We construct and estimate a unified model combining three of the main sources of cross-country income disparities:[...]

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable,...

We consider a recently proposed class of nonlinear time series models and focus mainly onmisspecification testing for models of such type. Following the modeling cycle for nonlineartime series models of specification, estimation and evaluation we first treat how to choosean adequate transition...

We consider time series models in which the conditional mean of the response variable given thepast depends on latent covariates. We assume that the covariates can be estimated consistentlyand use an iterative nonparametric kernel smoothing procedure for estimating the conditional meanfunction....

Tse (1998) proposes a model which combines the fractionally integrated GARCH formulationof Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH specification of Ding,Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant...

Determining good parameter estimates in ESTAR models is known to be difficult. We showthat the phenomena of getting strongly biased estimators is a consequence of the so-calledidentication problem, the problem of properly distinguishing the transition function in relationto extreme parameter...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-runconcept the specific dynamic driving the process is largely build upon a priori economicbelief rather than a thorough statistical modeling procedure. The two prevailing timeseries models, i.e. the exponential smooth...

This paper uses dimension asymptotics to study why overfit linear regression models shouldbe compared out-of-sample; we let the number of predictors used by the larger model increasewith the number of observations so that their ratio remains uniformly positive. Under this limittheory, the naive...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm...