https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1980.tb01126.x

Threshold Autoregression, Limit Cycles and Cyclical Data

An approach to the modeling and analysis of multiple time series is proposed. Properties of a class of vector autoregressive moving average models are discussed. Modeling procedures consisting of tentative specification, estimation, and diagnostic checking are outlined and illustrated by three real examples.

Modeling Multiple Time Series with Applications

We consider estimation of the long term average power output from a wind turbine generator at a site for which few data on wind speeds are available. Long term records of wind speeds at the 12 synoptic meteorological stations are also used. Inference is based on a simple and parsimonious approximating model which accounts for the main features of wind speeds in Ireland, namely seasonal effects, spatial correlation, short-memory temporal autocorrelation and long-memory temporal dependence. It synthesizes deseasonalization, kriging, ARMA modelling and fractional differencing in a natural way

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2347679

Space-time modelling with long-memory dependence assessing Ireland's wind power resource

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2345077

Accuracy of Forecasting: An Empirical Investigation

The implications for applied econometrics of the assumption that unobservable expectations are formed rationally in Muth's sense are examined. The statistical properties of the resulting models and their distributed lag and time series representations are described. Purely extrapolative forecasts of endogenous variables can be constructed, as alternatives to rational expectations, but are less efficient. Identification and estimation are considered: an order condition is that no more expectations variables than exogenous variables enter the model. Estimation is based on algorithms for nonlinear-in-parameters systems; other approaches are surveyed. Implications for economic policy and econometric policy evaluation are described. EXPECTATIONS VARIABLES ARE WIDELY USED in applied econometrics, since the optimizing behavior of economic agents, which empirical research endeavors to capture, depends in part on their views of the future. Directly observed expectations or anticipations are relatively rare, hence implicit forecasting schemes are used. Most commonly expectations are taken to be extrapolations, that is, weighted averages of past values of the variable under consideration. However, these "are almost surely inaccurate gauges of expectations. Consumers, workers, and businessmen ... do read newspapers and they do know better than to base price expectations on simple extrapolation of price series alone" (Tobin [31, p. 14]). An alternative approach is offered by the rational expectations hypothesis of Muth [15], which assumes that in forming their expectations of endogenous variables, economic agents take account of the interrelationships among variables described by the appropriate economic theory. "Price movements observed and experienced do not necessarily convey information on the basis of which a rational man should alter his view of the future. When a blight destroys half the midwestern corn crop and corn prices subsequently rise, the information conveyed is that blights raise prices. No trader or farmer under these circumstances would change his view of the future of corn prices, much less of their rate of change, unless he is led to reconsider his estimate of the likelihood of blights," again quoting Tobin. This paper examines the implications of the rational expectations hypothesis for applied econometrics, and argues that its full force has yet to be appreciated in empirical work. The discussion is quite general, proceeding in terms of the standard linear simultaneous equation system, and pays little attention to specific applications of the hypothesis, such as the "efficient markets" literature and

W.-Y. T. Chan

Papers

Multiple Time Series Modelling: Another Look at the Mink‐Muskrat Interaction