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  • 1.
    Habimana, Olivier
    Jönköping University, Jönköping International Business School, JIBS, Economics. Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali, Rwanda.
    Wavelet multiresolution analysis of the liquidity effect and monetary neutrality2019In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 53, no 1, p. 85-110Article in journal (Refereed)
    Abstract [en]

    This paper employs the maximum overlap discrete wavelet transform to obtain timescale decompositions of monetary aggregates, short-term interest rates and output to investigate two propositions in monetary economics: the liquidity effect and the long-run neutrality of money. Evidence from correlation and Granger causality over five timescales suggests that the liquidity effect is statistically significant in both the US and Sweden’s economies, with a shorter time horizon in the US than in Sweden. There is no evidence of monetary neutrality in both economies; at finest timescales, output Granger causes money in Sweden, whereas it is the other way around in the US. At long time horizons, there is a feedback between money and output in both economies. Key to our findings is that monetary disturbances have significant real effects and these effects last longer than it is assumed in real business cycle models.

  • 2.
    Karlsson, Peter
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    The Incompleteness Problem of the APT model.2011In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 38, no 2, p. 129-151Article in journal (Refereed)
    Abstract [en]

    The Arbitrage Pricing Theory provides a theory to quantify risk and the reward for taking it. While the theory itself is sound from most perspectives, its empirical version is connected with several shortcomings. One extremely delicate problem arises because the set of observable asset returns rarely has a history of complete observations. Traditionally, this problem has been solved by simply excluding assets without a complete set of observations from the analysis. Unfortunately, such a methodology may be shown to (i) lead for any fixed time period to selection bias in that only the largest companies will remain and (ii) lead to an asymptotically empty set containing no observations at all. This paper discusses some possible solutions to this problem and also provides a case study containing Swedish OMX data for demonstration.

  • 3.
    Karlsson, Peter S.
    et al.
    Departments of Economics and Statistics, Linnaeus University, Växjö, Sweden .
    Behrenz, Lars
    Departments of Economics and Statistics, Linnaeus University, Växjö, Sweden .
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Performances of Model Selection Criteria When Variables are Ill Conditioned2019In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 54, no 1, p. 77-98Article in journal (Refereed)
    Abstract [en]

    Model selection criteria are often used to find a “proper” model for the data under investigation when building models in cases in which the dependent or explained variables are assumed to be functions of several independent or explanatory variables. For this purpose, researchers have suggested using a large number of such criteria. These criteria have been shown to act differently, under the same or different conditions, when trying to select the “correct” number of explanatory variables to be included in a given model; this, unfortunately, leads to severe problems and confusion for researchers. In this paper, using Monte Carlo methods, we investigate the properties of four of the most common criteria under a number of realistic situations. These criteria are the adjusted coefficient of determination ((Formula presented.)-adj), Akaike’s information criterion (AIC), the Hannan–Quinn information criterion (HQC) and the Bayesian information criterion (BIC). The results from this investigation indicate that the HQC outperforms the BIC, the AIC and the (Formula presented.)-adj under specific circumstances. None of them perform satisfactorily, however, when the degree of multicollinearity is high, the sample sizes are small or when the fit of the model is poor (i.e., there is a low (Formula presented.). In the presence of all these factors, the criteria perform very badly and are not very useful. In these cases, the criteria are often not able to select the true model. 

  • 4.
    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).

  • 5.
    Li, Yushu
    et al.
    Center for Labor Market Policy Research (CAFO), Department of Economic and Statistics, Linnaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Testing for unit roots in panel data using a wavelet ratio method2013In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 41, no 1, p. 59-69Article in journal (Refereed)
    Abstract [en]

    For testing unit root in single time series, most tests concentrate on the time domain. Recently, Fan and Gençay (Econom Theory 26:1305–1331, 2010) proposed a wavelet ratio test which took advantage of the information from the frequency domain by using a wavelet spectrum methodology. This test shows a better power than many time domain based unit root tests including the Dickey–Fuller (J Am Stat Assoc74:427–431, 1979) type of test in the univariate time series case. On the other hand, various unit root tests in multivariate time series have appeared since the pioneering work of Levin and Lin (Unit root test in panel data: new results, University of California at San Diego, Discussion Paper, 1993). Among them, the Im–Pesaran–Shin (IPS) (J Econ 115(1):53–74, 1997) test is widely used for its straightforward implementation and robustness to heterogeneity. The IPS test is a group mean test which uses the average of the test statistics for each single series. As the test statistics in each seriescan be flexible, this paper will apply the wavelet ratio statistic to give a comparison with the test by using Dickey–Fuller t statistic in the single series. Simulation results show a gain in power by employing the wavelet ratio test instead of the Dickey–Fullert statistic in the panel data case. As the IPS test is sensitive to cross sectional dependence, we further compare the robustness of both test statistics when there exists crosscorrectional dependence among the units in the panel data. Finally we apply a residual based wavestrapping methodology to reduce the over biased size problem brought up by the cross correlation for both test statistics.

  • 6. Mantalos, Panagiotis
    et al.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    The Robustness of the RESET Test to Non-normal Error Terms2007In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 30, no 4, p. 393-408Article in journal (Refereed)
  • 7.
    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.

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