RDP 2024-04: Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator References

Anthonisz M (2021), ‘Daily Nowcasting of Global and Australian GDP Growth’, Economic research article, Queensland Treasury Corporation website, 26 May, viewed 10 January 2023. Available at <https://www.qtc.com.au/institutional-investors/news-and-publications/research/daily-nowcasting-of-global-and-australian-gdp-growth/>.

Armesto MT, KM Engemann and MT Owyang (2010), ‘Forecasting with Mixed Frequencies’, Federal Reserve Bank of St. Louis Review, 92(6), pp 521–536.

Aruoba SB, FX Diebold and C Scotti (2009), ‘Real-time Measurement of Business Conditions’, Journal of Business & Economic Statistics, 27(4), pp 417–427.

Australian Treasury (2018), ‘Nowcasting Australia's Gross Domestic Product’, Treasury Working Paper No 2018-04.

Aylmer C and T Gill (2003), ‘Business Surveys and Economic Activity’, RBA Research Discussion Paper No 2003-01.

Bai J, E Ghysels and JH Wright (2013), ‘State Space Models and MIDAS Regressions’, Econometric Reviews, 32(7), pp 779–813.

Bai J and K Li (2016), ‘Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension’, The Review of Economics and Statistics, 98(2), pp 298–309.

Bai J and S Ng (2008), ‘Forecasting Economic Time Series Using Targeted Predictors’, Journal of Econometrics, 146(2), pp 304–317.

Bai J and P Wang (2015), ‘Identification and Bayesian Estimation of Dynamic Factor Models’, Journal of Business & Economic Statistics, 33(2), pp 221–240.

Bair E, T Hastie, D Paul and R Tibshirani (2006), ‘Prediction by Supervised Principal Components’, Journal of the American Statistical Association, 101(473), pp 119–137.

Bańbura M, D Giannone, M Modugno and L Reichlin (2013), ‘Now-casting and the Real-time Data Flow’, in G Elliott and A Timmermann (eds), Handbook of Economic Forecasting: Volume 2A, Handbooks in Economics, North Holland, Amsterdam, pp 195–237.

Bańbura M and M Modugno (2014), ‘Maximum Likelihood Estimation of Factor Models on Datasets with Arbitrary Pattern of Missing Data’, Journal of Applied Econometrics, 29(1), pp 133–160.

Bańbura M and G Rünstler (2007), ‘A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP’, European Central Bank Working Paper Series No 751.

Barigozzi M and M Luciani (2019), ‘Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM Algorithm’, Unpublished manuscript, ver 1, 10 October. Available at <https://arxiv.org/abs/1910.03821v1>.

Baumeister C and P Guérin (2020), ‘A Comparison of Monthly Global Indicators for Forecasting Growth’, NBER Working Paper No 28014.

Boivin J and S Ng (2006), ‘Are More Data Always Better for Factor Analysis?’, Journal of Econometrics, 132(1), pp 169–194.

Bok B, D Caratelli, D Giannone, A Sbordone and A Tambalotti (2017), ‘Macroeconomic Nowcasting and Forecasting with Big Data’, Federal Reserve Bank of New York Staff Report No 830.

Brillinger DR (1981), Time Series: Data Analysis and Theory, Expanded edn, Holden-Day Series in Time Analysis, Holden-Day, San Francisco.

Bulligan G, M Marcellino and F Venditti (2015), ‘Forecasting Economic Activity with Targeted Predictors’, International Journal of Forecasting, 31(1), pp 188–206.

Chauvet M and S Potter (2013), ‘Forecasting Output’, in G Elliott and A Timmermann (eds), Handbook of Economic Forecasting: Volume 2A, Handbooks in Economics, North Holland, Amsterdam, pp 141–194.

Chinn MD, B Meunier and S Stumpner (2023), ‘Nowcasting World Trade with Machine Learning: A Three-step Approach’, NBER Working Paper No 31419.

Choi H and H Varian (2012), ‘Predicting the Present with Google Trends’, Economic Record, 88(s1), pp 2–9.

Clark TE and MW McCracken (2005), ‘Evaluating Direct Multistep Forecasts’, Econometric Reviews, 24(4), pp 369–404.

Clements MP and AB Galvão (2008), ‘Macroeconomic Forecasting with Mixed-frequency Data: Forecasting Output Growth in the United States’, Journal of Business & Economic Statistics, 26(4), pp 546–554.

Clements MP and AB Galvão (2009), ‘Forecasting US Output Growth Using Leading Indicators: An Appraisal Using MIDAS Models’, Journal of Applied Econometrics, 24(7), pp 1187–1206.

Cunningham A, J Eklund, C Jeffery, G Kapetanios and V Labhard (2012), ‘A State Space Approach to Extracting the Signal from Uncertain Data’, Journal of Business & Economic Statistics, 30(2), pp 173–180.

Doz C, D Giannone and L Reichlin (2011), ‘A Two-step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering’, Journal of Econometrics, 164(1), pp 188–205.

Doz C, D Giannone and L Reichlin (2012), ‘A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models’, The Review of Economics and Statistics, 94(4), pp 1014–1024.

Ferrara L and C Marsilli (2019), ‘Nowcasting Global Economic Growth: A Factor-augmented Mixed-frequency Approach’, The World Economy, 42(3), pp 846–875.

Forni M, M Hallin, M Lippi and L Reichlin (2000), ‘The Generalized Dynamic-factor Model: Identification and Estimation’, The Review of Economics and Statistics, 82(4), pp 540–554.

Foroni C and M Marcellino (2014), ‘A Comparison of Mixed Frequency Approaches for Nowcasting Euro Area Macroeconomic Aggregates’, International Journal of Forecasting, 30(3), pp 554–568.

Foroni C, M Marcellino and C Schumacher (2015), ‘Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials’, Journal of the Royal Statistical Society Series A: Statistics in Society, 178(1), pp 57-82.

Galvão AB (2013), ‘Changes in Predictive Ability with Mixed Frequency Data’, International Journal of Forecasting, 29(3), pp 395–410.

Galvão AB and M Lopresto (2020), ‘Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth Using a Mixed-frequency Bottom-up Approach’, Economic Statistics Centre of Excellence, ESCoE Discussion Paper No 2020-06.

Ghysels E, V Kvedaras and V Zemlys (2016), ‘Mixed Frequency Data Sampling Regression Models: The R Package midasr’, Journal of Statistical Software, 72(4), pp 1-35.

Ghysels E, P Santa-Clara and R Valkanov (2004), ‘The MIDAS Touch: Mixed Data Sampling Regression Models’, Centre Interuniversitaire de Recherche en Analyse des Organisations, Scientific Series, CIRANO Working Paper No 2004s-20.

Ghysels E, A Sinko and R Valkanov (2007), ‘MIDAS Regressions: Further Results and New Directions’, Econometric Reviews, 26(1), pp 53-90.

Giannone D, L Reichlin and D Small (2008), ‘Nowcasting: The Real-time Informational Content of Macroeconomic Data’, Journal of Monetary Economics, 55(4), pp 665-676.

Gillitzer C and J Kearns (2007), ‘Forecasting with Factors: The Accuracy of Timeliness’, RBA Research Discussion Paper No 2007-03.

Gillitzer C, J Kearns and A Richards (2005), ‘The Australian Business Cycle: A Coincident Indicator Approach’, RBA Research Discussion Paper No 2005-07.

Hallin M and R Liška (2007), ‘Determining the Number of Factors in the General Dynamic Factor Model’, Journal of the American Statistical Association, 102(478), pp 603-617.

Hartigan L and M Wright (2023), ‘Monitoring Financial Conditions and Downside Risk to Economic Activity in Australia’, Economic Record, 99(325), pp 253-287.

He C and T Rosewall (2020), ‘The Sahm Rule: A Recession Indicator for Australia’, Unpublished manuscript, Reserve Bank of Australia, 16 January.

Higgins P (2014), ‘GDPNow: A Model for GDP “Nowcasting”’, Federal Reserve Bank of Atlanta Working Paper 2014-7.

Jardet C and B Meunier (2022), ‘Nowcasting World GDP Growth with High-frequency Data’, Journal of Forecasting, 41(6), pp 1181-1200.

Kamber G, J Morley and B Wong (2018), ‘Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter’, The Review of Economics and Statistics, 100(3), pp 550-566.

Koenig EF, S Dolmas and J Piger (2003), ‘The Use and Abuse of Real-time Data in Economic Forecasting’, The Review of Economics and Statistics, 85(3), pp 618-628.

Leboeuf M and L Morel (2014), ‘Forecasting Short-term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions’, Bank of Canada Discussion Paper No 2014-3.

Lee K, N Olekalns, K Shields and Z Wang (2012), ‘Australian Real-time Database: An Overview and an Illustration of its Use in Business Cycle Analysis’, Economic Record, 88(283), pp 495–516.

Lewis DJ, K Mertens, JH Stock and M Trivedi (2021), ‘High-frequency Data and a Weekly Economic Index during the Pandemic’, in WR Johnson and G Herbert (eds), AEA Papers and Proceedings, Vol 111, American Economic Association, Nashville, pp 326–330.

Luciani M (2020), ‘Common and Idiosyncratic Inflation’, Board of Governors of the Federal Reserve System Finance and Economics Discussion Series No 2020-024.

Marcellino M and C Schumacher (2010), ‘Factor MIDAS for Nowcasting and Forecasting with Ragged-edge Data: A Model Comparison for German GDP’, Oxford Bulletin of Economics and Statistics, 72(4), pp 518–550.

Maroz D, JH Stock and MW Watson (2021), ‘Comovement of Economic Activity during the Covid Recession’, Bendeim Center for Finance Princeton University, Working Paper, 15 December. Available at <https://www.princeton.edu/~mwatson/wp.html>.

Matheson TD (2006), ‘Factor Model Forecasts for New Zealand’, International Journal of Central Banking, 2(2), pp 169–237.

McCracken MW (2007), ‘Asymptotics for Out of Sample Tests of Granger Causality’, Journal of Econometrics, 140(2), pp 719–752.

Mosley L, T-ST Chan and A Gibberd (2024), ‘The Sparse Dynamic Factor Model: A Regularised Quasi-maximum Likelihood Approach’, Statistics and Computing, 34, Article 68.

Nunes LC (2005), ‘Nowcasting Quarterly GDP Growth in a Monthly Coincident Indicator Model’, Journal of Forecasting, 24(8), pp 575–592.

Panagiotelis A, G Athanasopoulos, RJ Hyndman, B Jiang and F Vahid (2019), ‘Macroeconomic Forecasting for Australia Using a Large Number of Predictors’, International Journal of Forecasting, 35(2), pp 616–633.

Richardson A, T van Florenstein Mulder and T Vehbi (2021), ‘Nowcasting GDP Using Machine-learning Algorithms: A Real-time Assessment’, International Journal of Forecasting, 37(2), pp 941–948.

Roberts I and J Simon (2001), ‘What do Sentiment Surveys Measure?’, RBA Research Discussion Paper No 2001-09.

Sahm C (2019), ‘Direct Stimulus Payments to Individuals’, in H Bousheym, R Nunn and J Shambaugh (eds), Recession Ready: Fiscal Policies to Stabilize the American Economy, The Brookings Institution, Hamilton Project, Washington DC, pp 67–92.

Schorfheide F and D Song (2015), ‘Real-time Forecasting with a Mixed-frequency VAR’, Journal of Business & Economic Statistics, 33(3), pp 366–380.

Sheen J, S Trück and BZ Wang (2015), ‘Daily Business and External Condition Indices for the Australian Economy’, Economic Record, 91(S1), pp 38–53.

Siliverstovs B (2020), ‘Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts’, Empirical Economics, 58(1), pp 7–27.

Stock JH and MW Watson (2002), ‘Forecasting Using Principal Components from a Large Number of Predictors’, Journal of the American Statistical Association, 97(460), pp 1167–1179.

Stock JH and MW Watson (2016), ‘Dynamic Factor Models, Factor-augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics’, in JB Taylor and H Uhilg (eds), Handbook of Macroeconomics: Volume 2A, Handbooks in Economics, Elsevier, Amsterdam, pp 415–525.

Stone A and S Wardrop (2002), ‘Real-time National Accounts Data’, RBA Research Discussion Paper No 2002-05.

West KD (2006), ‘Forecast Evaluation’, in G Elliott, CWJ Granger and A Timmermann (eds), Handbook of Economic Forecasting: Volume 1, Handbooks in Economics 24, Elsevier, Amsterdam, pp 99–134.