Patterns of poverty among elderly Americans: a latent class Markov model

This article studies poverty persistence and the role of social security programmes on poverty among elderly in the US. We use a Latent Markov model to disentangle unobserved heterogeneity and state dependence. Because of its dynamic nature, unobserved heterogeneity is modelled to vary over time. This allows to capture different latent states of poverty that change over time. Result indicates the existence of three unobserved types evolving over time according to their propensity to be poor. Moreover, a strong persistence in poverty especially for women, individuals living alone and ethnic minorities is found. Finally, the estimates indicate that giving social assistance tends to reduce poverty.     

How to improve a technology evaluation model: A data-driven approach

Academic research suggests a number of technology evaluation models. To ensure effective use, models need to be improved in accordance with changing internal and external environments. However, a majority of previous studies focus on model development, while a few emphasize their implementation or improvement. To fill this research gap, this study suggests a systematic approach to examining the validity of technology evaluation models and improving them. We consider three propositions as criteria for improvement: 1) the coherence of the evaluation results with the evaluation purpose, 2) the appropriateness of the evaluation methods, and 3) the concreteness of the evaluation model. Rather than using expert opinions, this study takes a data-driven approach, wherein we analyze actual evaluation results and determine whether the model produces the intended results. A case study of 291 technology evaluation results, all made by the South Korean government in support of technology-based small and medium-sized enterprises, is conducted to verify the suggested approach's applicability. This is one of the few studies to address issues regarding improvements to a technology evaluation model. Its approach can help to develop and continuously improve a valid technology evaluation model, thus leading to more effective practice.     

Convex risk measures for the aggregation of multiple information sources and applications in insurance

We propose a novel class of convex risk measures, based on the concept of the Fréchet mean, designed in order to handle uncertainty which arises from multiple information sources regarding the risk factors of interest. The proposed risk measures robustly characterize the exposure of the firm, by filtering out appropriately the partial information available in individual sources into an aggregate model for the risk factors of interest. Importantly, the proposed risks can be expressed in closed analytic forms allowing for interesting qualitative interpretations as well as comparative statics and thus facilitate their use in the everyday risk management process of the insurance firms. The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty.     

Pricing used books on Amazon.com: a spatial approach to price dispersion

This paper estimates a spatial autoregressive (SAR) model of price dispersion using publicly available internet bookselling data. It uses a semiparametric adaptive estimator that does not require the usual Gaussian assumption of maximum likelihood (ML) estimators. The results suggest that both price competition and seller heterogeneity are key drivers of the observed price dispersion. The paper finds that sellers with large sales volume, newly established sellers and US mainland states-based sellers tend to price lower. The identified significant spatial interaction is evidence of spatial price competition. Controlling for everything else, a seller asks a lower price when large sellers charge relatively high prices, which is also evidence of price-based selling and undercutting.     

Trade-threshold Effect on Inflation in Tunisia: New Evidence Resulting from a Nonlinear Approach

Trade openness is an important determinant of the inflation process. The effect of trade openness on inflation, however, is still an issue of debate at both theoretical and empirical levels. This study tried to provide a contribution to the literature by examining the relationship between inflation and trade openness in Tunisia over the period 1975Q1-2015Q4 using a nonlinear model. The originality of this study stems from the fact that it is the first investigation considering both the Residual-Based Tests for Cointegration in Models with the Regime Shifts and Threshold Regression model. The linear model confirms the existence of a positive relationship between inflation and trade in Tunisia. Yet, considering the nonlinear model, trade openness growth and Consumer Price Index (CPI) inflation growth show a statistically significant negative link as long as the trade openness evolution does not exceed the threshold. Nevertheless, if the trade openness growth is higher than the threshold, integrating the trade positively affects CPI inflation. Furthermore, a positive influence of Money supply growth on this type of inflation was noticed in Tunisia in all the considered regimes proving the effect of monetary factors on inflation level. Consequently, trade openness could be used to control inflation in Tunisia.     

Large agricultural investments in Kenya’s Nanyuki Area: Inventory and analysis of business models

Many experts agree that more agricultural investment is needed in the global South to improve local food security and reduce poverty. However, there is a lack of consensus about the types of investment needed to achieve these goals. This paper contributes to the literature on large agricultural investments and corresponding business models by inventorying and analysing such investments in Kenya’s Nanyuki area. We identify four clusters of business models that differ primarily by type of production and other distinct determinants, namely: demand from markets; access to land; land tenure regime and colonial history; actors involved; biophysical context; labour availability; and governance of the value chain via private standards. The study results shed light on the factors that help or hinder implementation of large agricultural investments and shape their impacts in the context of African land use systems. The way land is accessed represents one of the most-decisive factors determining the risks and opportunities associated with such projects. We find that most investments in the Nanyuki area occur on land bought or leased from private owners.     

Identifying Price Reviews by Firms: An Econometric Approach

Price reviews are a potentially costly activity. A significant fraction of unchanged prices may stem from firms not reviewing prices, rather than from obstacles to changing prices per se, such as menu costs. In this paper, we disentangle these two causes of price stickiness by estimating an inflated ordered probit model on a panel of French manufacturing firms. The results point to a low frequency of price reviews, suggestive of the relevance of information costs as a determinant of the observed price stickiness. In view of the “inattentive producers” literature, pointing that the source of price rigidity matters, this is suggestive of a large real effect of monetary policy.     

FX Intervention in the New Keynesian Model

We develop an open-economy New Keynesian Model with foreign exchange (FX) intervention in the presence of a financial accelerator and shocks to risk appetite in international capital markets. We obtain closed-form solutions for optimal monetary and FX intervention policies assuming the central bank cannot commit to future policies, and we compare the solution to that under policy commitment. We show how FX intervention can help reduce the volatility of the exchange rate, of inflation, and of the output gap, thus mitigating welfare losses associated with shocks in the international capital markets. We also show that, when the financial accelerator is strong, there is a risk of indeterminacy (self-fulfilling currency and inflation movements) although FX intervention can reduce this risk and thus reinforce the credibility of the inflation targeting regime. Model simulations match well the impact of a VIX shock obtained by local projections on a panel of inflation targeting emerging markets.     

Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Factor state–space models for high-dimensional realized covariance matrices of asset returns

We propose a dynamic factor state–space model for high-dimensional covariance matrices of asset returns. It makes use of observed risk factors and assumes that the latent integrated joint covariance matrix of the assets and the factors is observed through their realized covariance matrix with a Wishart measurement density. For the latent integrated covariance matrix of the assets we impose a strict factor structure allowing for dynamic variation in the covariance matrices of the factors and the residual components as well as in the factor loadings. This factor structure translates into a factorization of the Wishart measurement density which facilitates statistical inference based on simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application to realized covariance matrices for 60 NYSE traded stocks using the Fama–French factors and sector-specific factors represented by Exchange Traded Funds (ETFs) shows that the model performs very well in- and out of sample.