Franses, Philip Hans and McAleer, Michael and Legerstee, Rianne (2012) Evaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments. [ Documentos de Trabajo del Instituto Complutense de Anaálisis Económico (ICAE); nº 14, 2012, ] (Unpublished)
Available under License Creative Commons Attribution Non-commercial.
Official URL: http://eprints.ucm.es/15603/
Macroeconomic forecasts are frequently produced, widely published, intensively discussed and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.
|Item Type:||Working Paper or Technical Report|
JEL Classifications: C22, C51, C52, C53, E27, E37.
|Uncontrolled Keywords:||Macroeconomic forecasts, Econometric models, Human intuition, Biased forecasts, Forecast performance, Forecast evaluation, Forecast comparison.|
|Subjects:||Social sciences > Economics > Econometrics|
Social sciences > Economics > Macroeconomics
|Series Name:||Documentos de Trabajo del Instituto Complutense de Anaálisis Económico (ICAE)|
Batchelor, R. (2007), Bias in Macroeconomic Forecasts, International Journal of Forecasting, 23, 189-203.
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, 1066-1075.
Chong, Y.Y. and D.F. Hendry (1986), Econometric Evaluation of Linear Macro-Economic Models, Review of Economic Studies, 53, 671-690.
Clark, T.E. and M.W. McCracken (2001), Tests of Equal Forecast Accuracy and Encompassing for Nested Models, Journal of Econometrics, 105, 85-110.
Clements, M.P. and D.F. Hendry (2002) (eds.), A Companion to Economic Forecasting, Oxford: Blackwell.
Diebold, F.X. and R.S. Mariano (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263.
Elliott, G. and A. Timmermann (2008), Economic Forecasting, Journal of Economic Literature, 46, 3-56.
Eroglu, C. and K.L. Croxton (2010), Biases in Judgmental Adjustments of Statistical Forecasts: The Role of Individual Differences, International Journal of Forecasting, 26, 116-133.
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.
Fildes, R, P. Goodwin, M. Lawrence, and K. Nikolopoulos (2009), Effective Forecasting and Judgemental Adjustments: An Empirical Evaluation and Strategies for Improvement in Supply-Chain Planning, International Journal of Forecasting, 25, 3-23.
Franses, P.H., H. Kranendonk, and D. Lanser (2011), One Model and Various Experts: Evaluating Dutch Macroeconomic Forecasts, International Journal of Forecasting, 27, 482-495.
Franses, P.H. and R. Legerstee (2009), Properties of Expert Adjustments on Model-Based SKU-level Forecasts, International Journal of Forecasting, 25, 35-47.
Franses, P.H., M. McAleer and R. Legerstee (2009), Expert Opinion Versus Expertise in Forecasting, Statistica Neerlandica, 63, 334-346.
Granger, C.W.J. and P. Newbold (1986), Forecasting Economic Time Series, San Diego: Academic Press.
Heij, C., D.J.C. van Dijk and P.J.F. Groenen (2011), Real-time Macroeconomic Forecasting with Leading Indicators: An Empirical Comparison, International Journal of Forecasting, 27, 466-481.
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.R. McKenzie (1991), When Are Two Step Estimators Efficient?, Econometric Reviews, 10, 235-252.
Romer, C.D. and D.H. Romer (2004), A New Measure of Monetary Shocks: Derivation and Implications, American Economic Review, 94, 1055-1084.
Romer, C.D. and D.H. Romer (2008), The FOMC Versus the Staff: Where Can Monetary Policymakers Add Value?, American Economic Review: Papers & Proceedings, 98, 230-235.
Smith, J. and M. McAleer (1994), Newey-West Covariance Matrix Estimates for Models with Generated Regressors, Applied Economics, 26, 635-640.
Stock, J.H. and M.W. Watson (2003), Forecasting Output abd Inflation: The Role of Asset Prices, Journal of Economic Literature, 41, 788-829.
Timmermann, A. (2006), Forecast Combinations, in G. Elliott, C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North Holland.
West, K.D. (1996), Asymptotic Inference About Predictive Ability, Econometrica, 64, 1067-1084.
|Deposited On:||12 Jun 2012 13:56|
|Last Modified:||06 Feb 2014 10:28|
Repository Staff Only: item control page