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Erschienen in: Empirical Economics 2/2023

09.01.2023

Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

verfasst von: Hwee Kwan Chow, Yijie Fei, Daniel Han

Erschienen in: Empirical Economics | Ausgabe 2/2023

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Abstract

This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We consider various popular weighting schemes in the literature when conducting forecast pooling. As for factor extraction, both the conventional dynamic factor model and the three-pass regression filter approach are considered. We investigate the relative predictive performance of all methods in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. In comparison, we find information pooling tends to dominate both the quarterly autoregressive benchmark model and the forecast pooling strategy particularly during the Global Financial Crisis.

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Literatur
Zurück zum Zitat Abeysinghe T (1998) Forecasting Singapore’s quarterly GDP with monthly external trade. Int J Forecast 14(4):505–513CrossRef Abeysinghe T (1998) Forecasting Singapore’s quarterly GDP with monthly external trade. Int J Forecast 14(4):505–513CrossRef
Zurück zum Zitat Andreou E, Ghysels E, Kourtellos A (2010) Regression models with mixed sampling frequencies. J Econom 158(2):246–261CrossRef Andreou E, Ghysels E, Kourtellos A (2010) Regression models with mixed sampling frequencies. J Econom 158(2):246–261CrossRef
Zurück zum Zitat Andreou E, Ghysels E, Kourtellos A (2013) Should macroeconomic forecasters use daily financial data and how? J Bus Econ Stat 31(2):240–251CrossRef Andreou E, Ghysels E, Kourtellos A (2013) Should macroeconomic forecasters use daily financial data and how? J Bus Econ Stat 31(2):240–251CrossRef
Zurück zum Zitat Armesto MT, Engemann KM, Owyang MT et al (2010) Forecasting with mixed frequencies. Federal Reserve Bank of St. Louis Review 92(6):521–536 Armesto MT, Engemann KM, Owyang MT et al (2010) Forecasting with mixed frequencies. Federal Reserve Bank of St. Louis Review 92(6):521–536
Zurück zum Zitat Babii A, Ghysels E, Striaukas J (2021) Machine learning time series regressions with an application to nowcasting. J Bus Econ Stat, pp 1–23 Babii A, Ghysels E, Striaukas J (2021) Machine learning time series regressions with an application to nowcasting. J Bus Econ Stat, pp 1–23
Zurück zum Zitat Bai J, Ng S (2008) Forecasting economic time series using targeted predictors. J Econom 146(2):304–317CrossRef Bai J, Ng S (2008) Forecasting economic time series using targeted predictors. J Econom 146(2):304–317CrossRef
Zurück zum Zitat Banerjee A, Marcellino M, Masten I (2005) Leading indicators for euro-area inflation and GDP growth. Oxford Bull Econ Stat 67:785–813CrossRef Banerjee A, Marcellino M, Masten I (2005) Leading indicators for euro-area inflation and GDP growth. Oxford Bull Econ Stat 67:785–813CrossRef
Zurück zum Zitat Bates JM, Granger CW (1969) The combination of forecasts. J Oper Res Soc 20(4):451–468CrossRef Bates JM, Granger CW (1969) The combination of forecasts. J Oper Res Soc 20(4):451–468CrossRef
Zurück zum Zitat Bec F, Mogliani M (2015) Nowcasting French GDP in real-time with surveys and “blocked’’ regressions: combining forecasts or pooling information? Int J Forecast 31(4):1021–1042CrossRef Bec F, Mogliani M (2015) Nowcasting French GDP in real-time with surveys and “blocked’’ regressions: combining forecasts or pooling information? Int J Forecast 31(4):1021–1042CrossRef
Zurück zum Zitat Boivin J, Ng S (2005) Understanding and comparing factor-based forecasts. Int J Central Bank 1(3) Boivin J, Ng S (2005) Understanding and comparing factor-based forecasts. Int J Central Bank 1(3)
Zurück zum Zitat Chow HK, Choy KM (2009) Analyzing and forecasting business cycles in a small open economy: a dynamic factor model for Singapore. OECD J: J Bus Cycle Meas Anal 2009(1):19–41 Chow HK, Choy KM (2009) Analyzing and forecasting business cycles in a small open economy: a dynamic factor model for Singapore. OECD J: J Bus Cycle Meas Anal 2009(1):19–41
Zurück zum Zitat Chow HK, Choy KM (2009) Monetary policy and asset prices in a small open economy: a factor-augmented VAR analysis for Singapore. Ann Financ Econ 5(01):0950004CrossRef Chow HK, Choy KM (2009) Monetary policy and asset prices in a small open economy: a factor-augmented VAR analysis for Singapore. Ann Financ Econ 5(01):0950004CrossRef
Zurück zum Zitat Clements MP, Galvão AB (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J Bus Econ Stat 26(4):546–554CrossRef Clements MP, Galvão AB (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J Bus Econ Stat 26(4):546–554CrossRef
Zurück zum Zitat Clements MP, Galvão AB (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J Appl Econom 24(7):1187–1206CrossRef Clements MP, Galvão AB (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models. J Appl Econom 24(7):1187–1206CrossRef
Zurück zum Zitat Coroneo L, Iacone F (2020) Comparing predictive accuracy in small samples using fixed-smoothing asymptotics. J Appl Econom 35(4):391–409CrossRef Coroneo L, Iacone F (2020) Comparing predictive accuracy in small samples using fixed-smoothing asymptotics. J Appl Econom 35(4):391–409CrossRef
Zurück zum Zitat den Reijer A, Johansson A (2019) Nowcasting Swedish GDP with a large and unbalanced data set. Empir Econ 57(4):1351–1373CrossRef den Reijer A, Johansson A (2019) Nowcasting Swedish GDP with a large and unbalanced data set. Empir Econ 57(4):1351–1373CrossRef
Zurück zum Zitat Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20(1):134–144CrossRef Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20(1):134–144CrossRef
Zurück zum Zitat Doz C, Giannone D, Reichlin L (2011) A two-step estimator for large approximate dynamic factor models based on Kalman filtering. J Econom 164(1):188–205CrossRef Doz C, Giannone D, Reichlin L (2011) A two-step estimator for large approximate dynamic factor models based on Kalman filtering. J Econom 164(1):188–205CrossRef
Zurück zum Zitat Forni M, Hallin M, Lippi M, Reichlin L (2003) Do financial variables help forecasting inflation and real activity in the euro area? J Monet Econ 50(6):1243–1255CrossRef Forni M, Hallin M, Lippi M, Reichlin L (2003) Do financial variables help forecasting inflation and real activity in the euro area? J Monet Econ 50(6):1243–1255CrossRef
Zurück zum Zitat Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471):830–840CrossRef Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471):830–840CrossRef
Zurück zum Zitat Foroni C, Marcellino M (2014) A comparison of mixed frequency approaches for nowcasting euro area macroeconomic aggregates. Int J Forecast 30(3):554–568CrossRef Foroni C, Marcellino M (2014) A comparison of mixed frequency approaches for nowcasting euro area macroeconomic aggregates. Int J Forecast 30(3):554–568CrossRef
Zurück zum Zitat Foroni C, Marcellino M, Schumacher C (2015) Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. J R Stat Soc A Stat Soc 178(1):57–82CrossRef Foroni C, Marcellino M, Schumacher C (2015) Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. J R Stat Soc A Stat Soc 178(1):57–82CrossRef
Zurück zum Zitat Fuentes J, Poncela P, Rodríguez J (2015) Sparse partial least squares in time series for macroeconomic forecasting. J Appl Econom 30(4):576–595CrossRef Fuentes J, Poncela P, Rodríguez J (2015) Sparse partial least squares in time series for macroeconomic forecasting. J Appl Econom 30(4):576–595CrossRef
Zurück zum Zitat Galli A, Hepenstrick C, Scheufele R (2019) Mixed-frequency models for tracking short-term economic developments in Switzerland. 58th issue (June 2019) of the International Journal of Central Banking Galli A, Hepenstrick C, Scheufele R (2019) Mixed-frequency models for tracking short-term economic developments in Switzerland. 58th issue (June 2019) of the International Journal of Central Banking
Zurück zum Zitat Ghysels E, Santa-Clara P, Valkanov R (2004). The MIDAS touch: mixed data sampling regression models Ghysels E, Santa-Clara P, Valkanov R (2004). The MIDAS touch: mixed data sampling regression models
Zurück zum Zitat Ghysels E, Sinko A, Valkanov R (2007) MIDAS regressions: further results and new directions. Economet Rev 26(1):53–90CrossRef Ghysels E, Sinko A, Valkanov R (2007) MIDAS regressions: further results and new directions. Economet Rev 26(1):53–90CrossRef
Zurück zum Zitat Harvey DI, Leybourne SJ, Whitehouse EJ (2017) Forecast evaluation tests and negative long-run variance estimates in small samples. Int J Forecast 33(4):833–847CrossRef Harvey DI, Leybourne SJ, Whitehouse EJ (2017) Forecast evaluation tests and negative long-run variance estimates in small samples. Int J Forecast 33(4):833–847CrossRef
Zurück zum Zitat Heinisch K, Scheufele R (2018) Bottom-up or direct? Forecasting German GDP in a data-rich environment. Empir Econ 54(2):705–745CrossRef Heinisch K, Scheufele R (2018) Bottom-up or direct? Forecasting German GDP in a data-rich environment. Empir Econ 54(2):705–745CrossRef
Zurück zum Zitat Hepenstrick C, Marcellino M (2019) Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three-pass regression filter. J R Stat Soc A Stat Soc 182(1):69–99CrossRef Hepenstrick C, Marcellino M (2019) Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three-pass regression filter. J R Stat Soc A Stat Soc 182(1):69–99CrossRef
Zurück zum Zitat Kelly B, Pruitt S (2015) The three-pass regression filter: a new approach to forecasting using many predictors. J Econom 186(2):294–316CrossRef Kelly B, Pruitt S (2015) The three-pass regression filter: a new approach to forecasting using many predictors. J Econom 186(2):294–316CrossRef
Zurück zum Zitat Kim HH, Swanson NR (2018) Methods for backcasting, nowcasting and forecasting using factor-MIDAS: with an application to Korean GDP. J Forecast 37(3):281–302CrossRef Kim HH, Swanson NR (2018) Methods for backcasting, nowcasting and forecasting using factor-MIDAS: with an application to Korean GDP. J Forecast 37(3):281–302CrossRef
Zurück zum Zitat Kuck K, Schweikert K (2021) Forecasting Baden–Württemberg’s GDP growth: MIDAS regressions versus dynamic mixed-frequency factor models. J Forecast 40(5):861–882CrossRef Kuck K, Schweikert K (2021) Forecasting Baden–Württemberg’s GDP growth: MIDAS regressions versus dynamic mixed-frequency factor models. J Forecast 40(5):861–882CrossRef
Zurück zum Zitat Kuzin V, Marcellino M, Schumacher C (2013) Pooling versus model selection for nowcasting GDP with many predictors: empirical evidence for six industrialized countries. J Appl Econom 28(3):392–411CrossRef Kuzin V, Marcellino M, Schumacher C (2013) Pooling versus model selection for nowcasting GDP with many predictors: empirical evidence for six industrialized countries. J Appl Econom 28(3):392–411CrossRef
Zurück zum Zitat Laine O-M, Lindblad A (2021) Nowcasting Finnish GDP growth using financial variables: a MIDAS approach. J Finnish Econ Assoc 2(1):74–108CrossRef Laine O-M, Lindblad A (2021) Nowcasting Finnish GDP growth using financial variables: a MIDAS approach. J Finnish Econ Assoc 2(1):74–108CrossRef
Zurück zum Zitat Marcellino M, Schumacher C (2010) Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxford Bull Econ Stat 72(4):518–550CrossRef Marcellino M, Schumacher C (2010) Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxford Bull Econ Stat 72(4):518–550CrossRef
Zurück zum Zitat Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Econom 135(1–2):499–526CrossRef Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Econom 135(1–2):499–526CrossRef
Zurück zum Zitat Marcellino M, Sivec V (2021) Nowcasting GDP growth in a small open economy. Natl Inst Econ Rev 256:127–161CrossRef Marcellino M, Sivec V (2021) Nowcasting GDP growth in a small open economy. Natl Inst Econ Rev 256:127–161CrossRef
Zurück zum Zitat Rusnák M (2016) Nowcasting Czech GDP in real time. Econ Model 54:26–39 Rusnák M (2016) Nowcasting Czech GDP in real time. Econ Model 54:26–39
Zurück zum Zitat Schumacher C, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398CrossRef Schumacher C, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398CrossRef
Zurück zum Zitat Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20(2):147–162CrossRef Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20(2):147–162CrossRef
Zurück zum Zitat Stock JH, Watson MW (2004) Combination forecasts of output growth in a seven-country data set. J Forecast 23(6):405–430CrossRef Stock JH, Watson MW (2004) Combination forecasts of output growth in a seven-country data set. J Forecast 23(6):405–430CrossRef
Zurück zum Zitat Tay AS (2007) Financial variables as predictors of real output growth Tay AS (2007) Financial variables as predictors of real output growth
Zurück zum Zitat Timmermann A (2006) Forecast combinations. Handb Econ Forecast 1:135–196 Timmermann A (2006) Forecast combinations. Handb Econ Forecast 1:135–196
Zurück zum Zitat Tsui AK, Xu CY, Zhang Z (2018) Macroeconomic forecasting with mixed data sampling frequencies: evidence from a small open economy. J Forecast 37(6):666–675CrossRef Tsui AK, Xu CY, Zhang Z (2018) Macroeconomic forecasting with mixed data sampling frequencies: evidence from a small open economy. J Forecast 37(6):666–675CrossRef
Zurück zum Zitat Uematsu Y, Tanaka S (2019) High-dimensional macroeconomic forecasting and variable selection via penalized regression. Econom J 22(1):34–56CrossRef Uematsu Y, Tanaka S (2019) High-dimensional macroeconomic forecasting and variable selection via penalized regression. Econom J 22(1):34–56CrossRef
Zurück zum Zitat Yau R, Hueng CJ (2019) Nowcasting GDP growth for small open economies with a mixed-frequency structural model. Comput Econ 54(1):177–198CrossRef Yau R, Hueng CJ (2019) Nowcasting GDP growth for small open economies with a mixed-frequency structural model. Comput Econ 54(1):177–198CrossRef
Metadaten
Titel
Forecasting GDP with many predictors in a small open economy: forecast or information pooling?
verfasst von
Hwee Kwan Chow
Yijie Fei
Daniel Han
Publikationsdatum
09.01.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 2/2023
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-022-02356-9

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