Philip Hans Franses

Smooth transition autoregressive models - a survey of recent developments

This paper surveys recent developments related to the smooth transition autoregressive (STAR) time series model and several of its variants. We put emphasis on new methods for testing for STAR nonlinearity, model evaluation, and forecasting. Several useful extensions of the basic STAR model, which concern multiple regimes, time-varying non-linear properties, and models for vector time series, are also reviewed.

The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: Does age of relationship matter

The authors examine the effect of relational constructs (e.g., satisfaction, trust, and affective and calculative commitment) on customer referrals and the number of services purchased, as well as the moderating effect of age of the relationship on these relationships. The research reported, based on data obtained from a large sample of customers of an insurance company, combines archival and survey data. The results provide evidence that supports the moderating effect of relationship age on the relationship between satisfaction, affective and calculative commitment, and the number of services purchased.

Nonlinear time series models in empirical finance

1. Introduction 2. Some concepts in time series analysis 3. Regime-switching models for returns 4. Regime-switching models for volatility 5. Artificial neural networks for returns 6. Conclusion.

Forecasting stock market volatility using (non-linear) Garch models

In this paper we study the performance of the GARCH model and two of its non-linear modifications to forecast weekly stock market volatility. The models are the Quadratic GARCH (Engle and Ng, 1993) and the Glosten, Jagannathan and Runkle (1992) models which have been proposed to describe, for example, the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations such as the 1987 stock market crash and that the GJR model cannot be recommended for forecasting.

The Effects of Additive Outliers on Tests for Unit Roots and Cointegration

textabstractThe properties of the univariate Dickey-Fuller test and the Johansen test for the cointegrating rank when there exist additive outlying observations in the time series are examined. The analysis provides analytical as well as numerical evidence that additive outliers may produce spurious stationarity. Hence, the Dickey-Fuller test will reject a unit root too frequently, and the Johansen test will indicate too many cointegrating vectors. The results easily generalize to models with temporary change outliers. Through an empirical example, the analysis demonstrates how additive and temporary change outliers can be detected in practice, and it shows how dummy variables can be used to remove the influence of such extreme observations. A proper statistical procedure to detect outliers is necessary. Many statistical software packages for analyzing autoregressive integrating moving average models have built-in routines to detect outliers.

Seasonality, non-stationarity and the forecasting of monthly time series

In this paper the focus is on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first order differences, while seasonality is modeled with a constant and seasonal dummies. A method to empirically distinguish between these two models is presented. The relevance of this method is established by simulation results, as well as empirical evidence, which show that,. firstly, conventional autocorrelation checks are often not discriminative, and, secondly, that considering the first model while the second is more appropriate yields a deterioration of forecasting performance.

Additive outliers, GARCH and forecasting volatility

The Generalized Autoregressive Conditional Heteroskedasticity [GARCH] model is often used for forecasting stock market volatility. It is frequently found, however, that estimated residuals from GARCH models have excess kurtosis, even when one allows for conditional t-distributed errors. In this paper we examine if this feature can be due to neglected additive outliers [AOs], where we focus on the out-of-sample forecasting properties of GARCH models for AO-corrected returns. We find that models for AO-corrected data yield substantial improvement over GARCH and GARCH-t models for the original returns, and that this improvement holds for various samples, two forecast evaluation criteria and four stock markets.

Asymptotically perfect and relative convergence of productivity

In this paper we examine the extent to which countries are converging in per capita productivity levels. We propose to use cluster analysis in order to allow for the endogenous selection of converging countries. We formally define convergence in a time series analytical context, derive the necessary and sufficient conditions for convergence, and introduce a cluster analytical procedure that distinguishes several convergence clubs by testing for these conditions using a multivariate test for stationarity. We find a large number of relatively small convergence clubs, which suggests that convergence might not be such a widespread phenomenon. Copyright

The impact of satisfaction and payment equity on cross-buying

Abstract In the last decade, marketers have primarily focused on keeping customers. Only recently have they become aware that creating value by cross-selling additional services is also an important aspect of customer relationship management. In this article we investigate how satisfaction and payment equity, defined as the perceived fairness of the price, affect cross-buying at a multiservice provider. We also consider its competitors’ performance on these factors. Our results show that the effect of satisfaction differs between customers with lengthy and short relationships. It also shows that payment equity negatively affects cross-buying for customers with long relationships. However, if the prices of the supplier are perceived as fairer than the prices of the competitor, the customers’ probability of cross-buying increases.

Critical values for unit root tests in seasonal time series

In this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal variation and time series with structural breaks in the seasonal means. For each case, we give the appropriate auxiliary test regression, the test statistics, and the corresponding critical values for a selected set of sample sizes. We also illustrate the practical use of the auxiliary regressions for quarterly new car sales in the Netherlands. Supplementary to this paper, we provide Gauss programs with which one can generate critical values for particular seasonal frequencies and sample sizes.