STAT 581 SOME PAPERS IN THE TIME SERIES LITERATURE

This list of papers is highly influenced by my Duke years. Needs some revision.

Time Series Methods on Latent Structure

  • Aguilar O., Huerta G. Prado R. and West M. (1998). Bayesian inference on latent structure in time series. Institute of Statistics and Decision Sciences. Working Paper 98-07.
  • Aguilar O. and West M. (1998). Bayesian dynamic factor models and variance matrix discounting for portafolio allocation Institute of Statistics and Decision Sciences. Working Paper 98-03.
  • Huerta G. and West M. (1997). Priors and Component Structures in Autoregressive Time Series Models. Institute of Statistics and Decision Sciences. Working Paper 97-03.
  • West, M., Prado, R. and Krystal A. (1997) Evaluation and Comparison of EEG traces: Latent Structure in Non-Stationary Time Series. Institute of Statistics and Decision Sciences, Duk e University, Discussion paper 97-14
  • Huerta G. and West M. (1997). Bayesian Inference on Periodicities and Component Spectral Structure in Time Series. Institute of Statistics and Decision Sciences. Working Paper 97-13.
  • Prado, R. and West, M. (1996) Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions. Institute of Statistics and Decision Sciences , Duke University, Discussion paper 96-13
  • West M. (1995) Time series decomposition and analysis in a study of oxygen isotope records Postscript PDF Institute of Statistics and Decision Sciences, Duk e University, Discussion paper 95-18

    • Dynamic Models-Methodology

  • Carter, C. and Kohn, R. (1996). "Markov chain Monte Carlo in conditionally Gaussian state space models". Biometrika. 83, 5889-601.
  • M. West (1995) Bayesian Forecasting. Institute of Statistics and Decision Sciences, Duke University, Discussion paper 95-11
  • Cargnoni C., Mueller P., and West M. (1995) Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models. Institute of Statistics and Decision Scienc es, Duke University, Discussion paper 95-22
  • DeJong, P. and Shephard, N. (1995). "The Simulation Smoother for Time Series Models". Biometrika. 82, 339-350.
  • Carter, C. and Kohn, R. (1994). "On Gibbs sampling for state space models". Biometrika. 81, 541-53.
  • Shepard, N. (1994), Partial non-Gaussian state space, Biometrika, 81, 115-3
  • M. West (1993) Inference in Cycles in Time Series Institute of Statistics and Decision Sciences, Duke University, Discussion paper 93-A23

    • ARCH, GARCH, EGARCH, Stochastic Volatility models

  • Jacquier, E., Polson, N.G.,and Rossi, P. (1994). Bayesian analysis of stochastic volatility models. Journal of Business and Economics Statistics.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedacity. Journal of Econometrics. 31, 307-327.
  • Engle R. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica. 50, 987-1008.
  • Harvey, A.C., Ruiz, E. and Shepard, N. (1994). Multivariate stochastic variance models. Rev. Econ. Stud., 61, 247-264.
  • Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econmetrica, 59, 347-370.

    • On the Unit Root problem

  • McCulloch, R.E. and Tsay R.S (1994). Bayesian Inference of Trend- and Difference-Stationarity. Econometric Theory, 10, 596-608.
  • Dickey, D.A. and Fuller W.A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with Unit Root. Econometrica, 49, 1057-1072.
  • Dickey, D.A. and Fuller W.A. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74-366, 427-431.