The purpose of this paper is to analyse the effects of natural resources on income inequality conditional on economic complexity in 111 developed and developing countries from 1995 to 2016. The system-GMM results show that economic complexity reverses the positive effects of natural resource dependence on income inequality. Furthermore, results are robust to the distinction between dependence on point resources (fossil fuels, ores, and metals), dependence on diffuse resources (agricultural raw material), and resource abundance. Finally, there are significant differences between countries, depending on the level of ethnic fragmentation and democracy.

Risk measurements are clearly central to risk management, in particular for banks, (re)insurance companies, and investment funds. The question of the appropriateness of risk measures for evaluating the risk of financial institutions has been heavily debated, especially after the financial crisis of 2008/2009. Another concern for financial institutions is the pro-cyclicality of risk measurements. In this paper, we extend existing work on the pro-cyclicality of the Value-at-Risk to its main competitors, Expected Shortfall, and Expectile: We compare the pro-cyclicality of historical quantile-based risk estimation, taking into account the market state. To characterise the latter, we propose various estimators of the realised volatility. Considering the family of augmented GARCH(p, q) processes (containing well-known GARCH models and iid models, as special cases), we prove that the strength of pro-cyclicality depends on the three factors: the choice of risk measure and its estimators, the realised volatility estimator and the model considered, but, no matter the choices, the pro-cyclicality is always present. We complement this theoretical analysis by performing simulation studies in the iid case and developing a case study on real data.

While the linkage between team performance and attendances is well established, there has been negligible previous research using club memberships as an alternative indicator of demand for sport. Little attention has been paid to how the number of memberships is affected by common measures of team performance, such as the team’s win-ratio. This study utilises a previously unavailable long range time-series data set of annual memberships for an Australian Football League (AFL) club, Hawthorn FC. A succession of basic correlation analyses demonstrates that, while the relation between club membership numbers and win-ratios is strongly positive as it is for attendances (for most of the sample), some of the finer properties are substantially different. It is suggested that much of the reason for this lies in differences between the segmented nature of these markets for attendances and memberships.

This paper uses a difference-in-differences approach to analyze the treatment effect of a hail weather shock in a specific Swiss wine-growing region. We exploit a natural experiment from Switzerland's Three Lakes wine region in 2013 and examine its impact on the country's retail market. We find statistically significant (1%-level) effects of –22.8% and +2.8% for the volume and price of wine consumed, respectively. These effects can be interpreted as average treatment effects, which is the difference in outcomes between treatment and control groups using a pre-post shock study methodology. Several robustness checks confirm the statistical significance of the estimated effects and the initial assumptions.

The study applies parametric and nonparametric estimation methods to determine hedonic prices of rice quality attributes, and a partial equilibrium model to determine the payoff to investing in quality improvement in five countries in Sub-Saharan Africa. Results indicate that consumers are willing to pay price premiums for head rice, slender grains, peak viscosity, parboiled rice, and rice sold in urban markets. However, they strongly discount amylose content, rice with impurities and imported rice. Investing in quality improvement through amylose content reduction leads to net welfare gains with a benefit-cost ratio of 47.86 and internal rate of return of 90%.

We estimate a travel cost model for the George Washington & Jefferson National Forests using an On-Site Latent Class Poisson Model. We show that the constraints of ad-hoc truncation and homogenous preferences significantly impact consumer surplus estimates derived from the on-site travel cost model. By relaxing the constraints, we show that more than one class of visitors with unique preferences exists in the population. The resulting demand functions, price responsive behaviors, and consumer surplus estimates reflect differences across these classes of visitors. With heterogeneous preferences, a group of ‘local residents’ exists with a probability of 8% and, on average take 113 visits.

A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data. We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.

To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.

Are institutions a deep cause of economic growth? This paper tries to answer this question in a novel manner by focusing on within-country variation, over long periods of time, using a new hand-collected data set on institutions and the power-ARCH econometric framework. Focusing on the case of Brazil since 1870, our results suggest (a) that both changes in formal political institutions and informal political instability affect economic growth negatively, (b) there are important differences in terms of their short- versus long-run behaviour, and (c) not all but just a few selected institutions affect economic growth in the long-run.

This paper provides a toolbox for the credibility analysis of frequency risks, with allowance for the seniority of claims and of risk exposure. We use Poisson models with dynamic and second-order stationary random effects that ensure nonnegative credibilities per period. We specify classes of autocovariance functions that are compatible with positive random effects and that entail nonnegative credibilities regardless of the risk exposure. Random effects with nonnegative generalized partial autocorrelations are shown to imply nonnegative credibilities. This holds for ARFIMA(0, d, 0) models. The AR(p) time series that ensure nonnegative credibilities are specified from their precision matrices. The compatibility of these semiparametric models with log-Gaussian random effects is verified. Gaussian sequences with ARFIMA(0, d, 0) specifications, which are then exponentiated entrywise, provide positive random effects that also imply nonnegative credibilities. Dynamic random effects applied to Poisson distributions are retained as products of two uncorrelated and positive components: the first is time-invariant, whereas the autocovariance function of the second vanishes at infinity and ensures nonnegative credibilities. The limit credibility is related to the three levels for the length of the memory in the random effects. The limit credibility is less than one in the short memory case, and a formula is provided.

This article explores the global cycle hypothesis by testing whether the US stock market serves as an explanatory variable for the evolution of expansions and contractions in the UK stock market from 1922 until 2016. Alternatively, it tests an index that groups the stock markets of advanced economies to identify whether this driving force is international. Second, regarding co-movement with the US, the article explores whether its time-varying nature is contingent on the domestic and international economic policy regimes. I find evidence that there is a strong and contemporaneous co-movement between the US and UK stock markets. Additionally, through a VAR model, I identify that the movements in the UK stock market cause, in the Granger sense, changes in the index for advanced economies up to two years later. Furthermore, in the short-run co-movement between the US and UK stock markets is contingent on the macroeconomic trilemma while, in the long run, both domestic and international policy regimes affect the relationship. A final contribution is the design of a new methodology for describing the evolution of financial time series as risk-adjusted above or below average returns to different time horizons: the Local Bull Bear Indicators (LBBIs).

We use models incorporating the normalized difference vegetation index (NDVI) derived from remote sensing satellites to improve soybean yield predictions in 10 major producing states in the United States. Unlike traditional methods that assume an ordinary least squares model applies to all observations, we allow for global flexibility in the relationship between NDVI and soybean yield by using the flexible Fourier transform (FFT) model. FFT results confirm that there is a nonlinear response of soybean yield to NDVI over the growing season. Out-of-sample predictions indicate that allowing for global flexibility with the FFT improves the predictions in time-series prediction and forecasting.

In this paper, we propose an estimator of the survival probability for a Lévy risk model observed at low frequency. The estimator is constructed via a regularised version of the inverse of the Laplace transform. The convergence rate of the estimator in a sense of the integrated squared error is studied for large sample size. Simulation studies are also given to show the finite sample performance of our estimator.

The U.S. Department of Agriculture (USDA) releases crop condition reports detailing crop progress and growing conditions for various crops including corn, soybeans, and winter wheat. Previous research has investigated market impacts from various USDA reports, but crop condition reports have received little attention. This article investigates the impact of crop condition reports on winter wheat prices at the national level and for a local market. We employ both parametric and nonparametric tests. Results suggest that crop condition reports for winter wheat do not generally affect market prices. This contrasts with results found in corn and soybean markets.

This study utilized a semiparametric panel model to estimate environmental Kuznets curves (EKC) for carbon dioxide (CO2) in 15 Latin American countries, using hitherto unused data on forestry acreage in each country. Results showed an N-shaped curve for the region; however, the shape of the curve is sensitive to the removal of some groups of countries. Specification tests support a semiparametric panel model over a parametric quadratic specification.

This paper presents an extension of Chamberlain's approach to modelling correlated random effects in a dichotomous model estimated on panel data. The extension consists in relaxing the arbitrary normality assumption made on all error terms and on specifying instead a SNP distribution for the composite error term resulting in each wave from Chamberlain's treatment of correlated random effects. An application to a panel of 1325 German firms illustrates the feasibility of the approach.

The purpose of this paper is to investigate whether shocks to output and employment had permanent or transitory effects during the period from 1961 to 1990 in the Federal Republic of Germany. There are a lot of parametric and non-parametric approaches which can be used for data generating processes which are difference-stationary or trend-stationary. But especially output and employment time series show very strong seasonal movements. Before employing usual persistence measures one has to test for seasonal integration. The application of seasonal adjustment procedures which are typically used in practice lead to different degrees of persistence, so one has to be very careful in interpreting the results.