This paper examines the efficiency gains of GLS detrending over OLS detrending in the model yt = z′ tγ + ut, where z ′ t = (1, t, ..., t k−1) and ut ∼ I(d), −1/2 < d < 3/2. A famous theorem on trend removal by OLS regression (usually attributed to Grenander and Rosenblatt (1957) gave conditions for the asymptotic equivalence of GLS and OLS in deterministic trend estimation with I(0) errors. Whe...

This paper analyzes an approach to correcting spurious regressions involving unit-root nonstationary variables by generalized least squares (GLS) using asymptotic theory. This analysis leads to a new robust estimator and a new test for dynamic regressions. The robust estimator is consistent for structural parameters not just when the regression error is stationary but also when it is unit-root ...

Consider a regression model with infinitely many parameters and time series errors. We are interested in choosing weights for averaging across generalized least squares (GLS) estimators obtained from a set of approximating models. However, GLS estimators, depending on the unknown inverse covariance matrix of the errors, are usually infeasible. We therefore construct feasible generalized least s...

Generalized least squares (GLS) for model parameter estimation has a long and successful history dating to its development by Gauss in 1795. Alternatives can outperform GLS in some settings, and alternatives to GLS are sometimes sought when GLS exhibits curious behavior, such as in Peelle’s Pertinent Puzzle (PPP). PPP was described in 1987 in the context of estimating fundamental parameters tha...

The spread of information technology and the digitalization financial services raise a range theoretical questions as structures banking industry undergo change. This change has intensified with impact COVID-19 pandemic which is already being observed. purpose this study therefore threefold: (1) to analyze IT on strategy functioning pre-COVID-19 sector; (2) challenges banks are facing in COVID ...

Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-C...

The practice of estimating static conditional mean functions from time series data and then using "robust" covariance matrices for inference is examined against alternative approaches. We show that when contemporaneous exogeneity is violated, that the asymptotic bias associated with GLS can actually be less than that of OLS. This result extends to Feasible GLS where the error process is approxi...

This paper studies the error in forecasting an autoregressive process with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model that detrends the data using OLS before estimating the autoregressive parameters are much less precise than those based on an autoregression that includes the deterministic components, and the asymptotic dis...

Guided Local Search (GLS) is a general meta-heuristic that sits on top of local search algorithms and help them escape from local minima. However, GLS is often trapped in a small local area. Estimation of Distribution Algorithm(EDA) is a new evolutionary algorithm in which statistical information are explicitly extracted from the current population and then used for generating new solutions. Th...

While the relationship between stock market return and oil price is of great interest to researchers, previous studies do not investigate stock market return with petrochemical products market. In this paper, we analyzed the relationship between prices of main petrochemical products and stock returns of petrochemical companies in Tehran stock exchange. Using a panel data model and GLS estimatio...