The following “bibliography” should be a complete listing of all my published academic writings from 1998 to 2004. I’m posting it here both for the world to see, and as a handy place where I can find it myself if I ever need it again! I’ve added brief comments for some of the items; sometimes just to remind myself what the papers were all about. I’ve also included links to everything that is available online (most of the old working papers now only via web.archive.org). Outliers vs. nonlinearity in time series econometrics is the main theme here, and there are also several papers on long memory in the form of fractional integration. My non-academic writings, including a rock gig review at Rumba, to follow some other day perhaps!
This is my economics PhD dissertation, which contains an introduction and four articles: three published ones (in Communications in Statistics, Finnish Economic Papers and Applied Financial Economics), and an unpublished one analysing the impact of outliers on ARFIMA model estimation, with a simple robust two-stage estimation method.
Peer-reviewed journal articles
The effects of outliers on two nonlinearity tests. Communications in Statistics – Simulation and Computation, vol. 29, pp. 897-918 (2000)
A simulation study, showing how even a single outlier in a time series of 500 observations can seriously distort some commonly used tests for nonlinearity (ARCH and bilinearity tests here). Previous work had only considered more frequent outliers – this paper shows that the number of outliers can be very small, and the adverse impact still significant.
Outliers in eleven Finnish macroeconomic time series. Finnish Economic Papers, vol 14, pp 14-32, (2001)
Evaluating the impact of outliers on macroeconomic time series analysis. Conclusion: outliers can have a significant impact, and their treatment should always be carefully considered. I’m afraid I have yet to come to a completely satisfying conclusion about the best way of handling outliers in empirical work.
Outliers and predictability in monthly stock market returns. The Finnish Journal of Business Economics, vol 4/2002, pp 369-380 (2002)
Do outliers influence whether stock markets are predictable using simple time series forecasting methods? With mixed results.
First, a simulation study showing that the presence of outliers will bias time series (fractional integration) long memory estimates towards zero. An empirical example then shows that long memory is detected in stock market data more often if outliers are first taken into account.
Unemployment persistence of different labour force groups in Finland. Applied Economics Letters, vol 10, pp 455-458 (2003)
Fractional integration long memory models are used to estimate a measure of unemployment persistence for different labour force groups. The results show that unemployment is less persistent for females and young people, than for males and the entire labour force.
An empirical assessment of the presence of long memory in Finnish stock market data. Depending on the testing method used, statistically significant long memory is detected in 24% to 67% of the series, which is considerably more than what is usually found in data of this kind. This article is based on a working paper with some additional results (see below).
Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing, vol 8, pp 527-533 (2004)
Possibly my best idea, and also the most cited thing I’ve published. Proposes a new method for simultaneous outlier detection and variable selection, which overcomes a number of problems in this kind of statistical analysis. I’ve also got an application of this method for economic growth data, which I’ll try to polish and share here soon.
Research reports and working papers
Outliers in time series: A review. Research Reports No. 76, University of Turku, Department of Economics (1998)
My statistics Master’s dissertation. A review of statistics and econometrics research on outliers: their impact, detection, treatment, and modelling.
A nonlinear moving average test as a robust test for ARCH. Research Reports No. 81, University of Turku, Department of Economics (1999)
An idea I had – would using a test for one kind of nonlinearity work in detecting another kind, which may often be difficult to detect? Especially when outliers are involved? The answer: not really…
Small sample properties of a joint ARCH-bilinearity test. Research Reports No. 84, University of Turku, Department of Economics (1999)
Another idea – if you create a simultaneous Lagrange multiplier test for two different types of nonlinearity, how would that compare to the individual tests? The answer: about the same…
Aittokallio, T., O. Nevalainen, J. Tolvi, K. Lertola & E. Uusipaikka: Computation of restricted maximum-penalized-likelihood estimates in hidden Markov models. Turku Centre for Computer Science, Technical Report No. 380 (2000)
My main contribution here was to propose a specific kind of hidden Markov model (HMM) for modelling financial data series. The estimated HMM had two components to model the majority of observations: one with low, one with high volatility, to mimic “normal” and turbulent periods. Additional HMM components were then added to model outliers, or very extreme observations. Sadly this was never published anywhere.
Nonlinear model selection in the presence of outliers. Research Reports No. 90, University of Turku, Department of Economics (2001)
Playing around with model selection and outliers using information criteria, with limited success. But this work led to the later genetic algorithm paper in Soft Computing.
Suomalaisten makrotaloudellisten aikasarjojen stationaarisuus ja pitkän muistin ominaisuudet. Research Reports No. 95, University of Turku, Department of Economics (2002) [Stationarity and long memory properties of Finnish macroeconomic time series]
Showing that once you take outliers and level shifts into account, there is very clear evidence for the presence of long memory in macroeconomic data. I can’t remember why I wrote this one in Finnish, as the results could have been of interest outside of Finland as well. And this paper also does not seem to be available anywhere online any more?
Long memory in the Finnish stock market. Research Reports No. 103, University of Turku, Department of Economics (2002)
The Economics Bulletin article above is based on this working paper, which has additional results for volatility data, and results of estimated ARFIMA-FIGARCH models as well.
Book reviews and short notes
Vielä yksikköjuurista ja työttömyysaikasarjojen tilastollisesta luonteesta. Kansantaloudellinen aikakauskirja, vol 1/1999, pp 159-163 (1999) [A further note on unit roots and the statistical properties of unemployment time series, the Finnish Journal of Economics]
Rationaalisista odotuksista. Sosiologia, vol 2/2000, pp 145-146 (2000) [On rational expectations, Sosiologia – the Journal of Westermarck Society]
Miten olla hyvä taloustieteilijä? Kansantaloudellinen aikakauskirja, vol 2/2001, pp 339-341 (2001) [How to be a good economist? A book review of McCloskey, D. N.: How to be human – though an economist, the Finnish Journal of Economics]
Poikkeavat havainnot epälineaarisessa aikasarjaekonometriassa. Lectio praecursoria. Kansantaloudellinen aikakauskirja, vol 1/2002 [Outliers in nonlinear time series econometrics. Doctoral lecture, the Finnish Journal of Economics]
My introductory lecture at my PhD viva – a brief summary of my dissertation, aimed for the general public. I used my father as a guinea pig to test whether he would get it. (He did!)
Book review of Dhrymes, P. J.: Mathematics for Econometrics (3. ed.). Journal of the Royal Statistical Society, series D – the Statistician, vol 51, pp 411-412 (2002)
Book review of Ghysels, E, Swanson, N. R and Watson, M. W. (eds.): Essays in econometrics: The collected papers of Clive W. J. Granger. Journal of the Royal Statistical Society: Series D (The Statistician), vol 52, pp 113-114 (2003)
Book review of Tsay, R. S.: Analysis of financial time series. Journal of the Royal Statistical Society: Series D (The Statistician), vol 52, pp 128-129 (2003)
Book review of Zivot, E. and Wang, J.: Modeling Financial Time Series with S-Plus. Journal of the Royal Statistical Society: Series D (The Statistician), vol 52, p 705 (2003)