LAUSR

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.