Publications - Morten Ø. Nielsen
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Bcontroller%5D=Publications&cHash=7c86b23862cfeb79a84b72b01fcee213
en-usPURE Extensiontypo3support@science.au.dk (Web Department)30<![CDATA[Truncated sum-of-squares estimation of fractional time series models with generalized power law trend]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=21fa2c68-7e52-4399-8521-15622d7e569e&tx_pure_pure5%5BshowType%5D=pub&cHash=fc291c0cad9d63d7951558e1f31ebcac
Hualde, J., Nielsen, M. Ø.We consider truncated (or conditional) sum-of-squares estimation of a parametric fractional time series model with an additive deterministic structure. The latter consists of both a drift term and a generalized power law trend. The memory parameter of the stochastic component and the power parameter of the deterministic trend component are both considered unknown real numbers to be estimated and belonging to arbitrarily large compact sets. Thus, our model captures different forms of nonstationarity and noninvertibility as well as a very flexible deterministic specification. As in related settings, the proof of consistency (which is a prerequisite for proving asymptotic normality) is challenging due to non-uniform convergence of the objective function over a large admissible parameter space and due to the competition between stochastic and deterministic components. As expected, parameter estimates related to the deterministic component are shown to be consistent and asymptotically normal only for parts of the parameter space depending on the relative strength of the stochastic and deterministic components. In contrast, we establish consistency and asymptotic normality of parameter estimates related to the stochastic component for the entire parameter space. Furthermore, the asymptotic distribution of the latter estimates is unaffected by the presence of the deterministic component, even when this is not consistently estimable. We also include Monte Carlo simulations to illustrate our results.]]>ResearchSat, 01 Jan 2022 17:28:57 +010021fa2c68-7e52-4399-8521-15622d7e569e<![CDATA[Adaptive Inference in Heteroscedastic Fractional Time Series Models]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=271f330f-22fd-413c-8b7b-fc46218006f3&tx_pure_pure5%5BshowType%5D=pub&cHash=f222559f32e7174130d7e7baaf2b2448
Cavaliere, G., Nielsen, M. Ø., Robert Taylor, A. M.We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroscedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in such cases, unconditional heteroscedasticity inflates its variance matrix by a scalar quantity, λ > 1, thereby inducing a loss in efficiency relative to the unconditionally homoscedastic case, λ = 1. We propose an adaptive version of the CSS estimator, based on nonparametric kernel-based estimation of the unconditional volatility process. We show that adaptive estimation eliminates the factor λ from the variance matrix, thereby delivering the same asymptotic efficiency as that attained by the standard CSS estimator in the unconditionally homoscedastic case and, hence, asymptotic efficiency under Gaussianity. Importantly, the asymptotic analysis is based on a novel proof strategy, which does not require consistent estimation (in the sup norm) of the volatility process. Consequently, we are able to work under a weaker set of assumptions than those employed in the extant literature. The asymptotic variance matrices of both the standard and adaptive CSS (ACSS) estimators depend on any weak parametric autocorrelation present in the fractional model and any conditional heteroscedasticity in the shocks. Consequently, asymptotically pivotal inference can be achieved through the development of confidence regions or hypothesis tests using either heteroscedasticity-robust standard errors and/or a wild bootstrap. Monte Carlo simulations and empirical applications illustrate the practical usefulness of the methods proposed.]]>ResearchSat, 01 Jan 2022 17:28:57 +0100271f330f-22fd-413c-8b7b-fc46218006f3<![CDATA[To infinity and beyond]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=3ea7b83f-cc3d-4ed5-b5f4-71d42a89ce2a&tx_pure_pure5%5BshowType%5D=pub&cHash=5f9e0d76a686536579ba683180f530f9
Nielsen, M. O., Noel, A. L.This article provides an exact algorithm for efficient computation of the time series of conditional variances, and hence the likelihood function, of models that have an ARCH(infinity) representation. This class of models includes, for example, the fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model. Our algorithm is a variation of the fast fractional difference algorithm of Jensen, A.N. and M.o. Nielsen (2014), Journal of Time Series Analysis 35, 428-436. It takes advantage of the fast Fourier transform (FFT) to achieve an order of magnitude improvement in computational speed. The efficiency of the algorithm allows estimation (and simulation/bootstrapping) of ARCH(infinity) models, even with very large data sets and without the truncation of the filter commonly applied in the literature. In Monte Carlo simulations, we show that the elimination of the truncation of the filter reduces the bias of the quasi-maximum-likelihood estimators and improves out-of-sample forecasting. Our results are illustrated in two empirical examples.]]>ResearchSat, 01 May 2021 17:28:57 +02003ea7b83f-cc3d-4ed5-b5f4-71d42a89ce2a<![CDATA[Semiparametric tests for the order of integration in the possible presence of level breaks]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=16b7551b-c0c3-42a2-90ad-6cadb7b67e68&tx_pure_pure5%5BshowType%5D=pub&cHash=f873fe76325b25b393e1810ee19f427c
Iacone, F., Nielsen, M. Ø., Taylor, R.Lobato and Robinson developed semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or I(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalized to allow for testing of the null hypothesis that a series is (Formula presented.) for any δ lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well-known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodeled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem, we extend the Lobato and Robinson approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behavior of our proposed tests, as well as several empirical examples.]]>ResearchFri, 01 Apr 2022 17:28:57 +020016b7551b-c0c3-42a2-90ad-6cadb7b67e68<![CDATA[Truncated sum of squares estimation of fractional time series models with deterministic trends]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=17256527-9965-46c3-901d-bdc1aaf94f79&tx_pure_pure5%5BshowType%5D=pub&cHash=04eb945e2afc56ac07b9984a89edefd6
Hualde, J., Nielsen, M. Ø.We consider truncated (or conditional) sum of squares estimation of a parametric model composed of a fractional time series and an additive generalized polynomial trend. Both the memory parameter, which characterizes the behavior of the stochastic component of the model, and the exponent parameter, which drives the shape of the deterministic component, are considered not only unknown real numbers but also lying in arbitrarily large (but finite) intervals. Thus, our model captures different forms of nonstationarity and noninvertibility. As in related settings, the proof of consistency (which is a prerequisite for proving asymptotic normality) is challenging due to nonuniform convergence of the objective function over a large admissible parameter space, but, in addition, our framework is substantially more involved due to the competition between stochastic and deterministic components. We establish consistency and asymptotic normality under quite general circumstances, finding that results differ crucially depending on the relative strength of the deterministic and stochastic components. Finite-sample properties are illustrated by means of a Monte Carlo experiment.]]>ResearchSat, 01 Aug 2020 17:28:57 +020017256527-9965-46c3-901d-bdc1aaf94f79<![CDATA[Wild Bootstrap and Asymptotic Inference With Multiway Clustering]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=5fd2d9b5-311f-4978-b74b-6765c0b73756&tx_pure_pure5%5BshowType%5D=pub&cHash=fb7f43415129f8e0204951d922c60532
MacKinnon, J. G., Nielsen, M. Ø., Webb, M. D.We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which t-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of t-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the t-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.]]>ResearchFri, 01 Jan 2021 17:28:57 +01005fd2d9b5-311f-4978-b74b-6765c0b73756<![CDATA[The cointegrated vector autoregressive model with general deterministic terms]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=2d3683e9-843f-4fe6-a8bb-45b5b35b53b7&tx_pure_pure5%5BshowType%5D=pub&cHash=656ee7571ff8ef909ea315280f083a21
Johansen, S., Nielsen, M. Ø.In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X-t = gamma Z(t)+Y-t, where Z(t) belongs to a large class of deterministic regressors and Y-t is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression, and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are x(2)-distributed. (C) 2017 Elsevier B.V. All rights reserved.]]>ResearchMon, 01 Jan 2018 17:28:57 +01002d3683e9-843f-4fe6-a8bb-45b5b35b53b7<![CDATA[Fast and wild]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=20a741c8-2ff4-4f96-9b75-ea83145550b6&tx_pure_pure5%5BshowType%5D=pub&cHash=e85d0b4397853ad739b4cc7bb2ed91e8
Roodman, G. D., MacKinnon, J. G., Nielsen, M. Ø., Webb, M. D.ResearchTue, 01 Jan 2019 17:28:57 +010020a741c8-2ff4-4f96-9b75-ea83145550b6<![CDATA[Asymptotic theory and wild bootstrap inference with clustered errors]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=57e744d3-ba53-43ba-b373-ed9f7b332b71&tx_pure_pure5%5BshowType%5D=pub&cHash=2c419943e0c9bc3eec71834be559167c
Djogbenou, A. A., MacKinnon, J. G., Nielsen, M. Ø.ResearchTue, 01 Oct 2019 17:28:57 +020057e744d3-ba53-43ba-b373-ed9f7b332b71<![CDATA[Improved likelihood ratio tests for cointegration rank in the VAR model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=406ec12f-d9d8-447b-886e-83fd163e31e7&tx_pure_pure5%5BshowType%5D=pub&cHash=4f937c48ac50b3bb0d0b859e3202ec41
Boswijk, H. P., Jansson, M., Nielsen, M. Ø.ResearchThu, 01 Jan 2015 17:28:57 +0100406ec12f-d9d8-447b-886e-83fd163e31e7<![CDATA[Nonstationary Cointegration in the Fractionally Cointegrated VAR Model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=c181a0cc-c5be-4eb7-8b5e-87a01594c0f6&tx_pure_pure5%5BshowType%5D=pub&cHash=80454934b5693f69abf4f4a5ee04de1f
Johansen, S., Nielsen, M. Ø.We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and make two distinct contributions. First, in their consistency proof, Johansen and Nielsen (2012a) imposed moment conditions on the errors that depend on the parameter space, such that when the parameter space is larger, stronger moment conditions are required. We show that these moment conditions can be relaxed, and for consistency we require just eight moments regardless of the parameter space. Second, Johansen and Nielsen (2012a) assumed that the cointegrating vectors are stationary, and we extend the analysis to include the possibility that the cointegrating vectors are non-stationary. Both contributions require new analysis and results for the asymptotic properties of the likelihood function of the fractional CVAR model, which we provide. Finally, our analysis follows recent research and applies a parameter space large enough that the usual (non-fractional) CVAR model constitutes an interior point and hence can be tested against the fractional model using a Chi-squared-test.]]>ResearchMon, 01 Jul 2019 17:28:58 +0200c181a0cc-c5be-4eb7-8b5e-87a01594c0f6<![CDATA[Testing the CVAR in the Fractional CVAR Model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=78b28496-cad1-4335-a840-eb5bb5d1d9cb&tx_pure_pure5%5BshowType%5D=pub&cHash=33cd0ceb1d97b7a5774be94819522b31
Johansen, S., Nielsen, M. Ø.We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and show that the likelihood ratio test statistic for the usual CVAR model is asymptotically chi-squared-distributed. Because the usual CVAR model lies on the boundary of the parameter space for the fractional CVAR in Johansen and Nielsen (2012a), the analysis requires the study of the fractional CVAR model on a slightly larger parameter space so that the CVAR model lies in the interior. This in turn implies some further analysis of the asymptotic properties of the fractional CVAR model.]]>ResearchThu, 01 Nov 2018 17:28:58 +010078b28496-cad1-4335-a840-eb5bb5d1d9cb<![CDATA[Economic significance of commodity return forecasts from the fractionally cointegrated VAR model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=b8d74d32-1581-4904-9ff7-57f72aa8ddaf&tx_pure_pure5%5BshowType%5D=pub&cHash=fd47d154ce4d0f28e02c5e03df320c53
Dolatabadi, S., Narayan, P. K., Nielsen, M. Ø., Xu, K.We model and forecast commodity spot and futures prices using fractionally cointegrated vector autoregressive (FCVAR) models generalizing the well-known (non-fractional) CVAR model to accommodate fractional integration. In our empirical analysis to daily data on 17 commodity markets, the fractional model is statistically superior in terms of in-sample fit and out-of-sample forecasting. We analyze economic significance of the forecasts through dynamic (mean-variance) trading strategies, leading to statistically significant and economically meaningful profits in most markets. We generally find that the fractional model generates higher profits on average, especially in the futures markets.]]>ResearchThu, 01 Feb 2018 17:28:58 +0100b8d74d32-1581-4904-9ff7-57f72aa8ddaf<![CDATA[Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=8ea3bc2c-f668-4713-9abe-0e62d8a75f53&tx_pure_pure5%5BshowType%5D=pub&cHash=8ef87c3b1786ec689c0f67c62e11dc3d
Nielsen, M. Ø., Shibaev, S. S.We examine forecasting performance of the recent fractionally cointegrated vector auto-regressive (FCVAR) model. We use daily polling data of political support in the UK for 2010–2015 and compare with popular competing models at several forecast horizons. Our findings show that the four variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated vector auto-regressive model at all forecast horizons. The relative forecast improvement is higher at longer forecast horizons, where the root-mean-squared forecast error of the FCVAR model is up to 15% lower than that of the univariate fractional models and up to 20% lower than that of the cointegrated vector auto-regressive model. In an empirical application to the 2015 UK general election, the estimated common stochastic trend from the model follows the vote share of the UK Independence Party very closely, and we thus interpret it as a measure of Euroscepticism in public opinion rather than an indicator of the more traditional left–right political spectrum. In terms of prediction of vote shares in the election, forecasts generated by the FCVAR model leading to the election appear to provide a more informative assessment of the current state of public opinion on electoral support than the hung Parliament prediction of the opinion poll.]]>ResearchMon, 01 Jan 2018 17:28:58 +01008ea3bc2c-f668-4713-9abe-0e62d8a75f53<![CDATA[Quasi-maximum likelihood estimation and bootstrap inference in fractional time series models with heteroskedasticity of unknown form]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=b2aade32-24bd-4695-bb17-12ba1dbdf40e&tx_pure_pure5%5BshowType%5D=pub&cHash=9c38aa08b6112611fb9e9c78ea157db5
Cavaliere, G., Nielsen, M. Ø., Taylor, A.M. R.ResearchSun, 01 Jan 2017 17:28:58 +0100b2aade32-24bd-4695-bb17-12ba1dbdf40e<![CDATA[A fractionally cointegrated VAR model with deterministic trends and application to commodity futures markets]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=e9bf9641-8304-468a-a756-87664f7ee780&tx_pure_pure5%5BshowType%5D=pub&cHash=f4affd37095016bb91867f8d005ceeb9
Dolatabadi, S., Nielsen, M. Ø., Xu, K.ResearchFri, 01 Jan 2016 17:28:58 +0100e9bf9641-8304-468a-a756-87664f7ee780<![CDATA[The role of initial values in conditional sum-of-squares estimation of nonstationary fractional time series models]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=a64b3061-d271-4fd9-bbda-6345cfd8cf5e&tx_pure_pure5%5BshowType%5D=pub&cHash=83557880dd17bba74a4cbc18498207b8
Johansen, S., Nielsen, M. Ø.ResearchFri, 01 Jan 2016 17:28:58 +0100a64b3061-d271-4fd9-bbda-6345cfd8cf5e<![CDATA[Asymptotics for the Conditional-Sum-of-Squares Estimator in Multivariate Fractional Time-Series Models]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=3c435973-dfbb-49a2-8b23-069873cf0bd1&tx_pure_pure5%5BshowType%5D=pub&cHash=dda922795ee3ec00df1b69d4e7c76326
Nielsen, M. Ø.to the conditional maximum likelihood estimator, in multivariate fractional time-series models. The model is parametric and quite general and, in particular, encompasses the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probability, thus making the proof much more challenging than usual. The neighbourhood around the critical point where uniform convergence fails is handled using a truncation argument]]>ResearchThu, 01 Jan 2015 17:28:58 +01003c435973-dfbb-49a2-8b23-069873cf0bd1<![CDATA[Numerical distribution functions of fractional unit root and cointegration tests]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=79a29660-5145-4b5b-9cb5-444888ef7c24&tx_pure_pure5%5BshowType%5D=pub&cHash=59870c76ff3fbee87d950fbfc09461fa
Mackinnon, J. G., Nielsen, M. Ø.ResearchWed, 01 Jan 2014 17:28:58 +010079a29660-5145-4b5b-9cb5-444888ef7c24<![CDATA[The impact of financial crises on the risk-return tradeoff and the leverage effect]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=6f852522-c1e1-41be-8696-58fc393d24d6&tx_pure_pure5%5BshowType%5D=pub&cHash=242e2d3662f617ce59e6f740784c6f38
Christensen, B. J., Nielsen, M. Ø., Zhu, J.ResearchThu, 01 Jan 2015 17:28:58 +01006f852522-c1e1-41be-8696-58fc393d24d6<![CDATA[A fast fractional difference algorithm]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=12b80ce7-f895-4b21-9e0f-a0eec977d08f&tx_pure_pure5%5BshowType%5D=pub&cHash=9cb1fc42369bca321c6cac6fb77229ba
Jensen, A. N., Nielsen, M. Ø.We provide a fast algorithm for calculating the fractional difference of a time series. In standard implementations, the calculation speed (number of arithmetic operations) is of order T^{2}, where T is the length of the time series. Our algorithm allows calculation speed of order TlogT. For moderate and large sample sizes, the difference in computation time is substantial.]]>ResearchWed, 01 Jan 2014 17:28:58 +010012b80ce7-f895-4b21-9e0f-a0eec977d08f<![CDATA[A fractionally cointegrated VAR analysis of economic voting and political support]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=956d6e3e-4a4b-46d5-908e-cb57ff199d83&tx_pure_pure5%5BshowType%5D=pub&cHash=237723cfaa5c36f1f8b58628f429db53
Jones, M. E. C., Nielsen, M. O., Popiel, M. K.We use a fractionally cointegrated vector autoregressive model to examine the relationship between Canadian political support and macroeconomic conditions. This model is well suited for the analysis because it allows multiple fractional time series and admits simple asymptotic inference for the model parameters and tests of the hypotheses of interest. In the long-run equilibrium, we find that support for the Progressive Conservative Party was higher during periods of high interest rates and low unemployment, while support for the Liberal Party was higher during periods of low interest rates and high unemployment. We also test and reject the notion that party support is driven only by relative (to the United States) economic performance. Indeed, our findings suggest that US macroeconomic variables do not enter the long-run equilibrium of Canadian economic voting (political opinion poll support) at all.]]>ResearchSun, 01 Nov 2015 17:28:58 +0100956d6e3e-4a4b-46d5-908e-cb57ff199d83<![CDATA[A necessary moment condition for the fractional functional central limit theorem]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=85b1891b-1347-4582-ab24-86f83c74abff&tx_pure_pure5%5BshowType%5D=pub&cHash=a51f76f6932c9e3e59a335168164ec8a
Johansen, S., Nielsen, M. Ø.ResearchFri, 01 Jun 2012 17:28:58 +020085b1891b-1347-4582-ab24-86f83c74abff<![CDATA[Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=f81aee79-8220-4615-8040-068a75799980&tx_pure_pure5%5BshowType%5D=pub&cHash=c154c8e8b2cd5c3fa6e433184048ef99
Johansen, S., Nielsen, M. Ø.ResearchThu, 01 Nov 2012 17:28:58 +0100f81aee79-8220-4615-8040-068a75799980<![CDATA[Nearly efficient likelihood ratio tests of the unit root hypothesis]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=e94674ed-b351-41ca-9eee-d0392c9fda47&tx_pure_pure5%5BshowType%5D=pub&cHash=d6008e8a09277f7cb1cacd56380cc9d1
Jansson, M., Nielsen, M. Ø.ResearchSat, 01 Sep 2012 17:28:58 +0200e94674ed-b351-41ca-9eee-d0392c9fda47<![CDATA[Local polynomial Whittle estimation of perturbed fractional processes]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=d3e74ebb-3a47-4a43-b8a0-2b090c187f51&tx_pure_pure5%5BshowType%5D=pub&cHash=9d6a436bbc199e420d8d38af9a04254a
Frederiksen, P., Nielsen, F. S., Nielsen, M. Ø.ResearchSun, 01 Jan 2012 17:28:58 +0100d3e74ebb-3a47-4a43-b8a0-2b090c187f51<![CDATA[Likelihood inference for a nonstationary fractional autoregressive model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=52a1a9fd-cb24-429f-ac80-52a4a1faeaa7&tx_pure_pure5%5BshowType%5D=pub&cHash=b6f2d3a10cd06a3c2b1b4dfa76693f01
Johansen, S., Ørregård Nielsen, M.We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including d and b, and prove that they converge in distribution. We use the results to prove consistency of the maximum likelihood estimator for d,b in a large compact subset of {1/2]]>ResearchFri, 01 Jan 2010 17:28:58 +010052a1a9fd-cb24-429f-ac80-52a4a1faeaa7<![CDATA[A Vector Autoregressive Model for Electricity Prices subject to Long Memory and Regime Switching]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=6cd36930-2467-11df-b95d-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=fef978c503ef601f14647a1397a54936
Haldrup, N., Nielsen, F., Nielsen, M. Ø.ResearchFri, 01 Jan 2010 17:28:58 +01006cd36930-2467-11df-b95d-000ea68e967b<![CDATA[A powerful test of the autoregressive unit root hypothesis based on a tuning parameter free statistic]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=b6331030-2070-11df-b95d-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=e5e1b2ed7fad1e8ff1e74115a86a1160
Nielsen, M. Ø.ResearchThu, 01 Jan 2009 17:28:58 +0100b6331030-2070-11df-b95d-000ea68e967b<![CDATA[Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=212257c0-9ceb-11de-a092-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=e4168a6246336a1ac540425767e68f7a
Christensen, B. J., Nielsen, M. Ø., Zhu, J.ResearchFri, 01 Jan 2010 17:28:58 +0100212257c0-9ceb-11de-a092-000ea68e967b<![CDATA[The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=18636a70-6cc1-11de-b2cc-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=1ac2f93f75b6d99be8cbe986fa55c252
Busch, T., Christensen, B. J., Nielsen, M. Ø.ResearchSat, 01 Jan 2011 17:28:58 +010018636a70-6cc1-11de-b2cc-000ea68e967b<![CDATA[The Effect of Long Memory in Volatility on Stock Market Fluctuations]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=8a7e1c10-d576-11dc-bc43-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=ef99e9a6aacd7b23f10457c01fa54e28
Christensen, B. J., Nielsen, M. Ø.ResearchMon, 01 Jan 2007 17:28:58 +01008a7e1c10-d576-11dc-bc43-000ea68e967b<![CDATA[Asymptotic Normality of Narrow-Band Least Squares in the Stationary Fractional Cointegration Model and Volatility Forecasting]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=a9462490-bd3e-11db-bee9-02004c4f4f50&tx_pure_pure5%5BshowType%5D=pub&cHash=69c9b187963e3e477718deffc7f3a4ae
Christensen, B. J., Nielsen, M. Ø.ResearchSun, 01 Jan 2006 17:28:58 +0100a9462490-bd3e-11db-bee9-02004c4f4f50<![CDATA[ A regime switching long memory model for electricity prices]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=97f60900-bff8-11db-bee9-02004c4f4f50&tx_pure_pure5%5BshowType%5D=pub&cHash=0958294a3eb93f1903b9b4b992788311
Haldrup, N., Nielsen, M. Ø.ResearchSun, 01 Jan 2006 17:28:58 +010097f60900-bff8-11db-bee9-02004c4f4f50<![CDATA[Directional Congestion and regime switching in a long memory model for electricity prices]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=a3796c40-bff8-11db-bee9-02004c4f4f50&tx_pure_pure5%5BshowType%5D=pub&cHash=affc96530a0ded1fc2e8a5c2bdba8f80
Haldrup, N., Nielsen, M. Ø.ResearchSun, 01 Jan 2006 17:28:58 +0100a3796c40-bff8-11db-bee9-02004c4f4f50<![CDATA[Estimation of fractional integration and cointegration in the presence of data noise]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=2f55af50-bffc-11db-bee9-02004c4f4f50&tx_pure_pure5%5BshowType%5D=pub&cHash=a6183147110ecbadd02826e8e3214680
Haldrup, N., Nielsen, M. Ø.ResearchMon, 01 Jan 2007 17:28:58 +01002f55af50-bffc-11db-bee9-02004c4f4f50<![CDATA[Seasonality in Economic Models]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=fa869400-af80-11db-bee9-02004c4f4f50&tx_pure_pure5%5BshowType%5D=pub&cHash=6f55dcf1dfc7cf33d1a100038eb4b3cd
Brendstrup, B., Hylleberg, S., Nielsen, M. Ø., Skipper, L., Stentoft, L.ResearchThu, 01 Jan 2004 17:28:58 +0100fa869400-af80-11db-bee9-02004c4f4f50<![CDATA[Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration]]>
https://econ.au.dk/research/researchcentres/creates/people/research-fellows/morten-oe-nielsen?tx_pure_pure5%5Baction%5D=single&tx_pure_pure5%5Bcontroller%5D=Publications&tx_pure_pure5%5Bid%5D=dda35270-bd81-11da-ad69-000ea68e967b&tx_pure_pure5%5BshowType%5D=pub&cHash=b5d6fb37773b1be8bef909f2fdee8436
Nielsen, M. Ø., Frederiksen, P. H.ResearchSat, 01 Jan 2005 17:28:58 +0100dda35270-bd81-11da-ad69-000ea68e967b