Explore the vast range of titles available.

This paper aims to evaluate the foreign direct investment in North Macedonia, a small developing economy. Findings indicate that after the dissolution of Yugoslavia, North Macedonia’s policy of economic openness was generally successful and the country attracted substantial amounts of FDI by using its Technological industrial development zones and leveraging proximity to EU markets. By applying the gravity model to a panel of data, spanning 35 countries and a period of 14 years (2010-2023), this paper argues that economic dimensions of the host and source countries, geographic proximity, relative economic distance, economic integration, historical and cultural proximity, bilateral investment treaties and double taxation avoidance agreements have a positive impact on FDI. However, this paper does not find conclusive evidence that inflation rates, political corruption and innovation influence the FDI stock. In light of the need to avoid high concentration in just a few economic segments and high dependence on several source countries, the paper points out the need to simultaneously attract and diversify sources of foreign capital in order to enhance resilience to exogenous shocks. Diversified FDI stock is pivotal for achieving long-term macroeconomic stability and maintaining higher growth rates.

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross-country growth regressions using three datasets with 41-67 potential drivers of growth and 72-93 observations. Finally, we recommend priors for use in this and related contexts.

This paper provides a systematic estimation of import demand elasticities for a broad group of countries at a very disaggregated level of product detail. We use a semiflexible translog GDP function approach to formally derive import demands and their elasticities, which are estimated with data on prices and endowments. Within a theoretically consistent framework, we use the estimated elasticities to construct Feenstra's (1995) simplification of Anderson and Neary's trade restrictiveness index (TRI). The difference between TRIs and import-weighted tariffs is shown to depend on the tariff variance and the covariance between tariffs and import demand elasticities.

Industrial houses and governments of different countries and groups spend a sizeable amount of their earnings upon research and development activities to create new products and obtain patents for them. The short-run motive is to get patents, and the long-run motive is to influence income growth of the countries. The empirical findings so far are skeptical on the effects of research and development (R&D) spending. The present study further investigates the long-run associations and short-run dynamics among R&D spending, number of patents and per capita income growth in the panel of countries and groups for the period 1996–2017. Using VAR model for the panel data, the study observes that R&D spending, number of patents and per capita income growth have no long-run equilibrium relations but in the short-run, income growth and number of patents make a cause to R&D spending. However, there are weak causation from patents and R&D spending to income growth rates. The study thus recommends for controlling unfair competition on spending on R&D head and getting patents since it increases the magnitudes of social cost.

Recent swift comovements of local food and global crude oil prices have attracted the attention of policymakers and researchers. To evaluate this relationship, many studies have used time series models to explore global crude oil and local food prices. However, robust research based on advanced nonlinear time series models that incorporate control variables for their formation is lacking. In this paper, nonlinear techniques are applied to assess the asymmetric nexus between Brent oil prices and local retail food prices in Slovakia. To estimate this value, we extend the single-threshold NARDL approach to the MTNARDL model. The nominal exchange rate and industrial production index are used as the control variables. Compared with conventional NARDL models, the MTNARDL model provides a more detailed representation of global oil‒local food price linkages and detects the asymmetric effect of global oil prices on food prices from both long- and short-term perspectives. Interestingly, with respect to long- and short-term food price volatility, changes in response to oil price fluctuations are greatest under a regime with rather a small number of positive and moderate changes.

Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion. This article identifies a dilemma in these approaches: conventional formulations implicitly and unintentionally assume all firms apply uniform abatement factors. However, it is usually cost-effective to abate emissions in those firms where the marginal abatement costs are lowest. This article presents a simple formulation of weak disposability that allows for non-uniform abatement factors and preserves the linear structure of the model.

Fare and Grosskopf show that the monotonicity condition introduced by authors, including Hailu and Veeman, is consistent with the physical laws involved in the DEA model for measuring productivity. They cite that authors Hailu and Veeman's criticism of the weakly disposable model of technology is based on common misperception and misspecification of the model and that the authors fail to distinguish weak disposability from the choice of direction in which performance is measured.

This article extends the Chavas-Cox approach to non-parametric analysis by incorporating undesirable outputs to provide a more complete representation of the production technology. Inner and outer non-parametric technology bounds are constructed. The methods are illustrated with application to time series data for the Canadian pulp and paper industry. Conventional measures that ignore changes in pollutant outputs underestimate true productivity growth. Further, there is a large gap between estimates generated with reference to inner and outer bounds to the technology, suggesting that researchers need to be aware of the limitations of results derived from analyses relying only on DEA methods.

Anselin considers a few selected conceptual and practical aspects encountered in the use of spatial econometric methods in environmental and resource economics. He deals with both the valuation of air quality improvements as well as the socioeconomic factors behind deforestation.

The linearity of the U.S. hog-corn cycle has been questioned by Chavas and Holt (1991). Even so, attempts have not been made to model the potential nonlinear dynamics in the hog-corn cycle by using regime-switching models. One popular alternative is Terasvirta's smooth transition autoregressive (STAR) model, which assumes regime switching is endogenous and potentially smooth. In this article, we examine monthly data for the U.S. hog-corn cycle, 1910-2004. A member of the STAR family, the time-varying STAR, is fitted to the data and its properties examined. We find evidence of nonlinearity, regime-dependent behavior, and time-varying parameter change.