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Forecasting Average Weekly Earnings
Over the past three decades, wages and prices have experienced several shocks that economists largely failed to forecast. This box analysis describe the various approaches to forecasting wage growth designed to enhance the NIESR monthly wage growth tracker.
The simplest forecasting model is the Auto-Regressive Integrated Moving Average (ARIMA) model. It uses information from past observations to forecast future observations. The risks of using an ARIMA model for forecasting include potential inaccuracies due to its reliance solely on historical data, which may not account for sudden changes or external factors affecting the series.
A Vector Auto-Regression (VAR) model can capture the dynamic relationship between multiple time series variables without requiring extensive prior knowledge about the underlying structural relationship.
A Vector Error Correction Model (VECM), as used by the Bank of England, relies heavily on the expected long-run relationship between macroeconomic variables, known as the cointegrating equation, and thus requires a theoretical basis for the specification of the model. It captures the long-term equilibrium relationship between the variables and forms the focal point to which the variable of interest adjusts given short-term deviations from the long-term equilibrium.
When evaluating the models it can be concluded that the ARIMA model performs exceedingly well, suggesting the most parsimonious of all models can reap great accuracy rewards. But, incorporating cointegrating equations into the current NIESR VAR could improve the accuracy of the current VAR and additionally quantify uncertainty around each point forecast.
The simplest forecasting model is the Auto-Regressive Integrated Moving Average (ARIMA) model. It uses information from past observations to forecast future observations. The risks of using an ARIMA model for forecasting include potential inaccuracies due to its reliance solely on historical data, which may not account for sudden changes or external factors affecting the series.
A Vector Auto-Regression (VAR) model can capture the dynamic relationship between multiple time series variables without requiring extensive prior knowledge about the underlying structural relationship.
A Vector Error Correction Model (VECM), as used by the Bank of England, relies heavily on the expected long-run relationship between macroeconomic variables, known as the cointegrating equation, and thus requires a theoretical basis for the specification of the model. It captures the long-term equilibrium relationship between the variables and forms the focal point to which the variable of interest adjusts given short-term deviations from the long-term equilibrium.
When evaluating the models it can be concluded that the ARIMA model performs exceedingly well, suggesting the most parsimonious of all models can reap great accuracy rewards. But, incorporating cointegrating equations into the current NIESR VAR could improve the accuracy of the current VAR and additionally quantify uncertainty around each point forecast.
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