Nonlinear Econometric Modelling: A Selective Review
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[1] L. Hurwicz,et al. Measuring Business Cycles. , 1946 .
[2] C. Granger,et al. An introduction to bilinear time series models , 1979 .
[3] R. Hall. Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence , 1978, Journal of Political Economy.
[4] Anthony L Bertapelle. Spectral Analysis of Time Series. , 1979 .
[5] R. Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .
[6] Hung Man Tong,et al. Threshold models in non-linear time series analysis. Lecture notes in statistics, No.21 , 1983 .
[7] Salih N. Neftçi. Are Economic Time Series Asymmetric over the Business Cycle? , 1984, Journal of Political Economy.
[8] James L. McClelland,et al. James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.
[9] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[10] I. Zurbenko. The spectral analysis of time series , 1986 .
[11] M. Pourahmadi. ON STATIONARITY OF THE SOLUTION OF A DOUBLY STOCHASTIC MODEL , 1986 .
[12] A. Brandt. The stochastic equation Yn+1=AnYn+Bn with stationary coefficients , 1986 .
[13] Nien-Fan Zhang,et al. A RANDOM PARAMETER PROCESS FOR MODELING AND FORECASTING TIME SERIES , 1986 .
[14] Robert F. Engle,et al. Model selection for forecasting , 1986 .
[15] D. Tjøstheim. SOME DOUBLY STOCHASTIC TIME SERIES MODELS , 1986 .
[16] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[17] Frederic S. Mishkin. The Information in the Term Structure: Some Further Results , 1988 .
[18] M. Pourahmadi. STATIONARITY OF THE SOLUTION OF Xt= AtXt‐1+εt AND ANALYSIS OF NON‐GAUSSIAN DEPENDENT RANDOM VARIABLES , 1988 .
[19] Timo Teräsvirta,et al. Testing linearity against smooth transition autoregressive models , 1988 .
[20] H. White,et al. Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.
[21] Soumitra Dutta,et al. Bond rating: A non-conservative application of neural networks , 1988 .
[22] Timo Teräsvirta,et al. Testing linearity in univariate, time series models , 1988 .
[23] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[24] James M. Nason,et al. Nonparametric exchange rate prediction , 1990 .
[25] Halbert White,et al. Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.
[26] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[27] S. M. Carroll,et al. Construction of neural nets using the radon transform , 1989, International 1989 Joint Conference on Neural Networks.
[28] James D. Hamilton. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .
[29] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[30] Kurt Hornik,et al. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.
[31] H. Tong. Non-linear time series. A dynamical system approach , 1990 .
[32] Halbert White,et al. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.
[33] Ray C. Fair,et al. Comparing Information in Forecasts from Econometric Models , 1990 .
[34] David E. Runkle,et al. Testing the Rationality of Price Forecasts: New Evidence from Panel Data , 1990 .
[35] John E. Moody,et al. Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction , 1991, NIPS.
[36] Gordon Leitch,et al. Economic Forecast Evaluation: Profits versus the Conventional Error Measures , 1991 .
[37] Douglas M. Patterson,et al. Nonlinear Dynamics, Chaos, And Instability , 1994 .
[38] Herman Stekler,et al. Macroeconomic forecast evaluation techniques , 1991 .
[39] T. Teräsvirta,et al. Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models , 1992 .
[40] Victor Zarnowitz,et al. Twenty-Two Years of the Nber-Asa Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance , 1992 .
[41] M. Hashem Pesaran,et al. A Simple Nonparametric Test of Predictive Performance , 1992 .
[42] Bruce Mizrach,et al. Multivariate nearest‐neighbour forecasts of ems exchange rates , 1992 .
[43] Chung-Ming Kuan,et al. Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .
[44] Philip Rothman. The Comparative Power of the TR Test against Simple Threshold Models , 1992 .
[45] C. Granger,et al. Modelling Nonlinear Economic Relationships , 1995 .
[46] Dean Croushore,et al. Introducing: The Survey of Professional Forecasters , 1993 .
[47] Clive W. J. Granger,et al. Strategies for Modelling Nonlinear Time‐Series Relationships* , 1993 .
[48] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[49] Clive W. J. Granger,et al. Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests , 1993 .
[50] Andrew B. Whinston,et al. New directions in computational economics , 1994 .
[51] Halbert White,et al. Artificial neural networks: an econometric perspective ∗ , 1994 .
[52] Brian D. Ripley,et al. Neural Networks and Related Methods for Classification , 1994 .
[53] F. Diebold,et al. Comparing Predictive Accuracy , 1994, Business Cycles.
[54] Herman Stekler,et al. Are economic forecasts valuable , 1994 .
[55] R. Dorsey,et al. The Use of Artificial Neural Networks for Estimation of Decision Surfaces in First Price Sealed Bid Auctions , 1994 .
[56] M. Hashem Pesaran,et al. A generalization of the non-parametric Henriksson-Merton test of market timing , 1994 .
[57] G. Laroque,et al. Estimating the canonical disequilibrium model: Asymptotic theory and finite sample properties , 1994 .
[58] Simon M. Potter. A Nonlinear Approach to US GNP , 1995 .
[59] Eric Ghysels,et al. Série Scientifique Scientific Series N o 95 s19 IS SEASONAL ADJUSTMENT A LINEAR OR NONLINEAR DATA FILTERING PROCESS ? , 1997 .
[60] H. Pesaran,et al. The Use of Recursive Model Selection Strategies in Forecasting Stock Returns , 1995 .
[61] Richard Cohen,et al. Testing the Rationality of Price Forecasts: Comment , 1995 .
[62] Norman R. Swanson,et al. A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks , 1995 .
[63] Common Persistence in Nonlinear Autoregressive Models , 1996 .
[64] Philip Hans Franses,et al. Periodicity and Stochastic Trends in Economic Time Series , 1996 .
[65] Brendan McCabe,et al. Can Economic Time Series Be Differenced to Stationarity , 1996 .
[66] Does Seasonal Adjustment Change Inference from MARKOV Switching Models , 1996 .
[67] Andrew A. Weiss,et al. Estimating Time Series Models Using the Relevant Cost Function , 1996 .
[68] B. LeBaron,et al. A test for independence based on the correlation dimension , 1996 .
[69] Andre Lucas,et al. Testing for ARCH in the presence of additive outliers , 1999 .
[70] Norman R. Swanson,et al. Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data , 1996 .
[71] Norman R. Swanson,et al. An introduction to stochastic unit-root processes , 1997 .
[72] Norman R. Swanson,et al. A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks , 1997, Review of Economics and Statistics.
[73] Philip Hans Franses,et al. Recognizing changing seasonal patterns using artificial neural networks , 1997 .
[74] P. Franses,et al. Do We Often Find ARCH Because Of Neglected Outliers , 1997 .
[75] Philip Hans Franses,et al. On forecasting exchange rates using neural networks , 1998 .
[76] Norman R. Swanson,et al. Testing for stationarity-ergodicity and for comovements between nonlinear discrete time Markov processes , 2000 .
[77] Jeffrey S. Racine,et al. Semiparametric ARX neural-network models with an application to forecasting inflation , 2001, IEEE Trans. Neural Networks.
[78] Norman R. Swanson,et al. Further developments in the study of cointegrated variables , 2010 .