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Forecasting GDP growth from the outer space

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Abstract

We evaluate the usefulness of satellite-based data on nighttime lights for the prediction of annual GDP growth across a global sample of countries. Going beyond traditional measures of luminosity, such as the sum of lights within a country’s borders, we propose several innovative distribution- and location-based indicators attempting to extract new predictive information from the night lights data. Whereas our ?ndings are generally favorable to the use of the night lights data to improve the accuracy of simple autoregressive model-based forecasts, we also ?nd a substantial degree of heterogeneity across countries on the estimated relationships between light emissions and economic activity: individually estimated models tend to outperform pooled speci?cations, even though the latter provide more ef?cient estimates for out-of-sample forecasting. The estimation uncertainty affecting the country-speci?c estimates tends to be more pronounced for low and lower middle income countries. We conduct bootstrapped inference in order to evaluate the statistical signi?cance of our results.

Suggested Citation

  • Jaqueson K Galimberti, 2017. "Forecasting GDP growth from the outer space," KOF Working papers 17-427, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:17-427
    DOI: 10.3929/ethz-a-010852413
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    Cited by:

    1. Flávio Menezes & Vivian Figer & Fernanda Jardim & Pedro Medeiros, 2021. "Using electricity consumption to predict economic activity during COVID-19 in Brazil," Discussion Papers Series 641, School of Economics, University of Queensland, Australia.
    2. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," Papers 2007.03477, arXiv.org.
    3. Carlo Fezzi & Valeria Fanghella, 2020. "Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 885-900, August.
    4. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    5. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," DEM Working Papers 2020/8, Department of Economics and Management.
    6. Jaqueson Galimberti & Stefan Pichler & Regina Pleninger, 2020. "Measuring Inequality using Geospatial Data," Working Papers 2020-07, Auckland University of Technology, Department of Economics.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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