We propose a noncausal autoregressive model with time-varying parameters, and apply it to U.S. postwar inflation. The model .fits the data well, and the results suggest that inflation persistence follows from future expectations. Persistence has declined in the early 1980.s and slightly...

While the literature has established that there is substantial and highly selective return migration, the growing importance of repeat migration has been largely ignored. Using Markov chain analysis, this paper provides a modeling framework for repeated moves of migrants between the host and...

This paper discusses the type of trajectory a country's public debt path follows. In particular, a Markov switching ADF model is used to assess the sustainability of public debt by testing whether a government's present value borrowing constraint holds. Building on the work of Raybaudi et al....

We analyze the importance of global shocks for the global economy and national policy makers. More specifically, we investigate whether monetary policy has become less effective in the wake of financial globalization. We also examine whether there is increasing uncertainty for central banks due...

Using a binary reference series based on the dating procedure of Artis, Kontolemis and Osborn (1997) different procedures for predicting turning points of the German business cycles were tested. Specifically, a probit model as proposed by Estrella and Mishkin (1997) as well as Markov-switching...

This study tests the hypothesis that the ongoing restructuring process in the European electricity sector, as well as market participants' adaptation to the new legal framework, have caused electricity wholesale day-ahead prices to converge towards arbitrage freeness. Using hourly cross-border...

In this paper a dynamic bi-factor model with Markov switching is proposed to measure and predict turning points of the German business cycle. It estimates simultaneously the composite leading indicator (CLI) and composite coincident indicator (CCI) together with corresponding probabilities of...

Respondent driven sampling (RDS) is a network sampling technique typically employed for hard-to-reach populations (e.g. drug users, men who have sex with men, people with HIV). Similar to snowball sampling, initial seed respondents recruit additional respondents from their network of friends....