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  • 1.
    Almasri, Abdullah
    et al.
    Department of Economics and Statistics, Karlstad University, Karlstad, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Göteborg University, Göteborg, Sweden.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    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 countries2017In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 49, no 21, p. 2096-2105Article in journal (Refereed)
    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.

  • 2.
    Andersson, Martin
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Johansson, Börje
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kristofer, Månsson
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Dynamics of Entry and Exit of Product Varieties: what evolution dynamics can account for the empirical regularities?2009Report (Other academic)
  • 3.
    Habimana, Olivier
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for nonlinear unit roots in the presence of a structural break2015In: Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday / [ed] Thomas Holgersson, Växjö: Linnaeus University Press , 2015Chapter in book (Other academic)
  • 4.
    Habimana, Olivier
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for nonlinear unit roots in the presence of a structural break with an application to the qualified PPP during the 1997 Asian financial crisis2018In: International journal of finance and economics, ISSN 1076-9307, E-ISSN 1099-1158, Vol. 23, no 3, p. 221-232Article in journal (Refereed)
    Abstract [en]

    This paper applies Monte Carlo simulations to evaluate the size and power properties in the presence of a structural break, for the standard Augmented Dickey-Fuller (ADF) test versus nonlinear exponential smooth transition autoregressive unit root tests. The break causes the tests to be undersized, and the statistical power considerably decreases. Moreover, the effect is intensified in small samples and very much increased for more persistent nonlinear series. As a remedy, we modify the standard ADF and exponential smooth transition autoregressive unit root tests in order to adjust for a structural break. This improves both the power and the size considerably, even though the empirical size still is lower than the nominal one. More persistent series are more affected by structural breaks, and the new tests are most powerful under the existence of a rather persistent nonlinear data generating process (which is an empirically relevant and common type of data generating process). The proposed tests are applied to investigate mean reversion in the real effective exchange rates of 5 East and Southeast Asian countries, taking into account the structural change in exchange rate regime brought about by the 1997 Asian financial crisis. The empirical findings corroborate our simulation results; the modified more powerful tests are able to reject the unit root in all 5 countries, whereas the tests that do not consider the structural break could only reject in one of these cases.

  • 5.
    Hacker, R Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kim, Hyunjoo
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    An Investigation of the Causal Relations between Exchange Rates and Interest Rates Differentials using Wavelets2010Report (Other academic)
    Abstract [en]

    Monthly and quarterly data for the spot exchange rate of the Swedish Krona against major currencies have been used in this paper to investigate the causality in a Granger sense at different time scales between the spot exchange rate and the nominal interest rate differential by using wavelet analysis. Impulse response functions are also utilized to examine the signs of how one of these variables affects the other over time. One key empirical finding from the causality tests is that there is only substantial evidence of a causal relationship in the long run between the two variables. When using monthly data, this is true in both directions. When considering impulse responses on how the interest rate differential affects the exchange rate, there appears to be some evidence of more negative relationships at the shorter time scales and more positive relationships at the longer time scales.

  • 6.
    Hacker, R Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kim, Hyunjoo
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Relationship between Exchange Rate and Interest Rate Differentials: A Wavelet approach2010Report (Other academic)
    Abstract [en]

    This paper uses wavelet analysis to investigate the relationship between the spot exchange rate and the interest rate differential for seven pairs of countries, with a small country, Sweden, included in each of the cases. The key empirical results show that there tends to be a negative relationship between the spot exchange rate (domestic-currency price of foreign currency) and the nominal interest rate differential (approximately the domestic interest rate minus the foreign interest rate) at the shortest time scales, while a positive relationship is shown at the longest time scales. This indicates that among models of exchange rate determination using the asset approach, the sticky-price models are supported in the short-run while in the long-run the flexible-price models appear to better explain the sign of the relationship.

  • 7.
    Hacker, R. Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kim Karlsson, Hyunjoo
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    An investigation of the causal relations between exchange rates and interest rate differentials using wavelets2014In: International Review of Economics and Finance, ISSN 1059-0560, E-ISSN 1873-8036, Vol. 29, p. 321-329Article in journal (Refereed)
    Abstract [en]

    This paper uses wavelet analysis to investigate causality between the spot exchange rate and the nominal interest rate differential for seven country pairs, which includes Sweden. Impulse response functions are also utilized to examine the signs of how one of these variables affects the other over time. One key empirical finding from the causality tests is that there is strengthening evidence of the nominal interest rate differential Granger causing the exchange rate as the wavelet time scale increases. When considering impulse responses on how the interest rate differential affects the exchange rate, there appears to be some evidence of more negative relationships at the shorter time scales (i.e. an increase in the Swedish interest rate compared to that of another country is associated with a lower Swedish krona price of the other country's currency) and more positive relationships at the longer time scales.

  • 8.
    Hacker, R. Scott
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kim Karlsson, Hyunjoo
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    The relationship between exchange rates and interest rate differentials: A wavelet approach2012In: The World Economy, ISSN 0378-5920, E-ISSN 1467-9701, Vol. 35, no 9, p. 1162-1185Article in journal (Refereed)
    Abstract [en]

    This paper uses wavelet analysis to investigate the relationship between the spot exchange rate and  interest rate differential for seven pairs of countries, with a small country, Sweden, included in each case. The key empirical results show that there tends to be a negative relationship between the spot exchange rate (domestic-currency price of foreign currency) and the nominal interest rate differential (approximately the domestic interest rate minus the foreign interest rate) at the shortest time scales, while a positive relationship is shown at the longest time scales. This indicates that among models of exchange rate determination using the asset approach, the sticky-price models are supported in the short-run and flexible-price models in the long-run.

  • 9.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Linnaeus University, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Göteborg University, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Testing for panel unit roots under general cross-sectional dependence2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 5, p. 1785-1801Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.

  • 10.
    Johansson, Börje
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Klaesson, Johan
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Wallin, Tina
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Warda, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Vad betyder högskolan för Region Jönköpings ekonomi?2014Report (Other academic)
  • 11.
    Karlsson, Hyunjoo Kim
    et al.
    Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Investigation of the nonlinear behaviour in real exchange rates in developing regions2018In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291, Vol. 25, no 5, p. 335-339Article in journal (Refereed)
    Abstract [en]

    This article examines whether the purchasing power parity (PPP) theory holds or not for the economies in different developing regions located in Africa, Asia and Latin America. In order to investigate this issue, a nonlinear panel unit root test is used to determine if some or all of the real exchange rates in a panel follow a stationary exponential smooth transition autoregressive process. By applying the nonlinear panel unit root test, our results demonstrate an empirical support for the theory of PPP for the economies in developing regions.

  • 12.
    Khalaf, Ghadban
    et al.
    Department of Mathematics, King Khalid University, Abha, Saudi Arabia.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Modified Ridge Regression Estimators2013In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 42, no 8, p. 1476-1487Article in journal (Refereed)
    Abstract [en]

    Ridge regression is a variant of ordinary multiple linear regression whose goal is to circumvent the problem of predictors collinearity. It gives up the Ordinary Least Squares (OLS) estimator as a method for estimating the parameters [] of the multiple linear regression model [] . Different methods of specifying the ridge parameter k were proposed and evaluated in terms of Mean Square Error (MSE) by simulation techniques. Comparison is made with other ridge-type estimators evaluated elsewhere. The new estimators of the ridge parameters are shown to have very good MSE properties compared with the other estimators of the ridge parameter and the OLS estimator. Based on our results from the simulation study, we may recommend the new ridge parameters to practitioners.

  • 13.
    Khalaf, Ghadban
    et al.
    Department of Mathematics, King Khalid University, Saudi Arabia.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A Tobit Ridge Regression Estimator2014In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 1, p. 131-140Article in journal (Refereed)
    Abstract [en]

    This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches we use Monte Carlo simulations where the MSE is used as performance criteria. The simulated results indicate that the RR approach should always be preferred to the ML estimation method.

  • 14.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, USA.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Performance of some logistic ridge regression estimators2012In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 40, no 4, p. 401-414Article in journal (Refereed)
    Abstract [en]

    In this paper we generalize different approaches of estimating the ridge parameter k proposed by Muniz et al. (Comput Stat, 2011) to be applicable for logistic ridge regression (LRR). These new methods of estimating the ridge parameter in LRR are evaluated by means of Monte Carlo simulations along with the some other estimators of k that has already been evaluated by Månsson and Shukur (Commun Stat Theory Methods, 2010) together with the traditional maximum likelihood (ML) approach. As a performance criterion we use the mean squared error (MSE). In the simulation study we also calculate the mean value and the standard deviation of k. The average value is interesting firstly in order to see what values of k that are reasonable and secondly if several estimators have equal variance then the estimator that induces the smallest bias should be chosen. The standard deviation is interesting as a performance criteria if several estimators of k have the same MSE, then the most stable estimator (with the lowest standard deviation) should be chosen. The result from the simulation study shows that LRR outperforms ML approach. Furthermore, some of new proposed ridge estimators outperformed those proposed by Månsson and Shukur (Commun Stat Theory Methods, 2010).

  • 15.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A simulation study of some biasing parameters for the ridge type estimation of Poisson regression2015In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 44, no 4, p. 943-957Article in journal (Refereed)
    Abstract [en]

    This paper proposes several estimators for estimating the ridge parameter k based for Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criterion are very informative because, if several estimators have an equal estimated MSE then those with low average value and standard deviation of k should be preferred. Based on the simulated results we may recommend some biasing parameters which may be useful for the practitioners in the field of health, social and physical sciences.

  • 16.
    Kibria, B. M. Golam
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Some ridge regression estimators for the zero-inflated Poisson model2013In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, p. 721-735Article in journal (Refereed)
    Abstract [en]

    The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.

  • 17.
    Kim Karlsson, Hyunjoo
    et al.
    The Linnaeus University, Växjö, Sweden.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Statistics. The Linnaeus University, Växjö, Sweden .
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Wavelet quantile analysis of asymmetric pricing on the Swedish power market2017In: Empirica, ISSN 0340-8744, E-ISSN 1573-6911, Vol. 44, no 2, p. 249-260Article in journal (Refereed)
    Abstract [en]

    In this article we investigate if the Swedish consumer prices for electricity are adjusted equally fast regardless of whether the NordPool power market prices are decreased or increased. Due to relatively moderate variations in the variables, we have applied quantile regression, since it is mainly the large changes (above the median) that essentially tend to have a considerable effect on the consumer prices. Moreover, in order to adjust for stochastic- and deterministic trends, autocorrelation, structural breaks as well as to measure APT effects in the short- and in the medium-run, we apply a wavelet decomposition approach. Our results show evidence that significantly positive asymmetric price transmission (APT) effects exist in this market. More specifically, in the short-run (based on the wavelet decomposition D1 for 1–2 months cycles), we find that that there is a higher propensity to rapidly and systematically increase the consumer prices subsequently to an increase in the NordPool market price, compared with the propensity to decrease their customers prices subsequently to a corresponding drop in the NordPool market prices. However, no significant APT effects were detected in the medium- or in the long-run (i.e. the asymmetric price transmission effects are observed only in the short-run). In summary, we could isolate significant APT effects in the short-run (1–2 months decomposition cycles), and for large changes in the dependent variable (percentiles = 0.9). Therefore, only large changes in the NordPool prices lead to feedback effects in the form of asymmetric price transmission effects. Our evidence supports the notion of firms’ downward stickiness of retail prices for maximizing profit, which are not expected to be found on a fully efficient market. Although our finding shows that the price inefficiency is short-lived, these large temporal inefficiencies are still costly for the consumers. It should be noted that blunt traditional powerless methods do not detect these APT effects, while our wavelet quantile methods are powerful and make a significant contribution in the literature by providing new empirical evidence.

  • 18.
    Locking, Håkan
    et al.
    Department of Economics and Statistics, Centre for Labour Market and Discrimination Studies, Linnaeus University, Växjö, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, p. 698-710Article in journal (Refereed)
    Abstract [en]

    In ridge regression, the estimation of the ridge parameter is an important issue. This article 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 is judged by calculating the mean squared error (MSE) using Monte 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.

  • 19.
    Locking, Håkan
    et al.
    Department of Economics and Statistics, Linnaeus University, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Ridge estimators for probit regression: With an application to labour market data2015In: Bulletin of Economic Research, ISSN 0307-3378, E-ISSN 1467-8586, Vol. 66, no S1, p. S92-S103Article in journal (Refereed)
    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.

     

  • 20.
    Mantalos, Panagiotis
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The effect of spillover on the Johansens tests for Cointegration: A Monte Carlo Analysis2010In: International Journal of Computational Economics and Econometrics, ISSN 1757-1189 (Online); 1757-1170 (Print), Vol. 1, no 3/4, p. 327-342Article in journal (Refereed)
    Abstract [en]

    This paper investigates the effect of spillover (i.e. causality in variance) on the Johansens tests for cointegration by conducting a Monte Carlo experiment where 16 different data generating processes (DGP) are used and a number of factors that might affect the properties of the Johansens cointegration tests are varied. The result from the simulation study clearly shows that spillover effect leads to an over-rejection of the true null hypothesis. Hence, in the presence of spillover it becomes very hard to make inferential statements since it will often lead to erroneous claims that cointegration relationships exist.

  • 21.
    Muniz, Gisela
    et al.
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On developing ridge regression parameters: A graphical investigation2012In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 36, no 2, p. 115-138Article in journal (Refereed)
    Abstract [en]

    In this paper we review some existing and propose some new estimators for estimating the ridge parameter. All in all 19 different estimators have been studied. The investigation has been carried out using Monte Carlo simulations. A large number of different models have been investigated where the variance of the random error, the number of variables included in the model, the correlations among the explanatory variables, the sample size and the unknown coefficient vector were varied. For each model we have performed 2000 replications and presented the results both in term of figures and tables. Based on the simulation study, we found that increasing the number of correlated variable, the variance of the random error and increasing the correlation between the independent variables have negative effect on the mean squared error. When the sample size increases the mean squared error decreases even when the correlation between the independent variables and the variance of the random error are large. In all situations, the proposed estimators have smaller mean squared error than the ordinary least squares and other existing estimators.

  • 22.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A Wavelet-Based Approach of Testing for Granger Causality in the Presence of GARCH Effects2012In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 41, no 4, p. 717-728Article in journal (Refereed)
    Abstract [en]

    The size and power of the most commonly used tests and a new wavelet-based approach of testing for Granger causality is evaluated in this paper by means of a Monte Carlo study in which the error term follows a generalized autoregressive conditional heteroscedasticity consistent (GARCH) process. In the simulation study it is shown that the commonly used causality tests tends to over-reject the true null hypothesis in the presence of GARCH errors and that the new wavelet-based approach improves the size properties of the Granger causality test for all of the different situations evaluated.

  • 23.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Developing a Liu estimator for the negative binomial regression model: method and application2013In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 9, p. 1773-1780Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new shrinkage estimator for the negative binomial regression model that is a generalization of the estimator proposed for the linear regression model by Liu [A new class of biased estimate in linear regression, Comm. Stat. Theor. Meth. 22 (1993), pp. 393–402]. This shrinkage estimator is proposed in order to solve the problem of an inflated mean squared error of the classical maximum likelihood (ML) method in the presence of multicollinearity. Furthermore, the paper presents some methods of estimating the shrinkage parameter. By means of Monte Carlo simulations, it is shown that if the Liu estimator is applied with these shrinkage parameters, it always outperforms ML. The benefit of the new estimation method is also illustrated in an empirical application. Finally, based on the results from the simulation study and the empirical application, a recommendation regarding which estimator of the shrinkage parameter that should be used is given.

  • 24.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Essays on Nonlinearities and Time Scales in Macroeconomics and Finance2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of four chapters concerning the topics of nonlinearities and time scales in economics. The focus is on market frictions and price rigidities that may cause nonlinearities and different relationships between economic variables over time. It also focuses on applying robust econometrical methods. Chapter two evaluates the size and power of some nonlinear tests for panel unit roots in the presence of cross-sectional dependency and spatial dependency. Based on the simulated results some robust tests for nonlinear panel unit roots have been found.

    Chapter three applies robust linear and nonlinear tests for panel unit roots in order to investigate the purchasing power parity theory in developing regions. The main finding is that nonlinearities is an important phenomenon in the real effective exchange rates in developing regions and that support for several regions may be found by applying the nonlinear panel unit root test. Chapter four investigates if positive asymmetric price transmission (APT) exists in the Swedish mortgage loan market. Here robust quantile regression is used on data for the Swedish SEB bank. The main contribution is that positive APT effects are found, which implies that there is a higher propensity for the bank to rapidly increase its mortgage interest rates for customers following an increase in its borrowing costs, compared with the propensity for the bank to decrease its customers’ mortgage rates subsequent to a corresponding borrowing cost decrease.

    Chapter five investigates the causal relations between exchange rates and interest rate differentials using wavelets. Also the sign of the relationship between the two variables is studied using impulse response functions. The data used is for seven country pairs in which Sweden is included in all of the different combinations. In this chapter one key empirical finding is that the causal relationship between the two variables becomes stronger as the time scale increases. The other key empirical finding is that more evidence of negative relationships is found at the shorter time scales and more positive relationships at the longer time scales.

  • 25.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Issues of multicollinearity and conditional heteroscadasticity in time series econometrics2012Doctoral thesis, comprehensive summary (Other academic)
  • 26.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Issues of multicollinearity and conditional heteroscedasticy in time series econometrics2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This doctoral thesis consists of four chapters all related to the field of time series econometrics. The main contribution is firstly the development of robust methods when testing for Granger causality in the presence of generalized autoregressive conditional heteroscedasticity (GARCH) and causality-in-variance (i.e. spillover) effects. The second contribution is the development of different shrinkage estimators for count data models which may be used when the explanatory variables are highly inter-correlated.

    The first essay investigated the effect of spillover on some tests for causality in a Granger sense. As a remedy to the problem of over-rejection caused by the spillover effects White’s heteroscedasticity consistent covariance matrix is proposed. In the second essay the effect of GARCH errors on the statistical tests for Granger causality is investigated. Here some wavelet denoising methods are proposed and by means of Monte Carlo simulations it is shown that the size properties of the tests based on wavelet filtered data is better than the ones based on raw data.

    In the third and fourth essays ridge regression estimators for the Poisson and negative binomial (NB) regression models are investigated respectively. Then finally in the fifth essaya Liu type of estimator is proposed for the NB regression model. By using Monte Carlo simulations it is shown that the estimated MSE is lower for the ridge and Liu type of estimators than maximum likelihood (ML).

  • 27.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Nonlinear Behavior in Real Effective Exchange Rates for Developing RegionsManuscript (preprint) (Other academic)
  • 28.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On ridge estimators for the negative binomial regression model2012In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 29, no 2, p. 178-184Article in journal (Refereed)
    Abstract [en]

    The negative binomial (NB) regression model is very popular in applied research when analyzing count data. The commonly used maximum likelihood (ML) estimator is very sensitive to highly intercorrelated explanatory variables. Therefore, a NB ridge regression estimator (NBRR) is proposed as a robust option of estimating the parameters of the NB model in the presence of multicollinearity. To investigate the performance of the NBRR and the traditional ML approach the mean squared error (MSE) is calculated using Monte Carlo simulations. The simulated result indicated that some of the proposed NBRR methods should always be preferred to the ML method.

  • 29.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Testing for Nonlinear Panel Unit Roots in the Presence of Cross-Sectional and Spatial DependencyManuscript (preprint) (Other academic)
  • 30.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida International University.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A restricted Liu estimator for binary regression models and its application to an applied demand system2016In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, p. 1119-1127Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

  • 31.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B M Golam
    Florida International University, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Improved Ridge Regression Estimators for Binary Choice Models: An Empirical Study2014In: International Journal of Statistics in Medical Research, ISSN 1929-6029, Vol. 3, no 3, p. 257-265Article in journal (Refereed)
    Abstract [en]

    This paper suggests some new estimators of the ridge parameter for binary choice models that may be applied in the presence of a multicollinearity problem. These new ridge parameters are functions of other estimators of the ridge parameter that have shown to work well in the previous research. Using a simulation study we investigate the mean square error (MSE) properties of these new ridge parameters and compare them with the best performing estimators from the previous research. The results indicate that we may improve the MSE properties of the ridge regression estimator by applying the proposed estimators in this paper, especially when there is a high multicollinearity between the explanatory variables and when many explanatory variables are included in the regression model. The benefit of this paper is then shown by a health related data where the effect of some risk factors on the probability of receiving diabetes is investigated.

  • 32.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On Liu Estimators for the Logit Regression Model2012In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 29, no 4, p. 1483-1488Article in journal (Refereed)
    Abstract [en]

    This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested since the mean squared error (MSE) of the commonly used maximum likelihood (ML) method becomes inflated when the explanatory variables of the regression model are highly correlated. Using MSE, the optimal value of the shrinkage parameter is derived and some methods of estimating it are proposed. It is shown by means of Monte Carlo simulations that the estimated MSE and mean absolute error (MAE) are lower for the proposed Liu estimator than those of the ML in the presence of multicollinearity. Finally the benefit of the Lie estimator is shown in an empirical application where different economic factors are used to explain the probability that municipalities have net increase of inhabitants.

  • 33.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Performance of some weighted Liu estimators for logit regression model: An application to Swedish accident data2015In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 2, p. 363-375Article in journal (Refereed)
    Abstract [en]

    In this article, we propose some new estimators for the shrinkage parameter d of the weighted Liu estimator along with the traditional maximum likelihood (ML) estimator for the logit regression model. A simulation study has been conducted to compare the performance of the proposed estimators. The mean squared error is considered as a performance criteria. The average value and standard deviation of the shrinkage parameter d are investigated. In an application, we analyze the effect of usage of cars, motorcycles, and trucks on the probability that pedestrians are getting killed in different counties in Sweden. In the example, the benefits of using the weighted Liu estimator are shown. Both results from the simulation study and the empirical application show that all proposed shrinkage estimators outperform the ML estimator. The proposed D9 estimator performed best and it is recommended for practitioners.

  • 34.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. The Linnaeus University, Department of Economics and Statistics, Växjö, Sweden.
    Some Liu Type Estimators for the dynamic OLS estimator: With an application to the carbon dioxide Kuznets curve for Turkey2017In: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 3, no 3-4, p. 55-61Article in journal (Refereed)
    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.

  • 35.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, USA.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    On the Estimation of the CO2 Emission, Economic Growth and Energy Consumption Nexus Using Dynamic OLS in the Presence of Multicollinearity2018In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 10, no 5, article id 1315Article in journal (Refereed)
    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.

  • 36.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University Miami, Florida, USA.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Improved Liu Estimators for the Poisson Regression Model2012In: International Journal of Statistics and Probability, ISSN 1927-7032, Vol. 1, no 1Article in journal (Refereed)
    Abstract [en]

    A new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalized here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML.

  • 37.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Granger Causality Test in the Presence of Spillover Effects2009In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 38, no 10, p. 2039-2059Article in journal (Refereed)
    Abstract [en]

    In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.

  • 38.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    On Ridge Parameters in Logistic Regression2011In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 40, no 18, p. 3366-3381Article in journal (Refereed)
    Abstract [en]

    This article applies and investigates a number of logistic ridge regression (RR) parameters that are estimable by using the maximum likelihood (ML) method. By conducting an extensive Monte Carlo study, the performances of ML and logistic RR are investigated in the presence of multicollinearity and under different conditions. The simulation study evaluates a number of methods of estimating the RR parameter k that has recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one RR estimator that has a lower mean squared error (MSE) than the ML method for all the different evaluated situations.

  • 39.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Performance of Some Ridge Regression Estimators for the Multinomial Logit Model2018In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, no 12, p. 2795-2804Article in journal (Refereed)
    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

  • 40.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. Centre for Labour Market Policy (CAFO), Department of Economics and Statistics, Linnaeus.
    Kibria, Barimal Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    On some Ridge Regression Estimators: A Monte Carlo simulation study under Different Error Variances2010In: Journal of Statistics, ISSN 1684-8403, Vol. 17, no 1, p. 1-22Article in journal (Refereed)
    Abstract [en]

    Following Khalaf and Shukur (2005), Alkhamisi et al. (2006) and Muniz et al. (2010), this paper considers several estimators for estimating the Ridge parameter . This paper differs from aforementioned papers in three ways: 1. the number of regressors considered is between 4 to 12 instead of 2 to 4 which are the usual practice. 2. Both mean square error (MSE) and prediction sum of square (PRESS) are considered as the performance criterion. 3. Different error variances are used (σ is between 0.5 and 5). To compare the performance of the estimators, a simulation study has been conducted. It is evident that increasing the correlation between the independent variables has negative effect on the MSE and PRESS, while increasing the number of regressors has positive effect on MSE and PRESS. When the sample size increases the MSE decreases even when the correlation between the independent variables is large. It is interesting to note that the dominance pictures of the estimators remain the same under both the MSE and PRESS criterion. However, the performance of the estimators depends on the amount of error variances specifically, when the sample sizes are small.

  • 41.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A New Asymmetric Interaction Ridge (AIR) Regression Method2014In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 3, p. 616-643Article in journal (Refereed)
    Abstract [en]

    Despite that interaction terms are standard tools of regression analysis, the side effects of the inclusion of these terms in models estimated by ordinary least squares (OLS) are yet not fully penetrated. The inclusion of interaction effects induces multicollinearity problems since all non zero values are equal between the interaction term and the regressor. In this article, we propose a procedure to remedy this problem by the use of new ridge regression (RR) shrinkage parameters—which we call the asymmetric interaction ridge (AIR) regression method. By means of Monte Carlo simulations we evaluate both OLS and AIR using the mean square error (MSE) performance criterion. The result from the simulation study confirms our hypothesis that AIR always should be preferred to OLS since it has a lower estimated MSE. Moreover, the advantages of our new method are demonstrated in an empirical application where positive asymmetric price transmission effects are exposed for the mortgage interest rates of Handelsbanken Stadshypotek. It is observed that the mortgage interest rates increase more fully and rapidly to an increase in the bank's borrowing costs than to a decrease. This asymmetry is defined as positive asymmetric price transmission (APT).

  • 42.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A New Ridge Regression Causality Test in the Presence of Multicollinearity2014In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 2, p. 235-248Article in journal (Refereed)
    Abstract [en]

    The VAR lag structure applied for the traditional Granger causality (GC) test is always severely affected by multicollinearity due to autocorrelation among the lags. Therefore, as a remedy to this problem we introduce a new Ridge Regression Granger Causality (RRGC) test, which is compared to the GC test by means of Monte Carlo simulations. Based on the simulation study we conclude that the traditional OLS version of the GC test over-rejects the true null hypothesis when there are relatively high (but empirically normal) levels of multicollinearity, while the new RRGC test will remedy or substantially decrease this problem.

  • 43.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Testing for nonlinear panel unit roots under cross-sectional dependency: with an application to the PPP hypothesis2014In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 38, p. 121-132Article in journal (Refereed)
    Abstract [en]

    In this paper we propose a number of nonlinear panel unit root tests that are robust to cross-sectional dependency. These tests may be used to test the null hypothesis of non-stationarity against the alternative that some or all of the time series in the system of equations follow a stationary exponential smooth transition autoregressive (ESTAR) process. In contrast to previous research we relax the assumption that the cross-correlation structure is driven by a common-factor and consider an endogenous correlation structure. Based on the size and power results from the Monte Carlo simulations we recommend using the Wald version of our cross-sectional dependent robust nonlinear panel unit root (CDR-NPU) method.

    Finally, in an empirical application we demonstrate that our more powerful nonlinear method, in contrast to previous methods, can provide support for PPP even in smaller samples. In consistency with the univariate tests in Bahmani-Oskooee et al. (2008) our CDR-NPU tests support the theory that less industrialized economies exhibit stronger and more distinct nonlinear adjustment patterns towards PPP.

  • 44.
    Månsson, Kristofer
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Linnaeus University, Växjö, Sweden.
    Market Concentration and Market Power of the Swedish Mortgage Sector: a Wavelet Panel Efficiency Analysis2018In: Studies in Nonlinear Dynamics and Econometrics, ISSN 1081-1826, E-ISSN 1558-3708, Vol. 22, no 4Article in journal (Refereed)
    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.

  • 45.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A poisson ridge regression estimator2011In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 28, no 4, p. 1475-1481Article in journal (Refereed)
    Abstract [en]

    The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method. The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression estimator (PRR) as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the PRR and the traditional ML approaches for estimating the parameters of the Poisson regression model, we calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR method outperforms the traditional ML estimator in all of the different situations evaluated in this paper.

  • 46.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Developing interaction shrinkage parameters for the Liu estimator — with an application to the electricity retail market2015In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 46, no 4, p. 539-550Article in journal (Refereed)
    Abstract [en]

    In this article we examine multicollinearity in the standard OLS interaction-term model—a problem often disregarded by practitioners and in previous research. As a remedy we propose a number of new shrinkage parameters based on the Liu (Commun Stat 22:393–402, 1993) estimator. Using Monte Carlo simulations, we evaluate the robustness of all models for different data-generating processes under varying conditions such as altered sample sizes and error distributions. In the simulation study it is demonstrated that the Liu estimator, which is robust to multicollinearity, systematically outperforms the traditionally applied OLS approach. The simple reason is that interaction models by definition always induce substantial multicollinearity, which in turn distorts the inference of OLS. Conversely, the Liu estimator is robust against multicollinearity in interaction-term models. The advantages of our Liu-based method are also demonstrated in practice when examining the efficiency of the Swedish power retailing market. By the use of this unique data set we find strong evidence of positive asymmetric price transmission effects. Increases in Nord Pool electricity wholesale spot prices lead to immediate and full increases in the electricity retail prices, but decreases in Nord Pool prices are not completely passed down or are delayed before being passed down to consumers. This finding suggests evidence of inefficient and unjust wealth transfers from consumers to retailers in the Swedish power market.

  • 47.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A new nonlinear asymmetric cointegration approach using error correction models2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 2, p. 1661-1668Article in journal (Refereed)
    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.

  • 48.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kekezi, Orsa
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The efficiency of the Scandinavian banking sector – a wavelet quantile regression analysis2015In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 47, no 50, p. 5378-5389Article in journal (Refereed)
    Abstract [en]

    In this article, the Scandinavian housing financing market is analysed in order to determine whether the interest rate price-discovery processes of Denmark, Norway and Sweden are efficient. Based on wavelet quantile regression analysis, we find systematic positive asymmetric price transmission (APT) inefficiencies. We conclude that there is a very high propensity for mortgage lenders to directly increase its customers’ mortgage interest rates subsequently to an increase in its borrowing costs. However, after a corresponding borrowing cost decrease, the same mortgage lenders are very slow to decrease its customers’ mortgage rates. These positive coefficients for so-called APT effects are found in all Scandinavian countries, even if the coefficients for Norway were not statistically significant. Wavelet quantile regression analysis, with a focus on the relevant higher percentiles, is easily motivated since the mortgage rates are adjusted very infrequently. Moreover, wavelet decomposition allows a robust analysis at different time frequency scales, while simultaneously controlling for nonstationary trends, autocorrelation and structural breaks. Except for the still positive but yet insignificant and inconclusive coefficients for Norway, the result is very clear-cut. Regardless of which wavelet scaling decomposition or quantile coefficient that is studied – positive APT effects are clearly identified and confirmed on the Scandinavian mortgage market.

  • 49.
    Sjölander, Pär
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Asymmetric quantile analysis of the Swedish mortgage price discovery process2013In: Applied Economics, ISSN 0003-6846, E-ISSN 1466-4283, Vol. 45, no 21, p. 3088-3101Article in journal (Refereed)
    Abstract [en]

    Based on Swedish banking data we discover robust and significantly positive Asymmetric Price Transmission (APT) effects over all analysed regression quantiles of our mortgage interest rates, with even larger positive APT for the higher percentiles. The analysis was enabled through unique access to a Swedish bank's (SEB) own records of their true borrowing costs. Our central contribution is that there is a higher propensity for the bank to rapidly increase its mortgage interest rates for customers following an increase in its borrowing costs, compared with the propensity for the bank to decrease its customers’ mortgage rates subsequent to a corresponding borrowing cost decrease.

  • 50.
    Sjölander, Pär
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics. HUI Research, Stockholm, Sweden.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. HUI Research, Stockholm, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics. HUI Research, Stockholm, Sweden; Department of Economics and Statistics, Linnaeus University, Sweden.
    Testing for panel cointegration in an error-correction framework with an application to the Fisher hypothesis2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 3, p. 1735-1745Article in journal (Refereed)
    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.

12 1 - 50 of 51
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