Following the 2000 stockmarket crash, have US interest rates been held too low in relation to their natural level? Most likely, yes. Using a structural neo-Keynesian model, this paper attempts a real-time evaluation of the US monetary policy stance while ensuring consistency between the...

The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real time sequential analysis. We find evidence of self-exciting jump clustering since the...

Sequential Monte Carlo (SMC) methods are widely used for filtering purposes of non-linear economic or financial models. Nevertheless the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov- Chain...

There has been mixed evidence regarding the existence of rational bubbles in the foreign exchange markets. This paper introduces recently developed sequential unit root tests into the analysis of exchange rates bubbles. We find strong evidence of explosive behavior in the nominal Sterling-dollar...

There has been mixed evidence regarding the existence of rational bubbles in the foreign exchange markets. This paper introduces recently developed sequential unit root tests into the analysis of exchange rates bubbles. We find strong evidence of explosive behavior in the nominal Sterling-dollar...

There has been mixed evidence regarding the existence of rational bubbles in the foreign exchange markets. Standard unit root and cointegration tests are criticized for their low power to detect rational bubbles that periodically collapse. This paper introduces recently developed sequential unit...

This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restriction...

The dates of U.S. business cycle are reported by NBER with a considerable delay, so an early notion of turning points is of particular interest. This paper proposes a novel sequential approach designed for timely signaling these turning points. A directional cumulated sum decision rule is...

This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restriction...

Following the 2000 stockmarket crash, have US interest rates been held "too low" in relation to their natural level? Most likely, yes. Using a structural neo-Keynesian model, this paper attempts a real-time evaluation of the US monetary policy stance while ensuring consistency between the...

In the present work we develop partial sequential nonparametric tests for multiple comparison. We provide tests for the identity of several unknown univariate continuous distribution functions against patterned alternatives. Our tests are based on Wilcoxon score. We conduct some Monte Carlo...

We consider the optimization problem of a decision maker facing a sequence of coin tosses with an initially unknown probability Θ for heads. Before each toss she bets on either heads or tails and she wins one euro if she guesses correctly, otherwise she loses one euro. We investigate the effect...

We discuss regression models for ordered responses, such as ratings of bonds, schooling attainment, or measures of subjective well-being. Commonly used models in this context are the ordered logit and ordered probit regression models. They are based on an underlying latent model with single...

Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution...

In many applications one is interested to detect certain (known) patterns in the mean of a process with smallest delay. Using an asymptotic framework which allows to capture that feature, we study a class of appropriate sequential nonparametric kernel procedures under local nonparametric...