A Bayesian approach for estimation of weight matrices in spatial autoregressive models

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Our approach focuses on spatial weights which are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Gibbs sampling techniques and can be easily combined with and extended to more flexible spatial specifications. In addition to geographic prior structures, we also discuss shrinkage priors on the neighbourhood size, which are particularly useful in spatial panels where T is small relative to N

The gravity model for international trade: Specification and estimation issues in the prevalence of zero flows

The gravity model for international trade is one of the most successful empirical models in trade literature. There is a long tradition to log-linearise the multiplicative model and to estimate the parameters of interest by least squares. But this practice is inappropriate for several reasons. First of all, bilateral trade flows are frequently zero and disregarding countries that do not trade with each other produces biased results. Second, log-linearisation in the presence of heteroscedasticity leads to inconsistent estimates in general. In recent years, the Poisson gravity model along with pseudo maximum likelihood estimation methods have become popular as a way of dealing with such econometric issues as arise when dealing with origin-destination flows. But the standard Poisson model specification is vulnerable to problems of overdispersion and excess zero flows. To overcome these problems, this paper presents zero-inflated extensions of the Poisson and negative binomial specifications as viable alternatives to both the log-linear and the standard Poisson specifications of the gravity model. The performance of the alternative model specifications is assessed on a real world example, where more than half of country-level trade flows are zero.Series: Working Papers in Regional Scienc

Do mining activities foster regional development? Evidence from Latin America in a spatial econometric framework

Against the backdrop of steadily increasing global raw material demand, the socio-economic implications of metal ore extraction in developing countries are of major interest in academic and policy debates. This work investigates whether mining activities relate to the economic performance of mining regions and their surrounding areas. Usually, subnational impact assessments of mining activities are conducted in the form of qualitative in-field case studies and focus on a smaller sample of mining properties and regions. In contrast, we exploit a panel of 32 Mexican, 24 Peruvian and 16 Chilean regions over the period 2008 - 2015 and, in doing so, relate mine-specific data on extraction intensity to regional economic impacts. The study employs a Spatial Durbin Model (SDM) with heteroskedastic errors to provide a flexible econometric framework to measure the impact of natural resource extraction. The results suggest that mining intensity does not significantly affect regional economic growth in both short-run and medium-run growth models. Popular arguments of the mining industry that the extractive sector would trigger positive impulses for regional economic development cannot be verified. Rather, the findings support narratives that mining regions do not benefit from their wealth in natural resources due to low labour intensity, loose links to local suppliers and profit outflows.Series: Ecological Economic Paper

Forecasting Global Equity Indices Using Large Bayesian VARs

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-movements in the stock markets. The time-varying specification of the covariance structure moreover accounts for sudden shifts in the level of volatility. In an out-of-sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of root mean squared errors as well as Bayesian log predictive scores. The BVAR model without stochastic volatility, on the other hand, underperforms relative to the random walk. In a portfolio allocation exercise we moreover show that it is possible to use the forecasts obtained from our BVAR model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy-and-hold strategy. (authors' abstract)Series: Department of Economics Working Paper Serie

A Bayesian Markov-switching SAR model for time-varying cross-price spillovers

The spatial autoregressive (SAR) model is extended by introducing a Markov switching dynamics for the weight matrix and spatial autoregressive parameter. The framework enables the identification of regime-specific connectivity patterns and strengths and the study of the spatiotemporal propagation of shocks in a system with a time-varying spatial multiplier matrix. The proposed model is applied to disaggregated CPI data from 15 EU countries to examine cross-price dependencies. The analysis identifies distinct connectivity structures and spatial weights across the states, which capture shifts in consumer behaviour, with marked cross-country differences in the spillover from one price category to another

Unveiling Drivers of Deforestation: Evidence from the Brazilian Amazon

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Indirect impacts and determinants that eventually differ across locations and over time are important factors in these processes. These are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we employ a flexible modelling framework to gain more accurate quantitative insights into the complexities of deforestation phenomena. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 and explicitly consider spatial spillovers and varying impacts over time and space. Spillover effects from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in later years. This suggests moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that neglect of spatial dynamics and the assumption of homogeneous impacts leads to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.Series: Ecological Economic Paper

Comparing the impact of future cropland expansion on global biodiversity and carbon storage across models and scenarios

Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land-use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socio-economic scenarios (SSP1-3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritise future protection of biodiverse and carbon-rich areas

DNA of free-living bodonids (Euglenozoa: Kinetoplastea) in bat ectoparasites: potential relevance to the evolution of parasitic trypanosomatids

Kinetoplastids are flagellated protozoa, including principally free-living bodonids and exclusively parasitic trypanosomatids. In the most species-rich genus, Trypanosoma, more than thirty species were found to infect bats worldwide. Bat trypanosomes are also known to have played a significant role in the evolution of T. cruzi, a species with high veterinary medical significance. Although preliminary data attested the occurrence of bat trypanosomes in Hungary, these were never sought for with molecular methods. Therefore, amplification of an approx. 900-bp fragment of the 18S rRNA gene of kinetoplastids was attempted from 307 ixodid and 299 argasid ticks collected from bats, and from 207 cimicid bugs collected from or near bats in Hungary and Romania. Three samples, one per each bat ectoparasite group, were PCR positive. Sequencing revealed the presence of DNA from free-living bodonids (Bodo saltans and neobodonids), but no trypanosomes were detected. The most likely source of bodonid DNA detected here in engorged bat ectoparasites is the blood of their bat hosts. However, how bodonids were acquired by bats, can only be speculated. Bats are known to drink from freshwater bodies, i.e. the natural habitats of B. saltans and related species, allowing bats to ingest bodonids. Consequently, these results suggest that at least the DNA of bodonids might pass through the alimentary mucosa of bats into their circulation. The above findings highlight the importance of studying bats and other mammals for the occurrence of bodonids in their blood and excreta, with potential relevance to the evolution of free-living kinetoplastids towards parasitism

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity

Semi-parametric spatial autoregressive models in freight generation modeling

This paper proposes for the purposes of freight generation a spatial autoregressive model framework, combined with non-linear semi-parametric techniques. We demonstrate the capabilities of the model in a series of Monte Carlo studies. Moreover, evidence is provided for non-linearities in freight generation, through an applied analysis of European NUTS-2 regions. We provide evidence for significant spatial dependence and for significant non-linearities related to employment rates in manufacturing and infrastructure capabilities in regions. The non-linear impacts are the most significant in the agricultural freight generation sector