Are Forecast Updates Progressive?

Chang, Chia-Lin and Franses, Philip Hans and McAleer, Michael (2013): Are Forecast Updates Progressive?

Abstract

Many macroeconomic forecasts and forecast updates like those from IMF and OECD typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether macroeconomic forecast updates are progressive, where the interaction between model and intuition is explicitly taken into account. The data set for the empirical analysis is for Taiwan, where we have three decades of quarterly data available of forecasts and their updates of the inflation rate and real GDP growth rate. Our empirical results suggest that the forecast updates for Taiwan are progressive, and that progress can be explained predominantly by improved intuition.

• Item Type: MPRA Paper
• Language: English
• Keywords: Macroeconomic forecasts, econometric models, intuition, progressive forecast updates, forecast errors
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Bunn, D.W. and A.A. Salo (1996), Adjustment of forecasts with model consistent expectations, International Journal of Forecasting, 12, 163-170.

Chang, C.-L., P.H. Franses and M. McAleer (2011), How accurate are government forecasts of economic fundamentals? The case of Taiwan, International Journal of Forecasting, 27(4), 1066-1075.

Clark, T.E. and M.W. McCracken (2001), Tests of equal forecast accuracy and encompassing for nested models, Journal of Econometrics, 105, 85-110.

Fiebig, D.G., M. McAleer and R. Bartels (1992), Properties of ordinary least squares estimators in regression models with non-spherical disturbances, Journal of Econometrics, 54, 321-334.

Franses, P.H., M. McAleer and R. Legerstee (2009), Expert opinion versus expertise in forecasting, Statistica Neerlandica, 63, 334-346.

McAleer, M. (1992), Efficient estimation: the Rao-Zyskind condition, Kruskal's theorem and ordinary least squares, Economic Record, 68, 65-72.

McAleer, M. and C. McKenzie (1991), When are two step estimators efficient?, Econometric Reviews, 10, 235-252.

Oxley, L. and M. McAleer (1993), Econometric issues in macroeconomic models with generated regressors, Journal of Economic Surveys, 7, 1-40.

Pagan, A.R. (1984), Econometric issues in the analysis of regressions with generated regressors, International Economic Review, 25, 221-247.

Smith, J. and M. McAleer (1994), Newey-West covariance matrix estimates for models with generated regressors, Applied Economics, 26, 635-640.

Welch, B.L. (1951). On the comparison of several mean values: An alternative approach, Biometrika, 38, 330-336.