R.M. Kunst (Robert)
http://repub.eur.nl/ppl/8275/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryAsymmetric time aggregation and its potential benefits for forecasting annual data
http://repub.eur.nl/pub/78816/
Thu, 14 Aug 2014 00:00:01 GMT<div>R.M. Kunst</div><div>Ph.H.B.F. Franses</div>
For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an activity timescale instead of cal- endar time. Such a scheme may strike a balance between annual modeling (which processes little information) and modeling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform model- ing calendar time units (with some months or quarters containing more information than others). We suggest an algorithm that performs an approximate inversion of the inherent seasonal time deformation. We illustrate the procedure using two exemplary weekly time series.Testing for Seasonal Unit Roots in Monthly Panels of Time Series
http://repub.eur.nl/pub/25640/
Mon, 01 Aug 2011 00:00:01 GMT<div>R.M. Kunst</div><div>Ph.H.B.F. Franses</div>
We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly cross-sectionally augmented Hylleberg-Engle-Granger-Yoo (CHEGY) test to the monthly case. This parametric test is contrasted with a new non-parametric test, which is the panel counterpart to the univariate record unit-root seasonal (RURS) test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data. Testing for seasonal unit roots in monthly panels of time series
http://repub.eur.nl/pub/14861/
Thu, 19 Feb 2009 00:00:01 GMT<div>R.M. Kunst</div><div>Ph.H.B.F. Franses</div>
We consider the problem of testing for seasonal unit roots in monthly
panel data. To this aim, we generalize the quarterly CHEGY test
to the monthly case. This parametric test is contrasted with a new
nonparametric test, which is the panel counterpart to the univariate
RURS test that relies on counting extrema in time series. All methods
are applied to an empirical data set on tourism in Austrian provinces.
The power properties of the tests are evaluated in simulation experiments
that are tuned to the tourism data.Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?
http://repub.eur.nl/pub/13362/
Thu, 01 Nov 2007 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
In this paper we consider deterministic seasonal variation in quarterly industrial production for several European countries, and we address the question whether this variation has become more similar across countries over time. Due to economic and institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles, one may perhaps also find convergence in seasonality. To this aim, we propose a method that is based on treating the set of production series as a panel. By testing for the relevant parameter restrictions for moving window samples, we examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. Our main empirical finding is that there is no evidence for convergence in seasonality.On the Role of Seasonal Intercepts in Seasonal Cointegration
http://repub.eur.nl/pub/13503/
Fri, 01 Jan 1999 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
In the paper we consider the role of seasonal intercepts in seasonal cointegration analysis. For the nonseasonal unit root, such intercepts can generate a stochastic trend with a drift common to all observations. For the seasonal unit roots, however, we show that unrestricted seasonal intercepts generate trends that are different across the seasons. Since such seasonal trends may not appear in economic data, we propose a modified empirical method to test for seasonal cointegration. We evaluate our method using Monte Carlo simulations and using a four-dimensional data set of Austrian macroeconomic variables.Testing common deterministic seasonality
http://repub.eur.nl/pub/1560/
Fri, 01 Jan 1999 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
We propose methods to test for common deterministic seasonality, while allowing for possible seasonal unit roots. For this purpose, we consider panel methods, where we allow for individual and for common dynamics. To decide on the presence of seasonal unit roots, we introduce a decision-based approach, for which we derive the relevant critical values. We introduce an estimation method for our specific panel models. Our application concerns 16 quarterly industrial production series. One of our findings is that there is not much evidence for common deterministic seasonality.Testing for converging deterministic seasonal variation in European industrial production
http://repub.eur.nl/pub/1587/
Fri, 01 Jan 1999 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
In this paper we consider deterministic seasonal variation in quarterly production for several European countries, and we address the question whether this variation has become more similar across countries over time. Due to economic and institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles, one may perhaps also find convergence in seasonality. To this aim, we propose a new method, which is based on treating the set of production series as a panel. By testing for the relevant parameter restrictions for moving window samples, we examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. We derive the estimation bias, and show that it is very small for samples of more than three years of quarterly observations. Our main empirical finding is that there is almost no evidence for convergence in seasonality.On the role of seasonal intercepts in seasonal cointegration
http://repub.eur.nl/pub/1552/
Thu, 01 Jan 1998 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
In the paper we consider the role of seasonal intercepts in seasonal cointegration analysis. For the nonseasonal unit root, such intercepts can generate a stochastic trend with a drift common to all observations. For the seasonal unit roots, however, we show that unrestricted seasonal intercepts generate trends that are different across the seasons. Since such seasonal trends may not appear in economic data, we propose a modified empirical method to test for seasonal cointegration. We evaluate our method using Monte Carlo simulations and using a four-dimensional data set of Austrian macroeconomic variables.The impact of seasonal constants on forecasting seasonally cointegrated time series
http://repub.eur.nl/pub/2147/
Thu, 01 Jan 1998 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.M. Kunst</div>
In this paper we focus on the effect of (i) deleting, (ii) restricting or (iii) not restricting seasonal intercept terms on forecasting sets of seasonally cointegrated macroeconomic time series for Austria, Germany and the UK. A first empirical result is that the number of cointegrating vectors as well as the relevant estimated parameter values vary across the three models. A second result is that the quality of out-of-sample forecasts critically depends on the way seasonal constants are treated. In most cases, predictive performance can be improved by restricting the effects of seasonal constants. However, we find that the relative advantages and disadvantages of each of the three methods vary across the data sets and may depend on sample-specific features.