[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/nzb/nzbans/2019-04.html
   My bibliography  Save this paper

Mixed in New Zealand: Nowcasting Labour Markets with MIDAS

Author

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
    as

    Download full text from publisher

    File URL: https://www.rbnz.govt.nz/-/media/ReserveBank/Files/Publications/Analytical%20notes/2019/AN2019-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    10. 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.
    11. 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.
    12. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    2. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    3. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    4. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
    5. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    6. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    7. 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.
    8. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    9. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    10. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    11. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    12. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
    13. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    14. Barsoum, Fady & Stankiewicz, Sandra, 2015. "Forecasting GDP growth using mixed-frequency models with switching regimes," International Journal of Forecasting, Elsevier, vol. 31(1), pages 33-50.
    15. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    16. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    17. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    18. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    19. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nzb:nzbans:2019/04. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Reserve Bank of New Zealand Knowledge Centre (email available below). General contact details of provider: https://edirc.repec.org/data/rbngvnz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.