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Empirical Economics

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06-08-2019 | Original Article

State-dependent biases and the quality of China’s preliminary GDP announcements

Author: Lixiong Yang

Published in: Empirical Economics | Issue 6/2020

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Abstract

This paper investigates whether and how the systematic forecast errors of the quarterly GDP announcements in China depend on the state of the economy. Our contribution is both theoretical and empirical. On the theoretical side, we extend the predictive threshold regression of Gonzalo and Pitarakis (J Bus Econ Stat 35:202–217, 2017) by incorporating a time-varying and state-dependent threshold, which is a function of macroeconomic variables that affect the separation of regimes. On the empirical side, we apply our model to assess the quality of China’s preliminary GDP data. Our empirical results show that there exist forecast biases in the preliminary GDP data conditional on the state of the economy. Our results also lean toward supporting that there exist behavioral biases of underestimation and over-reaction to new information during periods of relatively good state. These results suggest some scope to improve the accuracy of the preliminary GDP data based purely on econometric models.

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In terms of a time-varying threshold, to the best of our knowledge, only special cases have appeared in the literature so far. Dueker et al. (2013) propose a smooth transition autoregressive (STAR) model with a time-varying/state-dependent threshold and apply the model to the dynamics of US short-term interest rates. Yang and Su (2018) propose a regression kink with a time-varying threshold and apply the model to the growth-debt nexus.

In Appendix, our Monte Carlo simulations show that ignoring the time-varying nature of a threshold can lead to seriously biased estimates. Also, the power of the test for threshold effect would decrease with the inclusion of a time-varying threshold.

Here, we focus on the estimation of model (3), as model (4) can be estimated using the same procedure.

We thank an anonymous referee to raise this point to us.

In Appendix, a number of simulation experiments are conducted to examine the performance of the proposed bootstrap method. The simulation results show that this bootstrap procedure works well.

The sector classification is in accordance with the “Sector Classification of the National Economy”, which is available online at the NBS website (http://www.stats.gov.cn/tjsj/tjbz/hyflbz/). According to the sectoral classification system, the primary sector (i.e., agriculture) comprises farming, forestry, animal husbandry and fisheries; the secondary sector comprises industry and construction; and other subsectors compose the tertiary sector.

As noted by Cashin et al. (2017), China’s real GDP growth slowed from an average of about 10% over the period 1980–2013 to 7% between 2014 and 2016.

The optimal threshold setting is not pursued in this paper, as the relevant literature is scant and indirect. A further investigation of this issue is worthwhile.

To save space, we only report the results with the block size \(b=4\). In an unreported appendix, we show that the empirical results are robust to the choice of block size.

In an unreported appendix, we show that the empirical results are robust to the choice of block size.

We thank the anonymous referee to raise this point to us.

Due to data availability, we cannot compute the past four-quarter growth average for the period 2000Q1–2001Q4, as the preliminary GDP data over the period 1998Q1–1999Q4 are not available. Hence, the model (14) is estimated based on the sample over the period 2002Q1–2016Q4.

We thank an anonymous referee to raise this point to us.

This linear model is estimated based on the sample over the period 2002Q1–2016Q4.

Andrews DW, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62(6):1383–1414CrossRef

Cashin P, Mohaddes K, Raissi M (2017) China’s slowdown and global financial market volatility: Is world growth losing out? Emerg Mark Rev 31(63):164–175CrossRef

Davies RB (1977) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64(2):247–254CrossRef

Davies RB (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74(1):33–43

Djogbenou A, Goncalves Sílvia, Perron B (2015) Bootstrap inference in regressions with estimated factors and serial correlation. J Time Ser Anal 36(3):481–502CrossRef

Dueker MJ, Psaradakis Z, Sola M, Spagnolo F (2013) State-dependent threshold smooth transition autoregressive models. Oxf Bull Econ Stat 75(6):835–854CrossRef

Gali J (2002) New perspectives on monetary policy, inflation, and the business cycle. NBER working paper series, No. 8767, National Bureau of Economic Research. https://www.nber.org/papers/w8767

Gonzalo J, Pitarakis JY (2017) Inferring the predictability induced by a persistent regressor in a predictive threshold model. J Bus Econ Stat 35(2):202–217CrossRef

Hansen BE (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64(2):413–430CrossRef

Hansen BE (1999) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econom 93(2):345–368CrossRef

Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603CrossRef

Hansen BE (2017) Regression kink with an unknown threshold. J Bus Econ Stat 35(2):228–240CrossRef

Hanson MS, Whitehorn J (2006) Reconsidering the optimality of Federal Reserve forecasts. Wesleyan University, Middletown

Holden K, Peel DA (1990) On testing for unbiasedness and efficiency of forecasts*. Manch School 58(2):120–127CrossRef

Joutz F, Stekler HO (2000) An evaluation of the predictions of the Federal Reserve. Int J Forecast 16(1):17–38CrossRef

Lyu C, Wang K, Zhang F, Zhang X (2018) GDP management to meet or beat growth targets. J Account Econ 66(1):318–338CrossRef

Messina JD, Sinclair TM, Stekler H (2015) What can we learn from revisions to the Greenbook forecasts? J Macroecon 45:54–62CrossRef

Mincer JA, Zarnowitz V (1969) The evaluation of economic forecasts. In: Economic forecasts and expectations: analysis of forecasting behavior and performance. NBER, pp 3–46

Newey WK, West KD (1994) Automatic lag selection in covariance matrix estimation. Rev Econ Stud 61(4):631–653CrossRef

Nordhaus WD (1987) Forecasting efficiency: concepts and applications. Rev Econ Stat 69(3):667–674CrossRef

Seo HM (2008) Unit root test in a threshold autoregression: asymptotic theory and residual-based block bootstrap. Econom Theory 24(06):1699CrossRef

Shao X (2011) A bootstrap-assisted spectral test of white noise under unknown dependence. J Econom 162(2):213–224CrossRef

Sinclair TM, Stekler HO (2013) Examining the quality of early GDP component estimates. Int J Forecast 29(4):736–750CrossRef

Sinclair TM, Stekler HO, Carnow W (2015) Evaluating a vector of the Fed’s forecasts. Int J Forecast 31(1):157–164CrossRef

Swanson NR, Van Dijk D (2006) Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry. J Bus Econ Stat 24(1):24–42CrossRef

Xie Z, Hsu SH (2016) Time varying biases and the state of the economy. Int J Forecast 32(3):716–725CrossRef

Yang L (2017) An assessment on the quality of China’s preliminary data of quarterly GDP announcements. Appl Econ 49(54):5558–5569CrossRef

Yang L, Su JJ (2018) Debt and growth: Is there a constant tipping point? J Int Money Finance 87:133–143CrossRef

Title: State-dependent biases and the quality of China’s preliminary GDP announcements
Author: Lixiong Yang
Publication date: 06-08-2019
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 6/2020
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01751-z

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