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Locking, Håkan
Publications (6 of 6) Show all publications
Almasri, A. & Locking, H. (2015). Forecasting risk premium using wavelet transform (1ed.). In: Thomas Holgersson (Ed.), Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday: (pp. 1-7). Linnaeus University Press
Open this publication in new window or tab >>Forecasting risk premium using wavelet transform
2015 (English)In: Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday / [ed] Thomas Holgersson, Linnaeus University Press, 2015, 1, p. 1-7Chapter in book (Other academic)
Place, publisher, year, edition, pages
Linnaeus University Press, 2015 Edition: 1
National Category
Economics Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
urn:nbn:se:lnu:diva-50784 (URN)978-91-87925-90-0 (ISBN)
External cooperation:
Available from: 2016-03-15 Created: 2016-03-15 Last updated: 2016-09-15Bibliographically approved
Locking, H., Månsson, K. & Shukur, G. (2014). Ridge estimators for probit regression: with an application to labour market data. Bulletin of Economic Research, 66(S1), S92-S103
Open this publication in new window or tab >>Ridge estimators for probit regression: with an application to labour market data
2014 (English)In: Bulletin of Economic Research, ISSN 0307-3378, E-ISSN 1467-8586, Vol. 66, no S1, p. S92-S103Article in journal (Refereed) Published
Abstract [en]

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data is collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower MSE than the ML method for all different situations that has been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba (1999) dataset which is based on a labor market experiment.

 

Place, publisher, year, edition, pages
John Wiley & Sons, 2014
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
urn:nbn:se:lnu:diva-29660 (URN)10.1111/boer.12015 (DOI)000348550700006 ()2-s2.0-84921598965 (Scopus ID)
Available from: 2013-10-16 Created: 2013-10-16 Last updated: 2020-01-24Bibliographically approved
Locking, H., Månsson, K. & Shukur, G. (2013). Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data. Communications in statistics. Simulation and computation, 42(3), 698-710
Open this publication in new window or tab >>Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data
2013 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, p. 698-710Article in journal (Refereed) Published
Abstract [en]

In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) usingMonte Carlo simulations.  In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.

Keywords
probit regression; maximum likelihood; multicollinearity; ridge regression; MSE;
National Category
Economics and Business
Research subject
Social Sciences
Identifiers
urn:nbn:se:lnu:diva-16201 (URN)10.1080/03610918.2011.654032 (DOI)000311694900014 ()2-s2.0-84870266096 (Scopus ID)
Available from: 2011-12-16 Created: 2011-12-16 Last updated: 2020-01-24Bibliographically approved
Almasri, A., Locking, H. & Shukur, G. (2013). Testing for trends and causality in Swedish environmental data, using Wavelet analysis. In: : . Paper presented at 1st Global Conference on Environmental Studies, Antalya, Turkey, April 24-26, 2013.
Open this publication in new window or tab >>Testing for trends and causality in Swedish environmental data, using Wavelet analysis
2013 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

This paper utilizes Wavelet based methodology to estimate and test for trends and granger causality in temperature andprecipitation. We use quarterly data from Sweden for the period 1884 up to 2011. The analysis suggests that temperatureand precipitation in Sweden currently have a positive trend in 2011. Thus the recent lower levels of the variables 2009-2010are estimated to be temporary fluctuations or deviations from the trend. Moreover, in the short run there are feedbackeffects between the variables and over longer periods, 4-8 years, temperature granger cause precipitation.

Keywords
Granger causality, Wavelet, Mann Kendall test, Temperature and precipitation
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
urn:nbn:se:lnu:diva-30678 (URN)
Conference
1st Global Conference on Environmental Studies, Antalya, Turkey, April 24-26, 2013
Available from: 2013-11-22 Created: 2013-11-22 Last updated: 2020-01-24Bibliographically approved
Almasri, A., Locking, H. & Shukur, G. (2009). Wavelet Based Forecasting Approach, with Application. In: Patrick Kellenberger (Ed.), 2009 International Conference on Financial Theory and Engineering: . Paper presented at ICFTE 2009 - The 2009 International Conference on Financial Theory and Engineering.
Open this publication in new window or tab >>Wavelet Based Forecasting Approach, with Application
2009 (English)In: 2009 International Conference on Financial Theory and Engineering / [ed] Patrick Kellenberger, 2009Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we outline a framework for forecasting using maximal overlap discrete wavelet transform (MODWT) based multiresoulution analysis (MRA). This framework has been applied for forecasting the tourism arrival series from Denmark to Norway. We compare forecasted values obtained from modeling the data in the time domain with the forecasted values from the wavelet domain using the traditional Box-Jenkins methodology. In both cases, diagnostic tests have been conducted to insure the specification of the model. The results have shown that the wavelet based forecasts outperforms the traditional Box-Jenkins approach in term of forecasts accuracy.

Keywords
Wavelet; MODWT; MRA; WaveForecasting.
National Category
Economics Probability Theory and Statistics
Research subject
Economy, Economics; Statistics/Econometrics
Identifiers
urn:nbn:se:vxu:diva-6785 (URN)
Conference
ICFTE 2009 - The 2009 International Conference on Financial Theory and Engineering
Available from: 2010-01-18 Created: 2010-01-18 Last updated: 2020-01-24Bibliographically approved
Almasri, A., Locking, H. & Shukur, G. (2008). Testing for Climate Change in Sweden During 1850-1999, using Wavelet analysis. Journal of Applied Statistics, 35(4)
Open this publication in new window or tab >>Testing for Climate Change in Sweden During 1850-1999, using Wavelet analysis
2008 (English)In: Journal of Applied Statistics, Vol. 35, no 4Article in journal (Other (popular science, discussion, etc.)) Published
Research subject
Economy, Economics
Identifiers
urn:nbn:se:vxu:diva-3313 (URN)
Available from: 2008-03-03 Created: 2008-03-03 Last updated: 2020-01-24Bibliographically approved
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