More powerful cointegration tests with non-normal errors

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Hyejin Lee ◽  
Junsoo Lee ◽  
Kyungso Im
Keyword(s):  
Least Squares ◽  
Testing Procedure ◽  
Error Distribution ◽  
Linear Estimation ◽  
Moment Conditions ◽  
Significant Power ◽  
Cointegration Tests ◽  
Normal Error ◽  
Simulation Results ◽  
Normal Error Distribution

AbstractIn this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors.

scholarly journals An Approximate Adder With a Near-Normal Error Distribution: Design, Error Analysis and Practical Application

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4518-4530
Author(s):  
Padmanabhan Balasubramanian ◽  
Raunaq Nayar ◽  
Douglas L. Maskell ◽  
Nikos E. Mastorakis
Keyword(s):  
Error Analysis ◽  
Error Distribution ◽  
Practical Application ◽  
Design Error ◽  
Normal Error ◽  
Normal Error Distribution

REDUNDANCY OF MOMENT CONDITIONS AND THE EFFICIENCY OF OLS IN SUR MODELS

2008 ◽  
Vol 24 (5) ◽  
pp. 1456-1460 ◽  
Author(s):  
Hailong Qian
Keyword(s):  
Least Squares ◽  
Asymptotic Efficiency ◽  
Generalized Method Of Moments ◽  
Sufficient Conditions ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
American Statistical Association ◽  
Orthogonality Condition ◽  
Moment Conditions ◽  
Necessary And Sufficient

In this note, based on the generalized method of moments (GMM) interpretation of the usual ordinary least squares (OLS) and feasible generalized least squares (FGLS) estimators of seemingly unrelated regressions (SUR) models, we show that the OLS estimator is asymptotically as efficient as the FGLS estimator if and only if the cross-equation orthogonality condition is redundant given the within-equation orthogonality condition. Using the condition for redundancy of moment conditions of Breusch, Qian, Schmidt, and Wyhowski (1999, Journal of Econometrics 99, 89–111), we then derive the necessary and sufficient condition for the equal asymptotic efficiency of the OLS and FGLS estimators of SUR models. We also provide several useful sufficient conditions for the equal asymptotic efficiency of OLS and FGLS estimators that can be interpreted as various mixings of the two famous sufficient conditions of Zellner (1962, Journal of the American Statistical Association 57, 348–368).

scholarly journals A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2871
Author(s):  
Gaoxu Deng ◽  
Shiqian Wu ◽  
Shiyang Zhou ◽  
Bin Chen ◽  
Yucheng Liao
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Bilateral Filter ◽  
Phase Unwrapping ◽  
Noise Variance ◽  
Post Processing ◽  
Quadratic Error ◽  
Error Norm ◽  
Discontinuous Phase ◽  
Simulation Results

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.

scholarly journals The Nexus between Financial Development and Economic Growth: Panel Data Evidence from Developing Countries

2021 ◽  
Vol 14 (10) ◽  
pp. 489
Author(s):  
E. M. Ekanayake ◽  
Ranjini Thaver
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Panel Data ◽  
Least Squares ◽  
Financial Development ◽  
Panel Cointegration ◽  
Sub Saharan Africa ◽  
Explanatory Variables ◽  
Cointegration Tests ◽  
Sub Saharan

The objective of this study is to investigate the nexus between financial development (FD) in economic growth (GROWTH) in developing countries. The study uses panel data from 138 developing countries during the period 1980–2018. The relationship between financial development and economic growth is investigated using four explanatory variables that are commonly used to measure the level of financial development and several other control variables, including a dummy variable representing the financial and banking crises. The sample of 138 developing countries is also classified into six geographic regions. We have carried out panel unit-root tests and panel cointegration tests before estimating the specified models using both Panel Least Squares (Panel LS) and Panel Fully Modified Least Squares (FMOLS) methods. In addition, panel Granger causality tests have been conducted to identify the direction of causality between FD and GROWTH for each of the regions. The results of the study provide evidence of a direct relationship between FD and GROWTH in developing countries. Furthermore, there is evidence of bi-directional causality running from FD to GROWTH and from GROWTH to FD in samples of Europe and Central Asia, South Asia, and all countries, but not in East Asia and Pacific, Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa.

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