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Mixing Frequencies: Stock Returns As A Predictor Of Real Output Growth, Anthony S. Tay
Mixing Frequencies: Stock Returns As A Predictor Of Real Output Growth, Anthony S. Tay
Research Collection School Of Economics
We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We find that our mixed frequency models perform well in forecasting real output growth.
Forecasting The Global Electronics Cycle With Leading Indicators: A Bayesian Var Approach, Hwee Kwan Chow, Keen Meng Choy
Forecasting The Global Electronics Cycle With Leading Indicators: A Bayesian Var Approach, Hwee Kwan Chow, Keen Meng Choy
Research Collection School Of Economics
Developments in the global electronics industry are typically monitored by tracking indicators that span a whole spectrum of activities in the sector. However, these indicators invariably give mixed signals at each point in time, thereby hampering attempts at prediction. In this paper, we propose a unified framework for forecasting the global electronics cycle by constructing a VAR model that captures the economic interactions between putative leading indicators representing expectations, orders, inventories and prices. The ability of the indicators to presage world semiconductor sales is first examined by Granger causality tests. Subsequently, an impulse response analysis confirms the leading qualities of …