Sir Clive Granger's Contributions to Nonlinear Time Series and Econometrics

[1] Heather M. Anderson and Farshid Vahid. Testing multiple equation systems for common nonlinear components. Journal of Econometrics, 84:1-36, 1998.
[2] R. Ashley, C. W. J. Granger, and R. Schmalensee. Advertising and aggregate con¬sumption: An analysis of causality. Econometrica, 48:1149-1167, 1980.
[3] Nathan S. Balke and Thomas B. Fomby. Threshold cointegration. International Economic Review, 38:627-645, 1997.
[4] J. M. Bates and Clive W. J. Granger. The combination of forecasts. Operational
Research Quarterly, 20:451-468, 1969.
[5] Ralf Becker and A. Stan Hurn. Testing for nonlinearity in mean in the presence of heteroskedasticity. Economic Analysis & Policy, 39:311-326, 2009.
[6] Anil K. Bera and Matthew L. Higgins. ARCH and bilinearity as competing models for nonlinear dependence. Journal of Business & Economic Statistics, 15:43-50,
1997.
[7] George E. P. Box and Gwilym M. Jenkins. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, 1970.
[8] W. A. Brock, W. D. Dechert, J. A. Scheinkman, and B. LeBaron. A test for in¬dependence based on the correlation dimension. Econometric Reviews, 15:197-235,
1996.
[9] Sangit Chatterjee and Mustafa R. Yilmaz. Chaos, fractals and statistics. Statistical
Science, 7:49-68, 1992.
[10] Harald Cramer. On some classes of non-stationary processes. In Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability, pages 157-178. University of California Press, 1961.
[11] R. B. Davies. Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 64:247-254, 1977.
[12] Melinda Deutsch, Clive W. J. Granger, and Timo Terasvirta. The combination of forecasts using changing weights. International Journal of Forecasting, 10:47-57,
1994.
[13] Ingolf Dittmann and Clive W. J. Granger. Properties of nonlinear transformations of fractionally integrated processes. Journal ofEconometrics, 110:113-133, 2002.
[14] Bruno Eklund. Four contributions to statistical inference in econometrics. Stockholm School of Economics, Stockholm, 2003.
REFERENCES 128
[15] Walter Enders and Clive W. J. Granger. Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16:304-331, 1998.
[16] Luigi Ermini and Clive W. J. Granger. Some generalizations on the algebra of I(1) processes. Journal ofEconometrics, 58:369-384, 1993.
[17] Eric Ghysels, Norman R. Swanson, and Mark W. Watson. Introduction. In Eric Ghysels, Norman R. Swanson, and Mark W. Watson, editors, Essays in economet¬rics. Collected papers of Clive W.J. Granger, volume 1, pages 1-27, Cambridge, 2001. Cambridge University Press.
[18] Stephen M. Goldfeld and Richard E. Quandt. Nonlinear Methods in Econometrics. North-Holland, Amsterdam, 1972.
[19] Clive W. J. Granger. Long memory relationships and the aggregation of dynamic models. Journal ofEconometrics, 14:227-238, 1980.
[20] Clive W. J. Granger. Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16:121-130, 1981.
[21] Clive W. J. Granger. Forecasting white noise. In Arnold Zellner, editor, Applied time series analysis of economic data : Proceedings of the Conference on Applied Time Series Analysis of Economic Data, October 13-15, 1981, Arlington, VA, pages 308-314, Washington, DC, 1983. U.S. Department of Commerce, Bureau of the Census.
[22] Clive W. J. Granger. Developments in the study of cointegrated variables. Oxford Bulletin ofEconomics and Statistics, 48:213-228, 1986.
[23] Clive W. J. Granger. Models that generate trends. Journal ofTime Series Analysis,
9:329-343, 1988.
[24] Clive W. J. Granger. Developments in the nonlinear analysis of economic series. Scandinavian Journal ofEconomics, 93:263-276, 1991.
[25] Clive W. J. Granger. Comment. Statistical Science, 7:102-104, 1992.
[26] Clive W. J. Granger. Strategies for modelling nonlinear time-series relationships.
Economic Record, 69:233-238, 1993.
[27] Clive W. J. Granger. Some comments on empirical investigations involving cointe-gration. Econometric Reviews, 13:345-350, 1994.
[28] Clive W. J. Granger. Modelling nonlinear relationships between extended-memory variables. Econometrica, 63:265-279, 1995.
REFERENCES 129
[29] Clive W. J. Granger. Overview of nonlinear macroeconometric models. Macroeco-nomic Dynamics, 5:466-481, 2001.
[30] Clive W. J. Granger. Non-linear models: Where do we go next - time-varying parameter models? Studies in Nonlinear Dynamics and Econometrics, 12:Issue 1,
Article 3, 2008.
[31] Clive W. J. Granger and A. P. Andersen. An Introduction to Bilinear Time Series. Vandenhoeck and Ruprecht, Gottingen, 1978.
[32] Clive W. J. Granger and Allan P. Andersen. On the invertibility of time series models. Stochastic Processes and their Applications, 8:87-92, 1978.
[33] Clive W. J. Granger and Jeff Hallman. Nonlinear transformations of integrated time series. Journal ofTime Series Analysis, 12:207-224, 1991.
[34] Clive W. J. Granger and Namwon Hyung. Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, 11:399-421, 2004.
[35] Clive W. J. Granger and Namwon Hyung. Introduction to m-m processes. Journal
ofEconometrics, 130:143-164, 2006.
[36] Clive W. J. Granger, Tomoo Inoue, and Norman Morin. Nonlinear stochastic trends. Journal ofEconometrics, 81:65-92, 1997.
[37] Clive W. J. Granger, Maxwell L. King, and Halbert White. Comments on testing economic theories and the use of model selection criteria. Journal ofEconometrics,
67:173-187, 1995.
[38] Clive W. J. Granger and Hahn S. Lee. An introduction to time-varying parameter cointegration. In Peter Hackl and Anders H. Westlund, editors, Economic structural change. Analysis and forecasting, pages 139-157, Berlin, 1991. Springer.
[39] Clive W. J. Granger and T. H. Lee. Investigation of production, sales and inven¬tory relationships using multicointegration and non-symmetric error using correction models. Journal ofApplied Econometrics, 4:S145-S159, 1989.
[40] Clive W. J. Granger and Tae-Hwy Lee. The effect of aggregation on nonlinearity.
Econometric Reviews, 18:259-269, 1999.
[41] Clive W. J. Granger and Jin-Lung Lin. Using the mutual information coefficient to identify lags in nonlinear models. Journal of Time Series Analysis, 15:371-384,
1994.
[42] Clive W. J. Granger, Esfandiar Maasoumi, and Jeff Racine. A dependence metric for possibly nonlinear processes. Journal ofTime Series Analysis, 25:649-669, 2004.
REFERENCES 130
[43] Clive. W. J. Granger and Norman Swanson. Further developments in the study of cointegrated variables. Oxford Bulletin of Economics and Statistics, 58:537-553,
1996.
[44] Clive W. J. Granger and Norman R. Swanson. An introduction to stochastic unit-root processes. Journal ofEconometrics, 80:35-62, 1997.
[45] Clive W. J. Granger and Timo Terasvirta. Modelling Nonlinear Economic Relation¬ships. Oxford University Press, Oxford, 1993.
[46] Clive W. J. Granger and Timo Terasvirta. A simple nonlinear time series model with misleading linear properties. Economics Letters, 62:161-165, 1999.
[47] Clive W. J. Granger, Timo Terasvirta, and Andrew J. Patton. Common factors in conditional distributions for bivariate time series. Journal ofEconometrics, 132:45¬57, 2006.
[48] David Harris, Brendan McCabe, and Stephen Leybourne. Stochastic cointegration: estimation and inference. Journal ofEconometrics, 111:363-384, 2002.
[49] David F. Hendry. The Nobel Memorial Prize for Clive W.J. Granger. Scandinavian
Journal ofEconomics, 106:187-213, 2004.
[50] Anders B. Kock and Timo Teraasvirta. Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009. International Journal of
Forecasting, 30:616-631, 2014.
[51] Anders B. Kock and Timo Teraasvirta. Forecasting macroeconomic variables using neural network models and three automated model selection techniques. Economet¬ric Reviews, 35:1753-1779, 2016.
[52] Markku Lanne and Pentti Saikkonen. Threshold autoregressions for strongly au-tocorrelated time series. Journal of Business & Economic Statistics, 20:282-289,
2002.
[53] Tae-Hwy Lee, Halbert White, and Clive W. J. Granger. Testing for neglected non-linearity in time series models: A comparison of neural network methods and alter¬native tests. Journal ofEconometrics, 56:269-290, 1993.
[54] S. J. Leybourne, B. P. M. McCabe, and A. R. Tremayne. Can economic time series be differenced to stationarity? Journal of Business & Economic Statistics, 14:435-446,
1996.
[55] Stephen J. Leybourne, Brendan P. M. McCabe, and Terence C. Mills. Randomized unit root processes for modelling and forecasting financial time series: Theory and applications. Journal ofForecasting, 15:253-270, 1996.
REFERENCES 131
[56] Chien-Fu J. Lin and Timo Teraasvirta. Testing the constancy of regression parameters against continuous structural change. Journal ofEconometrics, 62:211-228, 1994.
[57] T. Liu, C. W. J. Granger, and W. P. Heller. Using the correlation exponent to decide whether an economic series is chaotic. Journal ofApplied Econometrics, 7:S25-S39,
1992.
[58] Ritva Luukkonen, Pentti Saikkonen, and Timo Teraasvirta. Testing linearity against smooth transition autoregressive models. Biometrika, 75:491-499, 1988.
[59] Agustin Maravall. An application of nonlinear time series forecasting. Journal of Business & Economic Statistics, 1:66-74, 1983.
[60] Brendan McCabe, Stephen J. Leybourne, and David Harris. A residual-based test for stochastic cointegration. Econometric Theory, 22:429-456, 2006.
[61] Brendan P. M. McCabe and Andrew R. Tremayne. Testing a time series for difference stationarity. Annals ofStatistics, 23:1015-1028, 1995.
[62] A. I. McLeod and W. K. Li. Diagnostic checking ARMA time series models using squared residual autocorrelations. Journal ofTime Series Analysis, 4:269-273, 1983.
[63] Maurice B. Priestley. Evolutionary spectra and non-stationary processes. Journal of the Royal Statistical Society, Series B, 27:204-237, 1965.
[64] B. G. Quinn. Stationarity and invertibility of simple bilinear models. Stochastic Processes and their Applications, 12:225-230, 1982.
[65] Antti Ripatti and Pentti Saikkonen. Vector autoregressive processes with nonlinear time trends in cointegrating relations. Macroeconomic Dynamics, 5:577-597, 2001.
[66] Jorma Rissanen. Modeling by shortest data description. Automatica, 14:465-471,
1978.
[67] Gideon Schwarz. Estimating the dimension of a model. Annals ofStatistics, 4:461¬464, 1978.
[68] James H. Stock and Mark W. Watson. A comparison of linear and nonlinear uni-variate models for forecasting macroeconomic time series. In Robert F. Engle and Halbert White, editors, Cointegration, Causality and Forecasting. A Festschrift in Honour ofClive W.J. Granger, pages 1-44, Oxford, 1999. Oxford University Press.
[69] T. Subba Rao and M. M. Gabr. An Introduction to Bispectral Analysis and Bilinear Time Series Models. Springer, New York, 1984.
[70] Timo Teraasvirta. Power properties of linearity tests for time series. Studies in Nonlinear Dynamics and Econometrics, 1:3-10, 1996.
REFERENCES 132
[71] Timo Teraasvirta, Chien-Fu Lin, and Clive W. J. Granger. Power of the neural network linearity test. Journal of Time Series Analysis, 14:309-323, 1993.
[72] Timo Terasvirta, Dag Tj0stheim, and Clive W. J. Granger. Aspects of modelling nonlinear time series. In Robert F. Engle and Daniel L. McFadden, editors, Handbook ofEconometrics, volume 4, pages 2919-2957, Amsterdam, 1994. Elsevier.
[73] Timo Teraasvirta, Dag Tj0stheim, and Clive W. J. Granger. Modelling Nonlinear Economic Time Series. Oxford University Press, Oxford, 2010.
[74] Howell Tong. On a threshold model. In Chi-Hau Chen, editor, Pattern recognition and signal processing, number 29 in Nato Advanced Study Institutes series: Applied sciences. Sijthoff & Noordhoff, 1978.
[75] Howell Tong. Non-Linear Time Series. A Dynamical System Approach. Oxford University Press, Oxford, 1990.
[76] Ruey S. Tsay. Nonlinearity tests for time series. Biometrika, 73:461-466, 1986.
[77] Halbert White. An additional hidden unit test for neglected non-linearity in multi¬layer feedforward networks. In Proceedings ofthe International Joint Conference on Neural Networks, Washington DC, volume 1, pages 451-55, San Diego, CA, 1989.
SOS Printing.
[78] Halbert White. Approximate nonlinear forecasting methods. In Graham Elliott, Clive W. J. Granger, and Allan Timmermann, editors, Handbook ofEconomic Fore¬casting, volume 1, pages 459-512, Amsterdam, 2006. Elsevier.
[79] Halbert White and Clive W. J. Granger. Consideration of trends in time series. Journal ofTime Series Econometrics, 3:Iss. 1, Article 2, 2011.
[80] Gawon Yoon. A note on some properties of STUR processes. Oxford Bulletin of Economics and Statistics, 68:253-260, 2006.
[81] Gawon Yoon. Stochastic unit root processes: Maximum likelihood estimation, and new Lagrange multiplier and likelihood ratio tests. Economic Modelling, 52:725-732,
2016.