Interest rate convergence in the EMS prior to European Monetary Union

In this paper we analyze the convergence of interest rates in the European Monetary System (EMS) in a framework of changing persistence. This allows us to estimate the exact date of full convergence from the data. A change in persistence means that a time series switches from stationarity to non-stationarity, or vice versa. It is often argued that due to the specific historical situation in the EMS the interest rate differential was non-stationary before the full convergence of interest rates was achieved and stationary afterwards. Our empirical results suggest that the convergence date has been very different for Belgium, France, the Netherlands and Italy and are in line with the conclusions one would draw from a narrative approach. We compare three different estimators for the convergence date and find that the results are quite robust. Our results therefore stress the importance of credibility for monetary policy

Persistence of inflationary shocks: Implications for West African Monetary Union Membership

Plans are far advanced to form a second monetary union, the West African Monetary Zone (WAMZ), in Africa. While much attention is being placed on convergence criteria and preparedness of the five aspiring member states, less attention is being placed on the extent to which the dynamics of inflation in individual countries are (dis)similar. This paper aims to stimulate debate on the long term sustainability of the union by examining the dynamics of inflation within these countries. Using Fractional Integration (FI) methods, we establish that some significant differences exist among the countries. Shocks to inflation in Sierra Leone are non mean reverting; results for The Gambia, Ghana and Guinea-Bissau suggest some inflation persistence, despite being mean reverting. Some policy implications are discussed and some warnings are raised

Socially Disadvantaged Groups and Microfinance in India

In this paper we provide an empirical analysis of the performance of microfinance groups, known as Self-Help groups, based on an original census we carried out in a poor area of Northern India. We examine whether traditionally disadvantaged villagers, such as members of lower castes or landless farmers, are less likely to have access to groups. We also analyze their performance in terms of access to bank loans, which is an important benefit of the groups. We nd evidence of the attrition process being selective against lower castes: they have a lower probability of becoming a permanent member of a group. The net effects in terms of their expected access to a bank loan remain however relatively limited. By contrast, even though landless farmers are more likely to fail or leave the groups, they tend to benet disproportionately. In expected terms, they receive more than two times the amounts of bank loans given to farmers owning more than one acre. Overall, the program therefore has positive and important distributional implications.

Spectral rate theory for projected two-state kinetics

Classical rate theories often fail in cases where the observable(s) or order parameter(s) used are poor reaction coordinates or the observed signal is deteriorated by noise, such that no clear separation between reactants and products is possible. Here, we present a general spectral two-state rate theory for ergodic dynamical systems in thermal equilibrium that explicitly takes into account how the system is observed. The theory allows the systematic estimation errors made by standard rate theories to be understood and quantified. We also elucidate the connection of spectral rate theory with the popular Markov state modeling (MSM) approach for molecular simulation studies. An optimal rate estimator is formulated that gives robust and unbiased results even for poor reaction coordinates and can be applied to both computer simulations and single-molecule experiments. No definition of a dividing surface is required. Another result of the theory is a model-free definition of the reaction coordinate quality (RCQ). The RCQ can be bounded from below by the directly computable observation quality (OQ), thus providing a measure allowing the RCQ to be optimized by tuning the experimental setup. Additionally, the respective partial probability distributions can be obtained for the reactant and product states along the observed order parameter, even when these strongly overlap. The effects of both filtering (averaging) and uncorrelated noise are also examined. The approach is demonstrated on numerical examples and experimental single-molecule force probe data of the p5ab RNA hairpin and the apo-myoglobin protein at low pH, here focusing on the case of two-state kinetics

Essays in Applied Bayesian Analysis

With continuing rapid developments in computational power, Bayesian statistical methods, because of their user-friendliness and estimation capabilities, have become increasingly popular in a considerable variety of application fields. In this thesis, applied Bayesian methodological topics and empirical examples focusing on nonhomogeneous hidden Markov models (NHMMs) and measurement error models are explored in three chapters. In the first chapter, a subsequence-based variational Bayesian inference framework for NHMMs is proposed in order to address the computational problems encountered when analyzing datasets containing long sequences. The second chapter concentrates on measurement error models, where a Bayesian estimation procedure is proposed for the partial potential impact fraction (pPIF) with the presence of measurement error. The third chapter focuses on an empirical application in marketing, where a coupled nonhomogeneous hidden Markov model (CNHMM) is introduced to provide a novel framework for customer relationship management

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models