A Multiple-Equations Approach to Model-Based Forecasting

This chapter discusses the multiple-equations forecasting approach, which has more than one dependent variable along with several right-hand-side variables. Within the multiple-equations forecasting framework, we discuss vector autoregressions (VARs) and Bayesian vector autoregressions (BAVRs). The VAR/BVAR approaches can be utilized for short-term as well as long-term forecasting. Short-term forecasting is the focus of this chapter; we look at long-term forecasting in Chapter 12.

A specific forecasting approach in short-term forecasting is known as real-time forecasting of macroeconomic and financial variables. A real-time forecast implies forecasting before the actual release of a variable.1 Silvia and Iqbal (2012) developed an accurate real-time short-term, one-month-ahead, macroeconomic forecasting framework that accounts for the real-time challenge of data availability but also provides a more accurate forecast, on average, than those of the Bloomberg real-time consensus forecast.2 They compared their real-time forecasts with the Bloomberg real-time consensus for 20 major macroeconomic variables, including nonfarm payrolls, unemployment rate, Institute for Supply Management manufacturing index, consumer price index (CPI), industrial production, and housing starts. In this chapter, we follow the approach developed by Silvia and Iqbal (2012).

This chapter sheds light on four important areas of real-time macroeconomic ...

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