometrics modeling and forecasting, Economic growth
      • Working Paper
        Bayesian Modeling of Time-Varying Parameters Using Regression Trees
        Niko Hauzenberger Florian Huber Gary Koop James Mitchell
        01.11.2023 | WP 23-05
        In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.

        Topics: Business cycles, Econometrics modeling and forecasting
      • Working Paper
        Censored Density Forecasts: Production and Evaluation
        James Mitchell Martin Weale
        08.16.2022 | WP 21-12R
        This paper develops methods for the production and evaluation of censored density forecasts. The focus is on censored density forecasts that quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a fixed-point algorithm that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using historical forecast errors from the Federal Reserve Board and the Monetary Policy Committee (MPC) at the Bank of England suggests that the use of censored density functions to represent the pattern of forecast errors results in much greater parameter stability than do uncensored densities. We illustrate the utility of censored density forecasts when quantifying forecast risks after shocks such as the global financial crisis and the COVID-19 pandemic and and that these outperform the official forecasts produced by the MPC.

        Topics: Econometrics modeling and forecasting
      • Working Paper
        Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP
        Ana Beatriz Galvão James Mitchell
        07.13.2022 | WP 21-28R
        Economic statistics are commonly published without estimates of their uncertainty. We conduct two waves of a randomized controlled online experiment to assess if and how the UK public understands data uncertainty. A control group observes only the point estimate of GDP. Treatment groups are presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that GDP numbers are uncertain. Quantitative communications of data uncertainty help align the public’s subjective probabilistic expectations of data uncertainty with objective estimates, but do not decrease trust in the statistical office.

        Topics: International economics
      • Working Paper
        Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics
        James Mitchell Aubrey Poon Dan Zhu
        05.09.2022 | WP 22-12
        Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the 'data speak.' Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions.

        Topics: Econometrics modeling and forecasting, Economic growth
      • Working Paper
        Using Stochastic Hierarchical Aggregation Constraints to Nowcast Regional Economic Aggregates
        Gary Koop Stuart McIntyre James Mitchell Aubrey Poon
        03.03.2022 | WP 22-06
        Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a key role in delivering improved regional nowcasts; they prove to be more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.

        Topics: Econometrics modeling and forecasting, International economics
      • Working Paper
        Reconciled Estimates of Monthly GDP in the US
        Gary Koop Stuart McIntyre James Mitchell Aubrey Poon
        01.10.2022 | WP 22-01
        In the US, income and expenditure-side estimates of GDP (GDPI and GDPE) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (that is, the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

        Topics: Econometrics modeling and forecasting, Business cycles
      • Working Paper
        Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP
        Ana Beatriz Galvão James Mitchell
        12.22.2021 | WP 21-28
        Economic statistics are commonly published without any explicit indication of their uncertainty. To assess if and how the UK public interprets and understands data uncertainty, we conduct two waves of a randomized controlled online experiment. A control group is presented with the headline point estimate of GDP, as emphasized by the statistical office. Treatment groups are then presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that uncertainty is inherent in official GDP numbers. But communicating uncertainty information improves understanding. It encourages the public not to take estimates at face-value, but does not decrease trust in the data. Quantitative tools to communicate data uncertainty - notably intervals, density strips, and bell curves - are especially beneficial. They reduce dispersion of the public’s subjective probabilistic expectations of data uncertainty, improving alignment with objective estimates.

        Topics: International economics
      • Working Paper
        Censored Density Forecasts: Production and Evaluation
        James Mitchell Martin Weale
        05.27.2021 | WP 21-12
        This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a new estimator that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using historical forecast errors from the Federal Reserve Board and the Monetary Policy Committee at the Bank of England illustrates the utility of censored density forecasts when quantifying forecast risks after shocks such as the global financial crisis and the COVID-19 pandemic.

        Topics: Econometrics modeling and forecasting

    • Economic Commentaries

  • Book Chapters and Book Reviews

    • “The Evolution of Forecast Density Combinations in Economics.” With Knut Are Aastveit, Herman van Dijk, and Francesco Ravazzolo. Oxford Research Encyclopedia of Economics and Finance. 2019.
    • “Model Specifications and a Typology of Rapid Estimates.” With Dominique Ladiray, and Fabbio Bachini. In Handbook on Rapid Estimates. European Commission and United Nations, 2017.
    • “Combined Nowcasts.” With Silvia Lui. In Handbook on Rapid Estimates, European Commission and United Nations, 2017.
    • “Alternative Detrending Methods.” With Gian Luigi Mazzi and Ataman Ozyildiri. In Handbook on Cyclical Composite Indicators: For Business Cycle Analysis in Collaboration with The Conference Board, Inc. European Commission and United Nations, 2017.
    • “Aggregate versus Disaggregate Approaches to Constructing Rapid Estimates.” With Silvia Lui and Gian Luigi Mazzi. In Handbook on Rapid Estimates, European Commission and United Nations, 2017.
    • Peer Review of and Comment on “Evaluating Forecast Performance.” Bank of England, 2015.
    • “Nowcasting Quarterly Euro Area GDP Growth Using a Global VAR Model.” With Silvia Lui. In The GVAR Handbook: Structure and Applications of a Macro Model of the Global Economy for Policy Analysis. Edited by Filippo di Mauro and M. Hashem Pesaran. Oxford University Press, 2013.
    • “Macro Modelling with Many Models.” With Ida Wolden Bache, Francesco Ravazzolo, and Shaun P. Vahey. In Twenty Years of Inflation Targeting: Lessons Learned and Future Prospects. Edited by D. Cobham, Ø. Eitrheim, S. Gerlach, and J. Qvigstad. Cambridge University Press, Cambridge, 2010.
    • “Uncertainty Bounds for Cyclically Adjusted Budget Balances.” With R. Barrell and I. Hurst, Fiscal Policy Making in the European Union: an Assessment of Current Practice and Challenges. Edited by M. Larch and J. Nogueira Martins. Routledge, Oxford, 2009.
    • “Recent Developments in Density Forecasting.” With Stephen G. Hall. In Palgrave Handbook of Econometrics Volume II. Edited by. K. Patterson and T. Mills, 2009.
    • “Density Estimates for Real-Time Eurozone Output Gap Estimates.” In Growth and Cycle in the Eurozone. Edited by Gian Luigi Mazzi and Giovanni Savio, Palgrave, Basingstoke, 2007.
    • “Is There a Common Eurozone Business Cycle?” With K. Mouratidis. Monographs of Official Statistics: Papers and Proceedings of the third Eurostat Colloquium on Modern Tools for Business Cycle Analysis. Edited by Gian Luigi Mazzi and Giovanni Savio, Luxembourg–Office for Official Publications of the European Communities, 2004.
    • “Revisions to Economic Statistics.” Statistics Commission Report No. 17, Volume 2, 2004.
    • “A Review of ‘Matrix Calculus & Zero-One Matrices: Statistical and Econometric Applications’ by Darrell A. Turkington.” Economic Journal, 113: 398–400. 2003.