M. Ooms (Marius)
http://repub.eur.nl/ppl/1252/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryExponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments
http://repub.eur.nl/pub/20284/
Fri, 01 Oct 2010 00:00:01 GMT<div>S.J. Koopman</div><div>M. Ooms</div>
Generalizations of the KPSS-test for Stationarity
http://repub.eur.nl/pub/13345/
Mon, 01 Nov 2004 00:00:01 GMT<div>B. Hobijn</div><div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
We propose automatic generalizations of the KPSS-test for the null hypothesis of stationarity of a univariate time series. We can use these tests for the null hypotheses of trend stationarity, level stationarity and zero mean stationarity. We introduce the asymptotic null distributions and we determine consistency against relevant nonstationary alternatives. We compare the properties of the tests with those of other proposed tests for stationarity. Monte Carlo simulations support the relevance of the tests when an autoregressive process with large positive autocorrelations is likely under the null hypothesis.Did men of taste and civilization save the stage? Theatre-going in Rotterdam, 1860-1916. A statistical analysis of ticket sales
http://repub.eur.nl/pub/2164/
Wed, 01 Jan 2003 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>H. Gras</div><div>M. Ooms</div>
This essay deals with Dutch theater history of the second half of the 19th century (1860—1916). It statistically tests, whether the dominant opinion in Dutch theater writing, that after 1870 the stage recovered from a half century of decline, due to a renewed interest in it by the city elite, occupying the first ranks with a taste for civilized modern drama, and that, hence, a sharp cleft became visible between lower-rank tastes and upper-rank tastes. We test the tenability of this position on the basis of the Rotterdam Grand Theater archives, which contain ticket sales per rank per performance from 1776 till 1916, and the play bills of the performances. We analyze aggregated behavior of an anonymous theater consumers subdivided into price classes, hypothesizing that differences in attendance to high and low quality plays (as the critics judged them) over the different ranks, might reveal class-based divisions of taste. A long-memory time series analysis confirms that there is a significant gradual change of quality in the theater during the period 1860—1881, but this change is hardly rank- (and by implication likely class-) based. A second time series analysis, analyzing the impact of the repertoire and companies controlled for season and dynamics of the time series over the years 1860—1887 and 1887—1916, hardly sustains the narrative of recovery for most products as related to ranks. Only in a few telling instances, there was a clear opposition between low-rank tastes and upper-rank tastes. Hence, the recovery thesis must on the whole be rejected. This research will be followed-up by a prosopographical analysis of season-ticket and coupon holders in the Rotterdam theaters from 1773—1916, in which more detailed information on the social backgrounds and particularly on social class division of not anonymous theater audiences in `the long 19th century' is central.Loss of HR6B ubiquitin-conjugating activity results in damaged synaptonemal complex structure and increased crossing-over frequency during the male meiotic prophase.
http://repub.eur.nl/pub/3207/
Wed, 01 Jan 2003 00:00:01 GMT<div>W.M. Baarends</div><div>E. Wassenaar</div><div>J.W. Hoogerbrugge</div><div>W.A. van Cappellen</div><div>H.P. Roest</div><div>M. Ooms</div><div>J.H.J. Hoeijmakers</div><div>J.A. Grootegoed</div><div>J.T.M. Vreeburg</div>
The ubiquitin-conjugating enzymes HR6A and HR6B are the two mammalian homologs of Saccharomyces cerevisiae RAD6. In yeast, RAD6 plays an important role in postreplication DNA repair and in sporulation. HR6B knockout mice are viable, but spermatogenesis is markedly affected during postmeiotic steps, leading to male infertility. In the present study, increased apoptosis of HR6B knockout primary spermatocytes was detected during the first wave of spermatogenesis, indicating that HR6B performs a primary role during the meiotic prophase. Detailed analysis of HR6B knockout pachytene nuclei showed major changes in the synaptonemal complexes. These complexes were found to be longer. In addition, we often found depletion of synaptonemal complex proteins from near telomeric regions in the HR6B knockout pachytene nuclei. Finally, we detected an increased number of foci containing the mismatch DNA repair protein MLH1 in these nuclei, reflecting a remarkable and consistent increase (20 to 25%) in crossing-over frequency. The present findings reveal a specific requirement for the ubiquitin-conjugating activity of HR6B in relation to dynamic aspects of the synaptonemal complex and meiotic recombination in spermatocytes.Inflation, forecast intervals and long memory regression models
http://repub.eur.nl/pub/2162/
Tue, 16 Apr 2002 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>C.S. Bos</div><div>M. Ooms</div>
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to 2 years. Correcting for the effect of explanatory variables, we still find fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s. We compare the forecasts of ARFIMAX models and ARIMAX models over the period 1984–1999. The ARIMAX(1, 1, 1) model provides the best forecasts, but its multi-step forecast intervals are too large. The multi-step forecast intervals of the ARFIMAX(0, d, 0) model prove to be more realistic.A seasonal periodic long memory model for monthly river flows
http://repub.eur.nl/pub/13525/
Mon, 03 Sep 2001 00:00:01 GMT<div>M. Ooms</div><div>Ph.H.B.F. Franses</div>
Based on simple time series plots and periodic sample autocorrelations, we document that monthly river flow data displays long memory, in addition to pronounced seasonality. In fact, it appears that the long memory characteristics vary with the season. To describe these two properties jointly, we propose a seasonal periodic long memory model and fit it to the well-known Fraser river data (to be obtained from Statlib at http://lib.stat.cm.edu/datasets/). We provide a statistical analysis and provide impulse response functions to show that shocks in certain months of the year have a longer lasting impact than those in other months.Inflation, Forecast Intervals and Long Memory Regression Models
http://repub.eur.nl/pub/6874/
Fri, 23 Feb 2001 00:00:01 GMT<div>C.S. Bos</div><div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years. Even after correcting for the effect of explanatory variables, there is conclusive evidence of both fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s and we incorporate these breaks in the forecasting model for the 1980s and 1990s. We compare the results of the fractionally integrated ARFIMA(0,d,0) model with those for ARIMA(1,d,1) models with fixed order of d=0 and d=1 for inflation. Comparing mean squared forecast errors, we find that the ARMA(1,1) model performs worse than the other models over our evaluation period 1984-1999. The ARIMA(1,1,1) model provides the best forecasts, but its multi-step forecast intervals are too large.Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation
http://repub.eur.nl/pub/1619/
Wed, 08 Dec 1999 00:00:01 GMT<div>M. Ooms</div><div>J.A. Doornik</div>
We discuss computational aspects of likelihood-based specification, estimation,inference, and forecasting of possibly nonstationary series with long memory. We use the \\ARFIMA$(p,d,q)$ model with deterministic regressors and we compare sampling characteristics of approximate and exact first-order asymptotic methods. We extend the analysis using a higher-order asymptotic method, suggested by \\cite{CoxRe.87}. Efficient computation and simulation allow us to apply parametric bootstrap inference as well. We investigate the relevance of the differences between the methods for the time-series analysis of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK. We concentrate on (stationarity) tests for the order of integration and on inference for out-of-sample forecasts of the price level.Long memory and level shifts: re-analysing inflation rates
http://repub.eur.nl/pub/13508/
Thu, 11 Nov 1999 00:00:01 GMT<div>C.S. Bos</div><div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears.Forecasting long-memory left-right political orientations
http://repub.eur.nl/pub/2153/
Fri, 01 Jan 1999 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R. Eisinga</div><div>M. Ooms</div>
This paper considers out-of-sample forecasting of left–right political orientations of party affiliates in the Netherlands, using weekly data from 973 independent national Dutch surveys conducted between 1978 and 1996. The orientations of left-wing and right-wing party affiliates tend to converge over time in the sense that the differences between the average positions tend to decline. The left–right series also reveal long-memory properties in the sense that shocks appear to be highly persistent. We develop forecasting models that account for these data features and we derive the relevant forecast intervals.A seasonal periodic long memory model for monthly river flows
http://repub.eur.nl/pub/1530/
Tue, 22 Sep 1998 00:00:01 GMT<div>M. Ooms</div><div>Ph.H.B.F. Franses</div>
Based on simple time series plots and periodic sample autocorrelations, we document that monthly river flow data display long memory, in addition to pronounced seasonality. In fact, it appears that the long memory characteristics vary with the season. To describe these two properties jointly, we propose a seasonal periodic long
memory model and fit it to the well-known Fraser river data (to be obtained from Statlib at http://lib.stat.cmu.edu/datasets/. We provide a
statistical analysis and provide impulse response functions to show that shocks in certain months of the year have a longer lasting impact than those in other months.Long memory and level shifts: re-analysing inflation rates
http://repub.eur.nl/pub/1556/
Thu, 02 Jul 1998 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>M. Ooms</div><div>C.S. Bos</div>
A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears.Long Memory and Level Shifts: Re-Analyzing Inflation Rates
http://repub.eur.nl/pub/7759/
Fri, 27 Feb 1998 00:00:01 GMT<div>C.S. Bos</div><div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears.Convergence and Persistence of Left-Right Political Orientations in The Netherlands 1978-1995
http://repub.eur.nl/pub/1417/
Wed, 01 Jan 1997 00:00:01 GMT<div>R. Eisinga</div><div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
-Theory-
Two theories about trends in left-right political orientations are juxtaposed: the persistence theory claiming that left-right orientations are highly resistant to change versus the irrelevance theory anticipating a move of mass publics towards the center of the left-right continuum.
-Hypotheses-
The left-right ideological differences between the Dutch political parties have declined since the early 1980s. We therefore assume that the left-right political self-placements of the Dutch electorate have converged to the center position over time.
-Methods-
Descriptive statistics and fractionally integrated time series (ARFIMA) models were used to analyze data from 921 independent national Dutch surveys conducted between 1978 and 1995.
-Results-
The overtime distributions of left-right self-placement exhibit a depopulation of the left and right poles as people slowly gravitate to the center position. The aggregate orientations of religious and party affiliates also reveal a move to the common mid-point. Fractionally integrated time series models support the convergence thesis with right-most and left-most party affiliates converging most rapidly. However, the convergence we find may be part of a nonperiodic wave-like pattern were periods of convergence are alternated by periods of divergence. Future political conflicts may therefore again result in left-right political divergence.A periodic long memory model for quarterly UK inflation
http://repub.eur.nl/pub/2104/
Wed, 01 Jan 1997 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
We consider an extension of the fractionally integrated ARIMA(0, d, 0) model for quarterly UK inflation, where we allow the fractional integration parameter d to vary with the season s. This periodic ARFIMA(0, d, 0) model does not only provide an informative in-sample description, it may also be useful for out-of-sample forecasting. The main result is that the integration parameter in the first two quarters is significantly larger than that in the last two quarters.On periodic correlations between estimated seasonal and nonseasonal components for US and German unemployment
http://repub.eur.nl/pub/2105/
Wed, 01 Jan 1997 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>M. Ooms</div>
The orthogonality assumption of seasonal and nonseasonal components for official quarterly unemployment figures in Germany and the US is examined. Although nonperiodic correlations do not seem to reject the orthogonality assumption, a periodic analysis based on correlation functions that vary with the seasons indicates the violation of orthogonality. The unadjusted data can be described by periodic autoregressive models with a unit root. In simulations, the empirical findings for the German data are replicated, where simple models are used to generate artificial samples. Multivariate seasonal adjustment leads to large periodic correlations. Additive adjustment leads to smaller ones.A Note on the Effect of Seasonal Dummies on the Periodogram Regression
http://repub.eur.nl/pub/1385/
Mon, 01 Jan 1996 00:00:01 GMT<div>M. Ooms</div><div>U. Hassler</div>
We discuss how prior regression on seasonal dummies leads to singularities in periodogram regression procedures for the detection of long memory. We suggest a modified procedure. We illustrate the problems using monthly inflation data from Hassler and Wolters (1995).Flexible Seasonal Long Memory and Economic Time Series
http://repub.eur.nl/pub/1351/
Sun, 01 Jan 1995 00:00:01 GMT<div>M. Ooms</div>
We discuss specification, frequency domain estimation and application of flexible fractionally integrated seasonal long memory time series models, which allow for 'chi-squared' (seasonal) unit root testing.
We suggest periodogram regression and approximate ML estimation.
We successfully apply a flexible model on post war US GNP data, which shows the statistical significance of seasonal 'overdifferencing' due to seasonal adjustment.
Application to monthly shipping data for the Sound (1557-1783) shows the order of integration at frequency 0 and 1/12 about 0.5, with lower values at other frequencies.
We use several graphical techniques to evaluate the estimation results in the
frequency domain.Empirical Vector Autoregressive Modeling
http://repub.eur.nl/pub/14163/
Thu, 01 Apr 1993 00:00:01 GMT<div>M. Ooms</div>
Chapter 2 introduces the baseline version of the VAR model,
with its basic statistical assumptions that we examine in the sequel.
We first check whether the variables in the VAR can be transformed
to meet these assumptions. We analyze the univariate
characteristics of the series.
Important properties are a bounded spectrum,
the order of (seasonal) integration, linearity and normality after the appropriate
transformation. Subsequently, these properties are
contrasted with the properties of stochastic fractional integration.
We suggest data-analytic tools to check the assumption of univariate unit root integration. In an appendix we give a detailed account of unit root tests for stochastic unit root nonstationarity versus deterministic nonstationarity at frequencies of interest.
Chapter 3 first discusses local and global influence analysis,
which should point out the observations with the most notable impact on
the estimates of location and covariance parameters. The results from this
analysis can be helpful in spotting the sources of possible problems with
the baseline model. After the influence analysis we discuss the merits of
various statistical diagnostic tests for the adequacy of the separate regression equations. After one has estimated the unrestricted VAR one should check some overall characteristics of the system. We present several suggestions on how to do this.
Chapter 4 deals with common sources of misspecification stemming from problems
with seasonality and seasonal adjustment in the multivariate model.
We discuss a number of univariate unobserved component models
for stochastic seasonality, giving additional insight into the properties
of models with unit root nonstationarity. We also suggest a modification of a
simple but quite robust seasonal adjustment procedure. Some new data-analytic tools
are introduced to examine the seasonal component more closely.
Appendix A4.1 discusses the limitations of deterministic modeling of seasonality.
Appendix A4.2 treats aspects of backforecasting in models with nonstationarity in mean.
Chapter 5 introduces outlier models. We develop a testing procedure
to direct and evaluate the treatment of exceptional observations in the VAR.
We illustrate its application on an artificial data set that contains
important characteristics of macroeconomic time series.
The effect of the outliers and the effectiveness of the testing procedure
is also analyzed on a four-variate set of quarterly French data,
which exhibits cointegration. We compare some ready-to-use outlier correction
methods in the last section.
Chapter 6 deals with restrictions on the VAR model.
First we discuss a number of interesting reparameterizations of the VAR
under unit root restrictions. The reparameterizations lead to different
interpretations, which can help to assess the plausibility of empirical outcomes.
We present some straightforward transformation formulae for a number of these
parameterizations and show which assumptions are essential for the equivalence
of these models. We illustrate this in simple numerical examples.
Next we compare VAR based methods to estimate pushing trends and pulling equilibria
in multivariate time series. The predictability approach of Box and Tiao
receives special attention. Finally we discuss multivariate tests for unit roots and cointegration.
Chapter 7 applies the methods described in the previous chapters to analyze
gross fixed capital investment in the Netherlands from 1961 to 1988
in a six-variate system. We discuss a number of economic approaches
to model macroeconomic investment series.
We list a number of problems in empirical applications of these models.
Section 7.3 presents empirically relevant aspects of the measurement
model for macroeconomic investment. Section 7.4 applies
the univariate techniques of Chapters 2, 3, 4 and 5 to the investment series
and five other macroeconomic with a notable dynamic relationship with investment,
viz. consumption, imports, exports, the terms of trade and German industrial production.
The univariate analysis clearly shows the presence of nonstationary seasonal
components in a number of the series. The model is extended
with a structural break on the basis of results from the univariate analysis.
The subsequent multivariate analysis confirms the need for a structural break
in the model for the growth rates of the multivariate series.
An empirically important equilibrium relation between investment,
imports and exports is seen to remain stable over the entire sample period.
The partial correlation of deviations from this equilibrium and growth rates
of investment is large and stable.