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Mixed in New Zealand: Nowcasting Labour Markets with MIDAS

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Abstract

Decision-making in monetary policy is based on a large amount of information arriving at different frequencies. Given that many important economic variables are released with considerable time lags at low frequencies, policymakers often face the problem of assessing the current state of the economy with incomplete information. In New Zealand, the key labour market indicators, such as the unemployment rate, employment growth and labour force participation rate, are all published at a quarterly frequency and are published with some delay. In this paper, we use the so-called MIDAS (mixed-data sampling) approach to incorporate mixed-frequency data to “nowcast” the current state of the labour market, based on monthly indicators, taking into account publication lags. By taking into account the persistence and lags of the indicators, MIDAS builds a complicated dynamic relationship between the indicators and the labour market variables we investigate. The main purpose of the current study is to demonstrate the power of MIDAS in a prototypical model and its potential in building more comprehensive forecast models of labour market variables. We show that better nowcasts of the current state of the labour market are obtained by using monthly data on dwelling consents, motor vehicle registrations, international migration and business confidence data, compared to first order autoregressive and time-averaging benchmarks. The improvement is more dramatic in the case of forecast combinations. We also show that most of the improvement in forecast accuracy is obtained from the data available in the first month of the quarter. These results suggest that taking care of the mixed-frequency data with MIDAS improves our assessment of the current state of labour markets in New Zealand.

Suggested Citation

  • Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
  • Handle: RePEc:nzb:nzbans:2019/04
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    References listed on IDEAS

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    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    3. 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.
    4. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    6. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    7. Anthony S. Tay, 2006. "Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth," Macroeconomics Working Papers 22480, East Asian Bureau of Economic Research.
    8. Lars E.O. Svensson, 2014. "How to Weigh Unemployment Relative to Inflation in Monetary Policy?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(S2), pages 183-188, October.
    9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    10. 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.
    11. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    12. Anesti, Nikoleta & Hayes, Simon & Moreira, Andre & Tasker, James, 2017. "Peering into the present: the Bank’s approach to GDP nowcasting," Bank of England Quarterly Bulletin, Bank of England, vol. 57(2), pages 122-133.
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    Cited by:

    1. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.

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