We argue for Ordinary Least Squares (OLS) estimation of Taylor rules despite an endogeneity bias. To that end, we show analytically in the three-equation New Keynesian model OLS bias is proportional to fraction variance regressors due monetary shocks. Using simulations, this relationship also holds a quantitative U.S. economy. Since shocks explain only small typical rule regressors, tends be sm...

We develop an ordinary least squares estimator of the long memory parameter from a fractionally integrated process that is an alternative to the Geweke Porter-Hudak estimator. Using the wavelet transform from a fractionally integrated process, we establish a log-linear relationship between the wavelet coe cients' variance and the scaling parameter equal to the long memory parameter. This log-li...

Abstract This study aims to measure the effect of money supply on rate interest and gross domestic product (GDP) private sector in Jordan show order determine nature narrow expanded Jordanian economy. The uses a fully modified ordinary least squares (FM-OLS) method depending time data for period (1990–2019). results estimation that there is positive significant real GDP sector. In addition, it ...

Motivated by telecommunication applications, we investigate ways to estimate the parameters of a nonhomogeneous Poisson process with linear rate over a finite interval, based on the number of counts in measurement subintervals. Such a linear arrival-rate function can serve as a component of a piecewise-linear approximation to a general arrival-rate function. We consider ordinary least squares (...

In this paper, we demonstrate the importance of conducting well-thought-out sensitivity analyses for handling clustered data (data in which individuals are grouped into higher order units, such as students in schools) that arise from cluster randomized controlled trials (RCTs). This is particularly relevant given the rise in rigorous impact evaluations that use cluster randomized designs across...

We demonstrate the effectiveness of intrinsic Raman spectroscopy (IRS) at reducing errors caused by absorption and scattering. Physical tissue models, solutions of varying absorption and scattering coefficients with known concentrations of Raman scatterers, are studied. We show significant improvement in prediction error by implementing IRS to predict concentrations of Raman scatterers using bo...

In the Capital Asset Pricing Model (CAPM), beta coefficient is a very important parameter to be estimated. The most commonly used estimating methods are the Ordinary Least Squares (OLS) and some Robust Regression Techniques (RRT). However, these traditional methods make strong as sumptions which are unrealistic. In addition, The OLS method is very sensitive to extreme observations, while the RR...

The standard method for analyzing functional magnetic resonance imaging (fMRI) data applies the general linear model to the time series of each voxel separately. Such a voxelwise approach, however, does not consider the spatial autocorrelation between neighboring voxels in its model formulation and parameter estimation. We propose a spatio-temporal regression analysis for detecting activation i...