Bayesian analysis of multivariate threshold autoregressive models with missing data

In some fields, we are forced to work with missing data in multivariate time series. Unfortunately, the data analysis in this context cannot be carried out in the same way as in the case of complete data. To deal with this problem, a Bayesian analysis of multivariate threshold autoregressive models with exogenous inputs and missing data is carried out. In this paper, Markov chain Monte Carlo methods are used to obtain samples from the involved posterior distributions, including threshold values and missing data. In order to identify autoregressive orders, we adapt the Bayesian variable selection method in this class of multivariate process. The number of regimes is estimated using marginal likelihood or product parameter-space strategies.     

A generalised NGINAR(1) process with inflated‐parameter geometric counting series

In this paper we propose a new stationary first‐order non‐negative integer valued autoregressive process with geometric marginals based on a generalised version of the negative binomial thinning operator. In this manner we obtain another process that we refer to as a generalised stationary integer‐valued autoregressive process of the first order with geometric marginals. This new process will enable one to tackle the problem of overdispersion inherent in the analysis of integer‐valued time series data, and contains the new geometric process as a particular case. In addition various properties of the new process, such as conditional distribution, autocorrelation structure and innovation structure, are derived. We discuss conditional maximum likelihood estimation of the model parameters. We evaluate the performance of the conditional maximum likelihood estimators by a Monte Carlo study. The proposed process is fitted to time series of number of weekly sales (economics) and weekly number of syphilis cases (medicine) illustrating its capabilities in challenging cases of highly overdispersed count data.     

The efficiency of crackdowns: a lab-in-the-field experiment in public transportations

The concentration of high-frequency controls in a limited period of time (“crackdowns”) constitutes an important feature of many law-enforcement policies around the world. In this paper, we offer a comprehensive investigation on the relative efficiency and effectiveness of various crackdown policies using a lab-in-the-field experiment with real passengers of a public transport service. We introduce a novel game, the daily public transportation game, where subjects have to decide, over many periods, whether to buy or not a ticket knowing that there might be a control. Our results show that (a) concentrated crackdowns are less effective and efficient than random controls; (b) prolonged crackdowns reduce fare-dodging during the period of intense monitoring but induce a burst of fraud as soon as they are withdrawn; (c) pre-announced controls induce more fraud in the periods without control. Overall, we also observe that real fare-dodgers fraud more in the experiment than non-fare-dodgers.     

REGULATION AND INVESTMENT

We use newly assembled data on regulation in several sectors of many OECD countries to provide evidence that regulatory reform of product markets is associated with an increase in investment. A component of reform that plays a very important role is entry liberalization, but privatization also has a substantial effect on investment. Sensitivity analysis suggests that our results are robust. (JEL: E22, L5)     

Manual bamboo cutting tool

The paper presents the development of a cutting tool guide, specifically for the harvest of bamboo. The development was made based on precepts of eco-design and ergonomics, for prioritizing the physical health of the operator and the maintenance of the environment, as well as meet specific requirements of bamboo. The main goal is to spread the use of bamboo as construction material, handicrafts, among others, from a handy, easy assembly and material available tool.     

Financial Instruments for Mitigation of Flood Risks: The Case of Florence

This article analyzes the mechanisms and effects of innovative financial instruments that a central public administration (CPA) may adopt to minimize the flood risk in particularly exposed regions. The pattern we suggest assumes that in risky areas the CPA can issue two financial instruments, called project options and CAT‐bonds, producing a dynamic interaction among three types of agents: the CPA itself, the local public administrations, and private investors. We explore the possible scenarios of such interaction and the conditions under which the CPA's goal of maximal risk reduction is attained. This pattern is proposed for flood risk mitigation in the city of Florence, where the model dynamics are tested assuming parameters obtained from engineering studies.     

A recursive approach for estimating missing observations in an univariate time series

A linear recursive technique that does not use the Kalman filter approach is proposed to estimate missing observations in an univariate time series. It is assumed that the series follows an invertible ARIMA model. The procedure is based on the restricted forecasting approach, and the recursive linear estimators are optimal in terms of minimum mean-square error.     

Minimum Distance Estimation of Search Costs Using Price Distribution

ABSTRACT

It has been shown that equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observedprices alone. These quantiles can be difficult to estimate in practice. This article uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least-square problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogenous markets are available. We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues’ matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. Supplementary materials for this article are available online.