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Sir Clive W.J. Granger Memorial Special Issue on Econometrics Sir Clive W.J. Granger's Contributions to Forecasting

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Abstract (2. Language): 
Some of Clive Granger's many and varied contributions to economic forecasting are reviewed. These include contributions to forecast combination and forecast efficiency, to improving forecast practice, to forecast evaluation, and to the theory of forecasting. We also discuss some of the subsequent research and developments in these areas, which have sought to generalize the applicability of Granger's work. We also consider research in related areas motivated at least in part by Granger's work.
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References: 

[1] M. Aiolfi, C. Capistran, and A. Timmermann. Forecast combinations, chapter 11. In M. P. Clements and D. F. Hendry, editors, The Oxford Handbook of Economic Forecasting, pages 355-388. Oxford University Press, 2011.
[2] M. J. Andrews, A. P. L. Minford, and J. Riley. On comparing macroeconomic fore¬casts using forecast encompassing tests. Oxford Bulletin ofEconomics and Statistics, 58:279-305, 1996.
[3] M. Artis and M. Marcellino. Fiscal forecasting: the track record of the IMF, OECD and EC. Econometrics Journal, 4:S20-S36, 2001.
[4] G. A. Barnard. New methods of quality control. Journal of the Royal Statistical
Society, A, 126:255-259, 1963.
[5] J. M. Bates and C. W. J. Granger. The combination of forecasts. Operations Research Quarterly, 20:451-468, 1969. Reprinted in T.C. Mills (ed.), Economic Forecasting.
Edward Elgar, 1999.
[6] T Bollerslev. Generalised autoregressive conditional heteroskedasticity. Journal of
Econometrics, 51:307-327, 1986.
[7] G. E. P. Box and G. M. Jenkins. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, 1970.
[8] C. Capistran and A. Timmermann. Disagreement and biases in inflation expecta¬tions. Journal ofMoney, Credit and Banking, 41:365-396, 2009.
[9] J. L. Castle, M. P. Clements, and D. F. Hendry. Robust approaches to forecasting. International Journal ofForecasting, 31:99-112, 2015.
[10] J. L. Castle, M. P. Clements, and D. F. Hendry. An overview of forecasting facing breaks. Journal of Business Cycle Research, 12(1):3-23, 2016. DOI 10.1007/s41549-
016-0005-2.
[11] Y. Y. Chong and D. F. Hendry. Econometric evaluation of linear macro-economic models. Review of Economic Studies, 53:671-690, 1986. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
REFERENCES 51 [12] P. F. Christoffersen and F. X. Diebold. Optimal prediction under asymmetric loss.
Econometric Theory, 13:808-817, 1997.
[13] R. T. Clemen and R. L. Winkler. Combining economic forecasts. Journal ofBusiness and Economic Statistics, 4:39-46, 1986.
[14] M. P. Clements. Evaluating the Bank of England density forecasts of inflation. Economic Journal, 114:844 - 866, 2004.
[15] M. P. Clements. Internal consistency of survey respondents' forecasts: Evidence based on the Survey of Professional Forecasters. In J. L. Castle and N. Shephard, editors, The Methodology and Practice ofEconometrics. A Festschrift in Honour of David F. Hendry. Chapter 8, pages 206-226. Oxford University Press, Oxford, 2009.
[16] M. P. Clements. US inflation expectations and heterogeneous loss functions, 1968¬2010. Journal of Forecasting, 33(1):1-14, 2014.
[17] M. P. Clements. Assessing macro uncertainty in real-time when data are subject to revision. Journal ofBusiness & Economic Statistics, 2015. Forthcoming.
[18] M. P. Clements and A. B. Galvao. Combining predictors and combining information in modelling: Forecasting US recession probabilities and output growth. In C. Milas, P. Rothman, and D. van Dijk, editors, Nonlinear Time Series Analysis ofBusiness Cycles. Contributions to Economic Analysis Series, pages 55-73. Elsevier, 2006.
[19] M. P. Clements and D. I. Harvey. Forecasting combination and encompassing. In T. C. Mills and K. Patterson, editors, Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics, pages 169-198. Palgrave MacMillan, 2009.
[20] M. P. Clements and D. I. Harvey. Forecast encompassing tests and probability forecasts. Journal ofApplied Econometrics, 25:1028-1062, 2010.
[21] M. P. Clements and D. F. Hendry. Forecasting in cointegrated systems. Journal of Applied Econometrics, 10:127-146, 1995. Reprinted in T.C. Mills (ed.), Economic Forecasting. Edward Elgar, 1999.
[22] M. P. Clements and D. F. Hendry. Intercept corrections and structural change. Journal ofApplied Econometrics, 11:475-494, 1996.
[23] M. P. Clements and D. F. Hendry. Forecasting Economic Time Series. Cambridge University Press, Cambridge, 1998. The Marshall Lectures on Economic Forecasting.
[24] M. P. Clements and D. F. Hendry. Forecasting Non-stationary Economic Time Series. MIT Press, Cambridge, Mass., 1999.
[25] M. P. Clements and D. F. Hendry. Guest Editors' introduction: Information in economic forecasting. Oxford Bulletin ofEconomics and Statistics, 67:713-753, 2005.
REFERENCES 52
[26] M. P. Clements and D. F. Hendry. Forecasting with breaks. In G. Elliott, C. W. J Granger, and A Timmermann, editors, Handbook ofEconomic Forecasting, Volume 1. Handbook of Economics 24, pages 605-657. Elsevier, Horth-Holland, 2006.
[27] N. F. Coulson and R. P. Robins. Forecast combination in a dynamic setting. Journal ofForecasting, 12:63-68, 1993.
[28] D. Croushore. Forecasting with real-time data vintages, chapter 9. In M. P. Clements and D. F. Hendry, editors, The Oxford Handbook of Economic Forecasting, pages
247-267. Oxford University Press, 2011.
[29] D. Croushore. Frontiers of real-time data analysis. Journal ofEconomic Literature,
49:72-100, 2011.
[30] D. Croushore and T. Stark. A real-time data set for macroeconomists. Journal of
Econometrics, 105(1):111-130, 2001.
[31] C. De Mol, D. Giannone, and L. Reichlin. Forecasting using a large number of pre¬dictors: Is bayesian shrinkage a valid alternative to principal components? Journal of Econometrics, 146(2):318-328, 2008.
[32] M. Deutsch, C. W. J. Granger, and T. Terasvirta. The combination of forecasts using changing weights. International Journal ofForecasting, 10:47-57, 1994.
[33] F. X. Diebold. Serial correlation and the combination of forecasts. Journal of Business & Economic Statistics, 6:105-111, 1988.
[34] F. X. Diebold and R. Pauly. Structural change and the combination of forecasts. Journal ofForecasting, 6:21-40, 1987.
[35] F. X. Diebold and R. Pauly. The use of prior information in forecast combination. International Journal ofForecasting, 6:503-508, 1990.
[36] T. Doan, R. Litterman, and C. A. Sims. Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3:1-100, 1984.
[37] R. G. Donaldson and M. Kamstra. Forecast combining with neural networks. Journal ofForecasting, 15:49-61, 1996.
[38] G. Elliott. Forecasting with trending data. In G. Elliott, C. W. J Granger, and A Timmermann, editors, Handbook of Economic Forecasting, Volume 1. Handbook ofEconomics 24, pages 555-604. Elsevier, Horth-Holland, 2006.
[39] G. Elliott, I. Komunjer, and A. Timmermann. Estimation and testing of forecast rationality under flexible loss. Review of Economic Studies, 72:1107-1125, 2005.
[40] G. Elliott, I. Komunjer, and A. Timmermann. Biases in macroeconomic forecasts: Irrationality or asymmetric loss. Journal of the European Economic Association,
6:122-157, 2008.
REFERENCES 53
[41] G. Elliott and A. Timmermann. Optimal forecast combinations under general loss functions and forecast error distributions. Journal ofEconometrics, 122:47-79, 2004.
[42] R. F. Engle. Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation. Econometrica, 50:987-1007, 1982.
[43] R. F. Engle and B. S. Yoo. Forecasting and testing in co-integrated systems. Journal of Econometrics, 35:143-159, 1987.
[44] N. R. Ericsson. Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration. Journal ofPolicy
Modeling, 14:465-495, 1992.
[45] N. R. Ericsson and J. Marquez. Encompassing the forecasts of U.S. trade balance models. Review ofEconomics and Statistics, 75:19-31, 1993.
[46] R. Fildes and K. Ord. Forecasting competitions - their role in improving forecasting practice and research. In M. P. Clements and D. F. Hendry, editors, A Companion to Economic Forecasting, pages 322-353. Oxford: Blackwells, 2002.
[47] M. Forni, M. Hallin, M. Lippi, and L. Reichlin. The generalized factor model: Identification and estimation. Review of Economics and Statistics, 82:540-554, 2000.
[48] V. Genre, G. Kenny, A. Meyler, and A. Timmermann. Combining expert fore¬casts: Can anything beat the simple average? International Journal ofForecasting,
29(1):108-121, 2013.
[49] C. W. J. Granger. Prediction with a generalized cost of error function. Operations
Research Quarterly, 20:199-207, 1969.
[50] C. W. J. Granger. Forecasting white noise. In A. Zellner, editor, Proceedings of the Conference on Applied Time Series Analysis ofEconomic Data, pages 308-314. Bureau of the Census, Washington, DC, 1983.
[51] C. W. J. Granger. Combining forecasts - Twenty years later. Journal ofForecasting,
8:167-173, 1989.
[52] C. W. J. Granger. On the limitations of comparing mean squared forecast errors: Comment. Journal ofForecasting, 12:651-652, 1993.
[53] C. W. J. Granger. Can we improve the perceived quality of economic forecasts. Journal ofApplied Econometrics, 11:455-473, 1996.
[54] C. W. J. Granger. Outline of forecast theory using generalized cost functions. Spanish Economic Review, 1:161-173, 1999.
[55] C. W. J. Granger and A. P. Andersen. Introduction to Bilinear Time Series Models. Vandenhoeck & Ruprecht, Göttingen, 1978.
REFERENCES 54
[56] C. W. J. Granger and M. J. Machina. Forecasting and decision theory. In G. Elliott, C. W. J Granger, and A Timmermann, editors, Handbook ofEconomic Forecasting, Volume 1. Handbook ofEconomics 24, pages 81-98. Elsevier, Horth-Holland, 2006.
[57] C. W. J. Granger and O. Morgenstern. Predictability ofStock Market Prices. D. C. Heath and Company, Lexington, Massachusetts, 1970.
[58] C. W. J. Granger and P. Newbold. Some comments on the evaluation of economic forecasts. Applied Economics, 5:35-47, 1973. Reprinted in Mills, T. C. (ed.) (1999), Economic Forecasting. The International Library ofCritical Writings in Economics. Cheltenham: Edward Elgar.
[59] C. W. J. Granger and P. Newbold. Spurious regressions in econometrics. Journal of
Econometrics, 2:111-120, 1974.
[60] C. W. J. Granger and P. Newbold. Economic forecasting: The atheist's viewpoint. In G. A. Renton, editor, Modelling the Economy. Heinemann Educational Books, London, 1975.
[61] C. W. J. Granger and P. Newbold. Forecasting transformed series. Journal ofRoyal Statistical Society, Series B, 38:189-203, 1976.
[62] C. W. J. Granger and M. H. Pesaran. A decision-based approach to forecast evalu¬ation. In W. S. Chan, W. K. Li, and H. Tong, editors, Statistics and Finance: An Interface, pages 261-278. London: Imperial College Press, 2000.
[63] C. W. J. Granger and M. H. Pesaran. Economic and statistical measures of forecast accuracy. Journal ofForecasting, 19:537-560, 2000.
[64] C. W. J. Granger and R. Ramanathan. Improved methods of combining forecasts. Journal ofForecasting, 3:197-204, 1984.
[65] C. W. J. Granger and T. Teröasvirta. Modelling Nonlinear Economic Relationships. Oxford University Press, Oxford, 1993.
[66] Clive W. J. Granger. Forecasting stock market proces: Lessons for forecasters. International Journal ofForecasting, 8:3-13, 1992.
[67] Clive W. J. Granger and Yongil Jeon. A time-distance criterion for evaluating forecasting models. International Journal ofForecasting, 19(2):199-215, 2003.
[68] Clive W. J. Granger and Yongil Jeon. Comparing forecasts of inflation using time distance. International Journal ofForecasting, 19(3):339-349, 2003.
[69] A. Haddow, C. Hare, J. Hooley, and T. Shakir. Macroeconomic uncertainty: what is it, how can we measure it and why does it matter? Bank ofEngland Quarterly Bulletin, pages 100-109, 2013. 2013 Q2.
REFERENCES 55
[70] D. I. Harvey, S. Leybourne, and P. Newbold. Tests for forecast encompassing. Jour¬nal ofBusiness and Economic Statistics, 16:254-259, 1998. Reprinted in T.C. Mills (ed.), Economic Forecasting. Edward Elgar, 1999.
[71] D. F. Hendry and M. P. Clements. Pooling of forecasts. The Econometrics Journal,
7:1-31, 2004.
[72] D. F. Hendry and J-F. Richard. Recent developments in the theory of encompass¬ing. In B. Cornet and H. Tulkens, editors, Contributions to Operations Research and Economics. The XXth Anniversary ofCORE, pages 393-440. MIT Press, Cam¬bridge, MA, 1989. Reprinted in J. Campos, N.R. Ericsson and D.F. Hendry (eds.), General to Specific Modelling. Edward Elgar, 2005.
[73] David F. Hendry and Grayham E. Mizon. Unpredictability in economic analysis, econometric modeling and forecasting. Journal of Econometrics, 182(1):186-195,
2014.
[74] Malte Knöppel. Efficient estimation of forecast uncertainty based on recent forecast errors. International Journal ofForecasting, 30(2):257-267, 2014.
[75] J. P. LeSage and M. Magura. A mixture-model approach to combining forecasts. Journal ofBusiness and Economic Statistics, 10:445-452, 1992.
[76] R. L. Makridakis, S. Winkler. Averages of forecasts: some empirical results. Man¬agement Science, 29:987-996, 1983.
[77] Terence C. Mills. A Very British Affair. Six Britons and the Development of Time Series Analysis During the 20th Century. Palgrave Advanced Tests in Econometrics. Palgrave Macmillan, Basingstoke, 2013.
[78] C. Min and A. Zellner. Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates. Journal of
Econometrics, 56:89-118, 1993.
[79] J. Mincer and V. Zarnowitz. The evaluation of economic forecasts. In J. Mincer, edi¬tor, Economic Forecasts and Expectations, pages 3-46. National Bureau of Economic Research, New York, 1969.
[80] James Mitchell and Stephen G. Hall. Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation. Oxford Bulletin of Economics and Statistics, 67(s1):995-
1033, December 2005.
[81] G. E. Mizon. The encompassing approach in econometrics. In D. F. Hendry and K. F. Wallis, editors, Econometrics and Quantitative Economics, pages 135-172.
Basil Blackwell, Oxford, 1984.
REFERENCES 56
[82] G. E. Mizon and J-F. Richard. The encompassing principle and its application to non-nested hypothesis tests. Econometrica, 54:657-678, 1986.
[83] C. R. Nelson. The prediction performance of the FRB-MIT-PENN model of the US economy. American Economic Review, 62:902-917, 1972. Reprinted in Mills, T. C. (ed.) (1999), Economic Forecasting. The International Library of Critical Writings in Economics. Cheltenham: Edward Elgar.
[84] H. L. Nelson and C. W. J. Granger. Experience using the Box-Cox transformation when forecasting economic time series. Journal ofEconometrics, 10:57-69, 1979.
[85] P. Newbold and D. I. Harvey. Forecasting combination and encompassing. In M. P. Clements and D. F. Hendry, editors, A Companion to Economic Forecasting, pages
268-283. Oxford: Blackwells, 2002.
[86] A. J. Patton and A. Timmermann. Properties of optimal forecasts. Cepr discussion papers 4037, C.E.P.R. Discussion Papers., 2003.
[87] A. J. Patton and A. Timmermann. Testing forecast optimality under unknown loss. Journal ofthe American Statistical Association, 102:1172-1184, 2007.
[88] M. H. Pesaran and M. Weale. Survey expectations. In G. Elliott, C.W.J Granger, and A Timmermann, editors, Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pages 715-776. Elsevier, Horth-Holland, 2006.
[89] David L. Reifschneider and Peter Tulip. Gauging the uncertainty of the economic outlook from historical forecasting errors. Finance and economics discussion series, Board of Governors of the Federal Reserve System (U.S.), 2007.
[90] C. A. Sims. Macroeconomics and reality. Econometrica, 48:1-48, 1980.
[91] J. H. Stock and M. W. Watson. A comparison of linear and nonlinear models for forecasting macroeconomic time series. In R. F. Engle and H. White, editors, Coin-tegration, Causality and Forecasting, pages 1-44. Oxford University Press, Oxford,
1999.
[92] J. H. Stock and M. W. Watson. Forecasting output and inflation: The role of asset prices. Journal ofEconomic Literature, 41:788-829, 2003.
[93] J. H. Stock and M. W. Watson. Dynamic factor models. In M. P. Clements and D. F. Hendry, editors, Oxford Handbook of Economic Forecasting, pages 35-60. Oxford University Press, Oxford, 2011. Chapter 2.
[94] H. Theil. Economic Forecasts and Policy. North-Holland Publishing Company, Amsterdam, 1958. (2nd edition 1961).
[95] H. Theil. Economic Forecasts and Policy. North-Holland Publishing Company, Amsterdam, 1961. (Second revised edition).
REFERENCES 57
[96] A. Timmermann. Forecast combinations. In G. Elliott, C.W.J Granger, and A Tim-mermann, editors, Handbook ofEconomic Forecasting, Volume 1. Handbook ofEco-nomics 24, pages 135-196. Elsevier, Horth-Holland, 2006.
[97] Allan Timmermann and Clive W. J. Granger. Efficient market hypothesis and fore¬casting. International Journal ofForecasting, 20(1):15-27, 2004.
[98] H. R. Varian. A Bayesian approach to real estate assessment. In S. E. Fienberg and A. Zellner, editors, Studies in Bayesian econometrics and statistics in honor of Leonard J. Savage, pages 195-208. North Holland, Amsterdam, 1975.
[99] K. F. Wallis. Combining Density and Interval forecasts: A Modest Proposal. Oxford Bulletin ofEconomics and Statistics, 67(s1):983-994, 2005.
[100] M. West and P. J. Harrison. Bayesian Forecasting and Dynamic Models. Springer
Verlag, New York, 1989.
[101] P. Whittle. Prediction and Regulation by Linear Least-Square Methods. D. Van Nostrand, Princeton, 1963.

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