We explore the ability of traditional core inflation – consumer prices excluding food and energy – to predict headline CPI annual inflation. We analyze a sample of OECD and non-OECD economies using monthly data from January 1994 to March 2015. Our results indicate that sizable predictability...

This paper uses firm-level survey data matched with official tax records to estimate the unobserved true sales of formal firms in Mongolia. Taking into account firm-level incentives to comply with taxes and a production function technology linking unobserved true sales with observable firm-level...

We study whether stock market returns in oil-exporting countries can be predicted by oil price changes, and we investigate the link between predictability and the quality of each country's institutions. Returns are predictable for half the countries we consider, and predictability is stronger...

Despite several advantages of borrowing from Self Help Groups (SHGs), why do many enterprises in India, still rely on informal lenders (MoSPI, 2020)? To answer this question, we develop a novel enterprise-village matched dataset and use a variety of Machine Learning methods to predict the choice...

During times when the Chinese government wished to prop up the market, sell-side analysts from brokerages with significant government ownership issued relatively less pessimistic (or more optimistic) earnings forecasts, earnings-forecast revisions, and stock recommendations; they were also...

Due to the complexity of tax and the time and resources needed to monitor and examine tax returns, tax noncompliance is challenging to detect. Big data and sophisticated analytics might help tax authorities extract actionable data insights. Using income tax record data, this paper employs an...

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...