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Mixed Sampling Panel Data Model for Regional Job Vacancies Forecasting

Author

Listed:
  • Dorin JULA

    (Institute for Economic Forecasting, Romanian Academy/Ecological University of Bucharest, Department of Economics)

  • Nicolae Marius JULA

    ("Nicolae Titulescu" University of Bucharest)

Abstract

We tested, for Romania, the relationship at regional level between the job vacancies rate (with quarterly frequency) as explained variable and, as explanatory variables (regressors) unemployment rate (available with monthly frequency), respectively gross domestic product growth rate (who is presented at annual frequency). To identify the regional (cross-sections) specific effects, we use a panel data model. Since the analysed variables have different frequencies, the panel data model was built based on the MIDAS methodology. To avoid the cross-section correlation between errors, we used the SUR methodology to estimate the model's parameters. We found that, for all eight Romanian regions, the job vacancies are negative associated with unemployment (this result is consistent with the theory of Beveridge curve), and positive correlated with economic growth (and this result is in line with Okun's theory). The data also show a significant inertial effect in regional dynamics of vacancies.

Suggested Citation

  • Dorin JULA & Nicolae Marius JULA, 2017. "Mixed Sampling Panel Data Model for Regional Job Vacancies Forecasting," Eco-Economics Review, Ecological University of Bucharest, Economics Faculty and Ecology and Environmental Protection Faculty, vol. 3(1), pages 3-20, June.
    • Handle: RePEc:eub:ecoecr:v:1:y:2017:i:1:p:3-20
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    References listed on IDEAS

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    2. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    3. Beck, Nathaniel & Katz, Jonathan N., 1995. "What To Do (and Not to Do) with Time-Series Cross-Section Data," American Political Science Review, Cambridge University Press, vol. 89(3), pages 634-647, September.
    4. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    5. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
    6. Dorin JULA & Nicolae-Marius JULA, 2017. "Foreign Direct Investments and Employment. Structural Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 29-44, June.
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    More about this item

    Keywords

    job vacancy; unemployment rate; gross domestic product; panel data model; MIDAS; forecasting methods;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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