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Greenhouse gas (GHG) emissions have become a critical environmental issue with significant implications for global climate change. Understanding the factors that influence GHG emissions is essential for developing effective mitigation strategies. This study focuses on Mexico, a country that has experienced substantial economic and social changes over the past two decades. The primary objective was to analyze the impact of various economic and social variables on GHG emissions in Mexico using correlation and Vector Autoregression (VAR) analysis. The variables under consideration included Gross Domestic Product (GDP), energy consumption, population, per capita income, income inequality (measured by the Gini coefficient), and educational levels. Results showed that GDP, energy consumption, and population are positively correlated with GHG emissions and negatively correlated with income inequality. The Granger causality analysis showed that GDP and per capita income are strong predictors of GHG emissions; in contrast, income inequality and educational levels do not exhibit direct causative impacts on emissions. Finally, it was found that higher educational levels may contribute to lower GHG emissions. With this evidence, climate policies in Mexico can be formulated by addressing key areas, and policymakers can design strategies that effectively manage and reduce GHG emissions, aligning with sustainable development goals and mitigating the adverse effects of climate change.

The agricultural sector is being supported in Turkey, as well as in the world. The issue of competitiveness is observed in agriculture, despite supports. This study aims at investigating the impact of agricultural supports in Turkey on competitiveness of agricultural products. Vector autoregression (VAR) model has been adopted in the study. The internal terms of trade (TOT), percentage producer support estimate (PSE), and the producer nominal protection coefficient (NPC) variables have been included in the model. The internal terms of trade in Turkey have developed over time against the benefit of agricultural sector. PSE has had a significant impact on TOT. Therefore, the use of PSE as a political variable has been concluded as a significant. Means of support must be discussed in Turkey more than the amount of supports. In particular, supports that will provide farmers with competitive advantage and boost up product farmyard prices will be more efficient and beneficial for farmers.

This research employs a vector autoregression (VAR) analysis to explore the volatility and dynamic interactions between stock, commodity, and cryptocurrency markets. It focuses on the returns of the S&P 500, gold, crude oil, and Bitcoin to analyse their interconnections. Our results indicate that Bitcoin returns positively affect S&P 500 and crude oil, but negatively impact gold. Conversely, crude oil returns have a positive influence on gold but lead to decreased returns for Bitcoin and the S&P 500. Similarly, higher gold returns correspond to increased returns in crude oil and S&P 500 but decreased returns in Bitcoin. The rise of the S&P 500 negatively influences Bitcoin and crude oil returns, while gold returns remain unaffected. However, these relationships exhibit weak and limited strength. Including these assets in a portfolio can help risk mitigation, as Bitcoin diversifies crude oil, gold, and S&P 500, and crude oil diversifies S&P 500. These findings contribute to our understanding of global financial dynamics and inform decision-making in risk assessment, portfolio management, risk mitigation, and diversification strategies.

Like all Caribbean destinations, the Cayman Islands has two sectors of tourism: stay-over tourism and cruise-ship tourism. Before the 1990s, the official Cayman Islands' tourism strategy placed more emphasis on the stay-over sector. After the significant drop in the number of stay-over visitors in the late 1990s, the official stance shifted, placing more emphasis on the cruise-ship sector with the intent of converting cruise ship visitors into stay-over visitors. This study investigates the simultaneous relationship between the two sectors. Results suggest that in terms of the number of visitors generated to each other a) both sectors of tourism are simultaneously related, b) stay-over tourism is a substitute for cruise-ship tourism, and c) cruise-ship tourism is a complement to stay-over tourism. Policy makers should therefore note that while stay-over tourism is a substitute for cruise-ship tourism, cruise-ship is actually a complement to stay-over tourism.

Counter-seasonal imports of fresh produce facilitate year-round availability in the U.S. and may impact the seasonal structure of market price relationships. Vector autoregression analysis is used to determine the nature and extent of spatial price relationships among four geographically distinct regions in the U.S. fresh peach wholesale market. We evaluate differences in regional spatial price relationships and find statistical evidence that price relationships among regions are different in periods dominated by regional domestic supplies imports compared with periods when counter-seasonal imports dominate the market.

The grain/oilseed industry is undergoing considerable structural change through mergers and new value-added businesses, which raises price-related questions. We analyze the level of price integration prior to and following a merger between two grain firms and the start-up of a producer-owned ethanol facility. This research utilizes error correction vector autoregression analysis to compute market integration structural change effects. We find evidence that market integration initially increases with the merger, but deteriorates with time following the merger. We find no significant localized change in the level of price integration for the case of a new value-added business.

This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of the common stochastic trends and the impulse response functions, providing in each case illustrations of applicability. This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory while also revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.

We prove that local projections (LPs) and Vector Autoregressions (VARs) estimate the same impulse responses. This nonparametric result only requires unrestricted lag structures. We discuss several implications: (i) LP and VAR estimators are not conceptually separate procedures; instead, they are simply two dimension reduction techniques with common estimand but different finite-sample properties. (ii) VAR-based structural identification—including short-run, long-run, or sign restrictions—can equivalently be performed using LPs, and vice versa. (iii) Structural estimation with an instrument (proxy) can be carried out by ordering the instrument first in a recursive VAR, even under noninvertibility. (iv) Linear VARs are as robust to nonlinearities as linear LPs.

This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just-identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n-variable VAR is confined to the set of values that orthogonalize the population variance-covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse-response functions that are implicit in the traditional sign-restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just-identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.