We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. We are motivated by the least squares temporal difference learning algorithm (LSTD), which is known for its efficient use of sample experiences compared to pure temporal difference algorithms. LSTD is ideal for predict...

Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estimation. We introduce a new criterion based on sequentially minimized squared deviations, which are smaller than both the usual least squares and the squared prediction errors used in PLS. We also prove that our criterion...

• ABSTRACT We consider heteroscedastic linear models for which the variances are parametric functions of known regressors. Second order expansions are derived for a class of estimators which includes normal theory maximum likelihood and generalized least squares. The result is a fairly precise description of when conventional asymptotic variance formulae are optimistic; i.e., they underestimate...

The paper examines the stability of the day of the week effect in returns and volatility at the Indian capital market, covering the period January 1991 – September 2000. The paper specifies a generalized autoregressive conditional heteroscedasticity (GARCH) model on returns and introduces separate dummies for days in alternate weeks in the specification of both the mean and the conditional vari...

Generalized least-squares (GLS ) regression extends ordinary least-squares (OLS) estimation of the normal linear model by providing for possibly unequal error variances and for correlations between different errors. A common application of GLS estimation is to time-series regression, in which it is generally implausible to assume that errors are independent. This appendix to Fox and Weisberg (2...

We introduce a new nonparametric regression estimator that uses prior information on regression shape in the form of a parametric model. In eeect, we nonparametrically encompass the parametric model. We obtain estimates of the regression function and its derivatives along with local parameter estimates that can be interpreted from within the parametric model. We establish the uniform consistenc...

Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this paper, sev...