Predicting the turning points of business and economic time series

Article Abstract:

William F. Wecker's research into the use of linear least squares analysis to predict turning points in economic- and business-related time series is extended to fit multiple time-series models while considering model uncertainty and reliability. The extended least squares prediction method developed is then applied to predictions of turning points in time-series connected to gross national product levels and growth rates for consumer price indices and money supply levels. Wecker's univariate analysis techniques are applied, extended, and explained. A technique for 'debiasing' probability assessments derived using the least squares turning-point time-series analysis methodology is also developed.

Models, Economic research, Gross national product, Criticism and interpretation, Economic forecasting, Business cycles, Money supply, Consumer price indexes, Statistics (Mathematics), Correlation (Statistics), Mathematical statistics, Least squares, Linear algebraic groups, Wecker, William F.

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Optimal investments using empirical dynamic programming with application to natural resources

Article Abstract:

A discrete Markov time-series model is used to circumvent the problems that arise when dynamic programming is applied to investment problems. The resulting computer algorithms are applied to two natural resources examples. Results indicate that the specification of the state process and the level of discretization used in the Markov approximation are critical to the optimization of policies.

Investments, Stochastic analysis, Markov processes, Dynamic programming

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Conditional heteroscedasticity in time series of stock returns: evidence and forecasts

Article Abstract:

The time-series behavior of stock prices is analyzed. Results indicate that second-order dependence is exhibited by daily return series and a return generating process is shown to be first order autoregressive. The analysis also includes a statistical comparison of some out-of-sample forecasts of monthly return variances.

Stocks

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