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Real-Time Nowcasting Nominal GDP Under Structural Break

Author

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  • William Barnett

    (Department of Economics, University of Kansas; Center for Financial Stability, New York City; IC2 Institute, University of Texas at Austin)

  • Marcelle Chauvetz

    (University of California Riverside)

  • Danilo Leiva-Leonx

    (Bank of Canada)

Abstract

This paper provides early assessments of current U.S. Nominal GDP growth, which has been con- sidered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at di§erent frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information in?ow it also allows for potential parame- ter instability. We use small scale con?rmatory factor analysis in which the candidate variables are selected based on their ability to forecast GDP nominal. The model is fully estimated in one step using a nonlinear Kalman ?lter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Di§erently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate speci?cations. The results indicate that the proposed model containing information on real economic activity, in?ation, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth.

Suggested Citation

  • William Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
  • Handle: RePEc:kan:wpaper:201313
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    2. Ductor, Lorenzo & Leiva-Leon, Danilo, 2016. "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, vol. 102(C), pages 110-127.
    3. Gálvez-Soriano Oscar de Jesús, 2018. "Nowcasting Mexican GDP using Factor Models and Bridge Equations," Working Papers 2018-06, Banco de México.

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    More about this item

    Keywords

    Mixed Frequency; Ragged Edges; Real-Time; Nowcasting; Missing Data; Nonlinear; Structural Breaks; Dynamic Factor; Monetary Policy.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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