332
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Rationality and information advantage of the Japanese government’s construction forecasts over economic development

Pages 671-694 | Received 29 Dec 2022, Accepted 25 Jan 2024, Published online: 10 Feb 2024

References

  • Abdel-Wahab, M., and Vogl, B., 2011. Trends of productivity growth in the construction industry across Europe, US and Japan. Construction management and economics, 29 (6), 635–644.
  • Arai, N., 2020. Investigating the inefficiency of the CBO’s budgetary projections. International journal of forecasting, 36 (4), 1290–1300.
  • Beaudry, P., and Willems, T., 2022. On the macroeconomic consequences of over-optimism. American economic journal: macroeconomics, 14 (1), 38–59.
  • Clark, T.E., and McCracken, M.W., 2001. Tests of equal forecast accuracy and encompassing for nested models. Journal of econometrics, 105 (1), 85–110.
  • Coibion, O., and Gorodnichenko, Y., 2012. What can survey forecasts tell us about information rigidities? Journal of political economy, 120 (1), 116–159.
  • Diebold, F.X., and Mariano, R.S., 1995. Comparing predictive accuracy. Journal of business and economic statistics, 13 (3), 253–263.
  • Ericsson, N.R., 2017. How biased are US government forecasts of the federal debt? International journal of forecasting, 33 (2), 543–559.
  • Estévez-Abe, M., 2008. Welfare and capitalism in postwar Japan. Cambridge: Cambridge University Press.
  • EU-Japan Centre. 2015. Sustainable building and construction sector in Japan: analysis of opportunities for European firms. Tokyo: Centre for Industrial Cooperation.
  • Fan, R.Y.C., Ng, S.T., and Wong, J.M.W., 2010. Reliability of the Box–Jenkins model for forecasting construction demand covering times of economic austerity. Construction management and economics, 28 (3), 241–254.
  • Fan, R.Y.C., Ng, S.T., and Wong, J.M.W., 2011. Predicting construction market growth for urban metropolis: an econometric analysis. Habitat international, 35 (2), 167–174.
  • Frankel, J., 2011. Over-optimism in forecasts by official budget agencies and its implications. Oxford review of economic policy, 27 (4), 536–562.
  • Frankel, J., and Schreger, J., 2013. Over-optimistic official forecasts and fiscal rules in the Eurozone. Review of world economics, 149 (2), 247–272.
  • Goh, B.H., 2005. The dynamic effects of the Asian financial crisis on construction demand and tender price levels in Singapore. Building and environment, 40 (2), 267–276.
  • Hua, G.B., 1996. Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression. Construction management and economics, 14 (1), 25–34.
  • Hua, G.B., and Pin, T.H., 2000. Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach. Construction management and economics, 18 (5), 607–618.
  • Ito, T., and Hoshi, T., 2020. The Japanese economy. Cambridge, MA: MIT Press.
  • Jiang, H., and Liu, C., 2011. Forecasting construction demand: a vector error correction model with dummy variables. Construction management and economics, 29 (9), 969–979.
  • Jiang, H., Xu, Y., and Liu, C., 2014. Market effects on forecasting construction prices using vector error correction models. International journal of construction management, 14 (2), 101–112.
  • Kim, K.-B., Cho, J.-H., and Kim, S.-B., 2021. Model-based dynamic forecasting for residential construction market demand: a systemic approach. Applied Sciences, 11 (8), 3681.
  • Kissi, E., et al., 2018. Forecasting construction tender price index in Ghana using autoregressive integrated moving average with exogenous variables model. Construction economics and building, 18 (1), 70–82.
  • Lei, M., et al., 2023. Application of GSM-SVM for forecasting construction output: a case study of Hubei Province. Buildings, 13 (1), 48.
  • Lunsford, K.G., 2015. Forecasting residential investment in the United States. International journal of forecasting, 31 (2), 276–285.
  • Martinez, A.B., 2015. How good are US government forecasts of the federal debt? International journal of forecasting, 31 (2), 312–324.
  • Mincer, J. A., and Zarnowitz, V., 1969. The evaluation of economic forecasts. In Economic forecasts and expectations: analysis of forecasting behavior and performance. Cambridge, MA: National Bureau of Economic Research, 3–46.
  • Ministry of Land, Infrastructure, Transport and Tourism. 1998. Construction statistics guidebook. Ministry of Land, Infrastructure, Transport and Tourism. Available from https://www.mlit.go.jp/toukeijouhou/chojou/csg/contents.htm
  • Ministry of Land, Infrastructure, Transport and Tourism. 2021. Construction investment outlook for fiscal year 2021. Tokyo, Japan: Ministry of Land, Infrastructure, Transport and Tourism (in Japanese).
  • Miura, F., Takasaki, K., & Ogawa, K. 2021. Impact of the sharp decline in construction investment on the management of the construction industry. In. Construction Economic Report. No. 73. Research Institute of Construction and Economy (in Japanese), 162–192.
  • Neale, R., and Ameen, J., 2001. Discussion of “earthmoving productivity estimation using linear regression techniques” by Richard Neale and Jamal Ameen. Journal of construction engineering and management, 127 (1), 88–89.
  • Newey, W.K., and West, K.D., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55 (3), 703–708.
  • Ofori, G., 1990. The construction industry: aspects of its economics and management. Singapore: NUS Press.
  • Ord, K., Fildes, R. A., and Kourentzes, N., 2017. Principles of business forecasting. 2nd ed. New York: Wessex Press, A1.
  • Oshodi, O., et al., 2020. Construction output modelling: a systematic review. Engineering, construction and architectural management, 27 (10), 2959–2991.
  • Oteng-Abayie, E.F., and Dramani, J.B., 2019. Time-frequency domain causality of prime building cost and macroeconomic indicators in Ghana: implications for project selection. Construction management and economics, 37 (5), 243–256.
  • Rossi, B., and Sekhposyan, T., 2016. Forecast rationality tests in the presence of instabilities, with applications to Federal Reserve and survey forecasts. Journal of applied econometrics, 31 (3), 507–532.
  • Stock, J.H., and Watson, M.W., 1996. Evidence on structural instability in macroeconomic time series relations. Journal of business and economic statistics, 14 (1), 11–30.
  • Tan, Y., et al., 2015. Grey forecasting of construction demand in Hong Kong over the next ten years. International journal of construction management, 15 (3), 219–228.
  • Tsuchiya, Y., 2016. Assessing macroeconomic forecasts for Japan under an asymmetric loss function. International journal of forecasting, 32 (2), 233–242.
  • West, K.D., and McCracken, M.W., 1998. Regression-based tests of predictive ability. International economic review, 39 (4), 817–840.
  • Wong, J.M.W., and Ng, S.T., 2010. Forecasting construction tender price index in Hong Kong using vector error correction model. Construction management and economics, 28 (12), 1255–1268.
  • Wong, J.M.W., Chan, A.P.C., and Chiang, Y.H., 2007. Forecasting construction manpower demand: a vector error correction model. Building and environment, 42 (8), 3030–3041.
  • Woodall, B., 1996. Japan under construction: corruption, politics, and public works. Berkeley, CA: University of California Press.