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This study examines the lead-lag effect between the Chinese and US purchasing managers? index (PMI), and the West Texas Intermediate (WTI) crude oil prices for the period from January 2007 to April 2017 by adopting the wavelet theory model. The results show that oil prices were affected by the Chinese PMI in the long term after 2013, when China became the largest crude oil importer worldwide, bur supplies were greater than demand. In contrast, while the US PMI affected oil prices between 2008 and 2012, its dependence on foreign crude oil supplies declined, and thus its imports, afterwards. These results reveal crucial policy implications for both China and the United States. It also shows a structural change in oil prices and its role in the market between China and the United States.

Numerous studies have been conducted, globally and locally, on the impact of the exchange rate on economic growth. In the local context, only a handful of research have investigated this area of study to determine the extent to which the Purchasing Managers’ Index influence economic growth with the exchange rate, with limited research have been performed in Sri Lanka. This study explores the impact of exchange rate and Purchasing Managers’ Index on economic growth. Consequently, adopting an applied research methodology, the present study was based on secondary data published quarterly by the Central Bank of Sri Lanka reports and the Department of Census and Statistics of Sri Lanka from 2015 to 2021. The Vector autoregression model and Granger Causality Wald test were performed in this study. The empirical findings highlighted that economic growth and Purchasing Managers’ Index have a significant negative impact on the economic growth, while the exchange rate had a significant positive impact on the economic growth. Furthermore, the exchange rate and the Purchasing Managers’ Index did not help to predict the exchange rate. The implications of the study demonstrate the relevance of the exchange rate and manufacturing Purchasing Managers’ Index as indicators of changes in overall economic growth activities at the macro level. The findings will assist the Sri Lankan Government, policymakers, and foreign investors for effective decision making.

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

A structural break in the GDP growth—PMI relationship occurred in 2004Q1. The break is likely the result of a secular slowdown in average GDP growth. PMI-based forecasts of GDP growth that ignore the break are biased. Modeling the break eliminates forecast bias, reduces root mean square forecast error, and significantly increases the signaling power of the PMI.