Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new...

In current restructured wholesale power markets, the short length of time series for prices makes it difficult to use empirical price data to test existing price forecasting tools and to develop new price forecasting tools. This study therefore proposes a two-stage approach for generating...

Short-term congestion forecasting is highly important for market participants in wholesale power markets that use Locational Marginal Prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in...

This chapter reviews work by the authors on the multicriteria Flexible Least Squares (FLS) approach to model estimation. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/flshome.htm

A Fortran implementation for the "Flexible Least Squares" (FLS) method for time-varying linear regression (FLS-TVLR) is discussed. The latest Fortran implementation for FLS-TVLR, along with tutorials and research publications, can be accessed here: http://www.econ.iastate.edu/tesfatsi/flshome.htm

This article discusses the use of associative memories for obtaining preliminary parameter estimates for nonlinear systems. Annotated pointers to related work can be accessed at http://www.econ.iastate.edu/tesfatsi/vita.htm#MAM

An exact procedure is developed for sequentially updating the optimal solution for a general discrete-time nonlinear least-squares estimation problem as the process length increases and new observations are obtained. The optimal sequential estimation equations are derived by means of an...

This article appeared in Computer Science in Economics and Management (now Computational Economics). Parameter estimation problems for nonlinear systems are typically formulated as nonlinear optimization problems. For such problems, one has the usual difficulty that standard successive...

This study develops a general multicriteria Flexible Least Squares (FLS) framework for the sequential estimation of process states. Three well-known state estimation algorithms (the Viterbi, Larson-Peschon, and Kalman filters) are derived as monocriterion specializations. The FLS framework is...

This study uses the Flexible Least Squares method for Time-Varying Linear Regression (FLS-TVLR) to investigate coefficient stability for the Goldfeld U.S. money demand model over the volatile period 1959:Q2 to 1985:Q3. The only constraint imposed on coefficient variation over time is a...