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Månsson, K., Sjölander, P. & Shukur, G. (2018). Market Concentration and Market Power of the Swedish Mortgage Sector: a Wavelet Panel Efficiency Analysis. Studies in Nonlinear Dynamics and Econometrics, 22(4)
Open this publication in new window or tab >>Market Concentration and Market Power of the Swedish Mortgage Sector: a Wavelet Panel Efficiency Analysis
2018 (English)In: Studies in Nonlinear Dynamics and Econometrics, ISSN 1081-1826, E-ISSN 1558-3708, Vol. 22, no 4Article in journal (Refereed) Published
Abstract [en]

Based on a panel wavelet efficiency analysis, we conclude that there is a systematic pattern of positive asymmetric price transmission inefficiencies in the interest rates of the largest Swedish mortgage lenders. Thus, there seems to be a higher propensity for mortgage lenders to swiftly increase their customers’ mortgage interest rates subsequent to an increase in its borrowing costs, than to decrease their customers’ mortgage rates subsequent to a corresponding decrease in the cost of borrowing. A unique contribution is our proposed wavelet method which enables a robust detection of positive asymmetric price transmission effects at various time-frequency scales, while simultaneously controlling for non-stationary trends, autocorrelation, and structural breaks. Since traditional time-series analysis methods essentially implies that several wavelet time scales are aggregated into one single time series, the blunt traditional error correction analysis totally failed to discover APT effects for this data set. In summary, using the wavelet method we show that even though the customers in the end finally will benefit from decreases in the mortgage lenders’ financing costs, the lenders wait disproportionally long before the customers’ mortgage rates are decreased.

Place, publisher, year, edition, pages
Walter de Gruyter, 2018
Keywords
APT; banking sector; interest rate; wavelet
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-37274 (URN)10.1515/snde-2016-0021 (DOI)000444539100005 ()2-s2.0-85045845294 (Scopus ID)
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-10-02Bibliographically approved
Månsson, K., Kibria, B. M., Shukur, G. & Sjölander, P. (2018). On the Estimation of the CO2 Emission, Economic Growth and Energy Consumption Nexus Using Dynamic OLS in the Presence of Multicollinearity. Sustainability, 10(5), Article ID 1315.
Open this publication in new window or tab >>On the Estimation of the CO2 Emission, Economic Growth and Energy Consumption Nexus Using Dynamic OLS in the Presence of Multicollinearity
2018 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 10, no 5, article id 1315Article in journal (Refereed) Published
Abstract [en]

This paper introduces shrinkage estimators (Ridge DOLS) for the dynamic ordinary least squares (DOLS) cointegration estimator, which extends the model for use in the presence of multicollinearity between the explanatory variables in the cointegration vector. Both analytically and by using simulation techniques, we conclude that our new Ridge DOLS approach exhibits lower mean square errors (MSE) than the traditional DOLS method. Therefore, based on the MSE performance criteria, our Monte Carlo simulations demonstrate that our new method outperforms the DOLS under empirically relevant magnitudes of multicollinearity. Moreover, we show the advantages of this new method by more accurately estimating the environmental Kuznets curve (EKC), where the income and squared income are related to carbon dioxide emissions. Furthermore, we also illustrate the practical use of the method when augmenting the EKC curve with energy consumption. In summary, regardless of whether we use analytical, simulation-based, or empirical approaches, we can consistently conclude that it is possible to estimate these types of relationships in a considerably more accurate manner using our newly suggested method.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
DOLS, EKC, Kuznets curve, MSE, Ridge
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-41154 (URN)10.3390/su10051315 (DOI)000435587100011 ()IHHÖvrigtIS (Local ID)IHHÖvrigtIS (Archive number)IHHÖvrigtIS (OAI)
Available from: 2018-08-15 Created: 2018-08-15 Last updated: 2018-08-15Bibliographically approved
Månsson, K., Shukur, G. & Kibria, B. M. (2018). Performance of Some Ridge Regression Estimators for the Multinomial Logit Model. Communications in Statistics - Theory and Methods, 47(12), 2795-2804
Open this publication in new window or tab >>Performance of Some Ridge Regression Estimators for the Multinomial Logit Model
2018 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, no 12, p. 2795-2804Article in journal (Refereed) Published
Abstract [en]

This article considers several estimators for estimating the ridge parameter k for multinomial logit model based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz et al. (2012). The mean square error (MSE) is considered as the performance criterion. A simulation study has been conducted to compare the performance of the estimators. Based on the simulation study we found that increasing the correlation between the independent variables and the number of regressors has negative effect on the MSE. However, when the sample size increases the MSE decreases even when the correlation between the independent variables is large. Based on the minimum MSE criterion some useful estimators for estimating the ridge parameter k are recommended for the practitioners

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Estimation, LSE, MSE, Multicollinearity, Multinomial Logit, Ridge Regression, Simulation, AMS
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-20958 (URN)10.1080/03610926.2013.784996 (DOI)000429361000001 ()2-s2.0-85045066903 (Scopus ID)IHHEFSIS (Local ID)IHHEFSIS (Archive number)IHHEFSIS (OAI)
Available from: 2013-04-18 Created: 2013-04-18 Last updated: 2018-04-25Bibliographically approved
Shukur, G., Sjölander, P. & Månsson, K. (2017). A new nonlinear asymmetric cointegration approach using error correction models. Communications in statistics. Simulation and computation, 46(2), 1661-1668
Open this publication in new window or tab >>A new nonlinear asymmetric cointegration approach using error correction models
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 2, p. 1661-1668Article in journal (Refereed) Published
Abstract [en]

In this article, two new powerful tests for cointegration are proposed. The general idea is based on an intuitively appealing extension of the traditional, rather restrictive cointegration concept. In this article, we allow for a nonlinear, but most importantly a different, asymmetric convergence process to account for negative and positive changes in our cointegration approach. Using Monte Carlo simulations we verify, that the estimated size of the first test depends on the unknown value of a signal-to-noise ratio q. However, our second test—which is based on the original ideas of Kanioura and Turner—is more successful and robust in the sense that it works in all of the different evaluated situations. Furthermore it is shown to be more powerful than the traditional residual based Enders and Siklos method. The new optimal test is also applied in an empirical example in order to test for potential nonlinear asymmetric price transmission effects on the Swedish power market. We find that there is a higher propensity for power retailers to rapidly and systematically increase their retail electricity prices subsequent to increases in Nordpool's wholesale prices, than there is for them to reduce their prices subsequent to a drop in wholesale spot prices.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
Cointegration, Error Correction Models, Monte Carlo Simulations, Nonlinear Asymmetric Price Transmissions, Nordpool
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-37800 (URN)10.1080/03610918.2014.999087 (DOI)000395194600054 ()2-s2.0-84994172746 (Scopus ID)
Available from: 2017-10-28 Created: 2017-10-28 Last updated: 2018-02-05Bibliographically approved
Almasri, A., Månsson, K., Sjölander, P. & Shukur, G. (2017). A wavelet-based panel unit-root test in the presence of an unknown structural break and cross-sectional dependency, with an application of purchasing power parity theory in developing countries. Applied Economics, 49(21), 2096-2105
Open this publication in new window or tab >>A wavelet-based panel unit-root test in the presence of an unknown structural break and cross-sectional dependency, with an application of purchasing power parity theory in developing countries
2017 (English)In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 49, no 21, p. 2096-2105Article in journal (Refereed) Published
Abstract [en]

This article introduces two different non-parametric wavelet-based panel unit-root tests in the presence of unknown structural breaks and cross-sectional dependencies in the data. These tests are compared with a previously suggested non-parametric wavelet test, the parameteric Im-Pesaran and Shin (IPS) test and a Wald type of test. The results from the Monte Carlo simulations clearly show that the new wavelet-ratio tests are superior to the traditional tests both in terms of size and power in panel unit-root tests because of its robustness to cross-section dependency and structural breaks. Based on an empirical Central American panel application, we can, in contrast to previous research (where bias due to structural breaks is simply disregarded), find strong, clear-cut support for purchasing power parity (PPP) in this developing region.

Place, publisher, year, edition, pages
Routledge, 2017
Keywords
cross-sectional dependency; Panel unit-root test; structural break; wavelet
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-31591 (URN)10.1080/00036846.2016.1231908 (DOI)000396793200005 ()2-s2.0-84991220454 (Scopus ID)
Available from: 2016-08-31 Created: 2016-08-31 Last updated: 2017-06-08Bibliographically approved
Shukur, G., Doszyń, M. & Dmytrów, K. (2017). Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions. Communications in statistics. Simulation and computation, 46(5), 3667-3679
Open this publication in new window or tab >>Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 5, p. 3667-3679Article in journal (Refereed) Published
Abstract [en]

The Forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g. econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U2 coefficient and unbiasedness coefficient.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
forecasting, forecasting accuracy, forecast errors distributions, forecasts, unbiasedness
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-28030 (URN)10.1080/03610918.2015.1100734 (DOI)000402090000025 ()2-s2.0-85009216498 (Scopus ID)IHHStatistikIS (Local ID)IHHStatistikIS (Archive number)IHHStatistikIS (OAI)
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2017-09-06Bibliographically approved
Shukur, G. & Zeebari, Z. (2017). On the least absolut deviation method for ridge estimation of SURE models. Communications in Statistics - Theory and Methods
Open this publication in new window or tab >>On the least absolut deviation method for ridge estimation of SURE models
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed) Accepted
National Category
Social Sciences
Identifiers
urn:nbn:se:hj:diva-37799 (URN)0000-0002-3416-5896 (DOI)
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2018-06-07
Zeebari, Z., Kibria, B. M. & Shukur, G. (2017). Seemingly unrelated regressions with covariance matrix of cross-equation ridge regression residuals. Communications in Statistics - Theory and Methods
Open this publication in new window or tab >>Seemingly unrelated regressions with covariance matrix of cross-equation ridge regression residuals
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Generalized least squares estimation of a system of seemingly unrelated regressions is usually a two-stage method: (1) estimation of cross-equation covariance matrix from ordinary least squares residuals for transforming data, and (2) application of least squares on transformed data. In presence of multicollinearity problem, conventionally ridge regression is applied at stage 2. We investigate the usage of ridge residuals at stage 1, and show analytically that the covariance matrix based on the least squares residuals does not always result in more efficient estimator. A simulation study and an application to a system of firms' gross investment support our finding.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
Multicollinearity, Ridge regression, Seemingly unrelated regressions
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-37275 (URN)10.1080/03610926.2017.1383431 (DOI)XYZ ()2-s2.0-85033673015 (Scopus ID)IHHÖvrigtIS (Local ID)IHHÖvrigtIS (Archive number)IHHÖvrigtIS (OAI)
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-06-07
Månsson, K., Kibria, B. M. & Shukur, G. (2017). Some Liu Type Estimators for the dynamic OLS estimator: With an application to the carbon dioxide Kuznets curve for Turkey. Communications in Statistics: Case Studies, Data Analysis and Applications, 3(3-4), 55-61
Open this publication in new window or tab >>Some Liu Type Estimators for the dynamic OLS estimator: With an application to the carbon dioxide Kuznets curve for Turkey
2017 (English)In: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 3, no 3-4, p. 55-61Article in journal (Refereed) Published
Abstract [en]

This paper suggests some Liu type shrinkage estimators for the dynamic ordinary least squares (DOLS) estimator that may be used to combat the multicollinearity problem. DOLS is an estimator suggested to solve the finite sample bias of OLS caused by endogeneity issue when estimating regression models based on cointegrated variables. In this paper using simulation techniques it is shown that multicollinearity and non-normality of the error term is a problem in finite samples for the DOLS model. The merit of proposed Liu type estimator are shown by means of a Monte Carlo simulation study and using an empirical application.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
Liu, DOLS, MSE
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-39151 (URN)10.1080/23737484.2018.1424589 (DOI)IHHStatisticsIS (Local ID)IHHStatisticsIS (Archive number)IHHStatisticsIS (OAI)
Available from: 2018-04-17 Created: 2018-04-17 Last updated: 2018-04-17Bibliographically approved
Sjölander, P., Månsson, K. & Shukur, G. (2017). Testing for panel cointegration in an error-correction framework with an application to the Fisher hypothesis. Communications in statistics. Simulation and computation, 46(3), 1735-1745
Open this publication in new window or tab >>Testing for panel cointegration in an error-correction framework with an application to the Fisher hypothesis
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 3, p. 1735-1745Article in journal (Refereed) Published
Abstract [en]

In this article, three innovative panel error-correction model (PECM) tests are proposed. These tests are based on the multivariate versions of the Wald (W), likelihood ratio (LR), and Lagrange multiplier (LM) tests. Using Monte Carlo simulations, the size and power of the tests are investigated when the error terms exhibit both cross-sectional dependence and independence. We find that the LM test is the best option when the error terms follow independent white-noise processes. However, in the more empirically relevant case of cross-sectional dependence, we conclude that the W test is the optimal choice. In contrast to previous studies, our method is general and does not rely on the strict assumption that a common factor causes the cross-sectional dependency. In an empirical application, our method is also demonstrated in terms of the Fisher effect—a hypothesis about the existence of which there is still no clear consensus. Based on our sample of the five Nordic countries we utilize our powerful test and discover evidence which, in contrast to most previous research, confirms the Fisher effect.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
Error correction, Fisher hypothesis, Multivariate tests, Panel cointegration, Size and power
National Category
Social Sciences
Identifiers
urn:nbn:se:hj:diva-25174 (URN)10.1080/03610918.2015.1011332 (DOI)000398114600005 ()2-s2.0-84995388285 (Scopus ID)
Available from: 2014-11-23 Created: 2014-11-23 Last updated: 2017-06-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3416-5896

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