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The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

We propose a new Vector Autoregression (VAR) identification strategy to study the impact of noise, in the early releases of output growth figures, which exploits the informational advantage of the econometrician. Economic agents, uncertain about the underlying state of the economy, respond to noisy early data releases. Econometricians, with the benefit of hindsight, have access to data revisions as well, which we use to identify noise shocks. A surprising report of output growth produces qualitatively similar but quantitatively smaller effects than a demand shock. We also illustrate how a noise shock cannot be identified unless ex-post information is used.

This research aimed to measure and analyze the impact of exchange rate shocks on some variables of the Iraqi economy during (1990-2022), because of the different effects of these shocks on the macroeconomic variables represented in money supply and agricultural output. Exchange rates are related to the policy chosen by the Central Bank of Iraq in managing the foreign exchange market and in the rentier nature of the Iraqi economy. The research uses a quantitative method in estimating the levels of the impact of exchange rate shocks on some economic variables. Several methods are conducted to achieve the goals, including the VAR model, variance decomposition and Impulse Response Functions. The results showed that exchange rate variance reached 100% in the same variable in the first year and decrease to 97% at the end of the period, and the same in the impulse response. It was an internal reaction that predicts what can be called the self-wave of an exchange rate rise, while both the variation and the response to the money supply shock in Iraq were dependent on the policy of the Central Bank and compatible with what was happening in the exchange rate, as the analysis of variance in the first year reached 38.98% for the same variable and 61% of it is due to the exchange rate. The results also showed that agricultural output was weakly affected by the exchange rate shock and money supply.

This study aimed to investigate the impact of agricultural TOT shocks and the strength of their effects on some indicators of the agricultural sector. The research uses VAR, Variance decomposition and IRF approaches. The research is due to the almost monolithic exports and the multiplicity of imports, and the results of the agricultural product response to the shock of the agricultural gross terms of trade (GBTT) indicated that it had a negative impact, due to the shocks that the last variable was subjected to, which was reflected on an agricultural product, but they are not permanent as they returned to the normal situation within the sixth year. The research recommended adopting the TOT criterion as one of the criteria for drawing agricultural development plans in Iraq and one of its basic indicators to link the import capacity with the export capacity, as well as the need to pay attention to reviving agricultural exports to Iraq to stimulate agricultural terms of trade and then agricultural investment.

This study evaluates long-run relationships and short-run linkages between the private (unsecuritized) and the public (securitized) real estate markets of Australia, Netherlands, United Kingdom and the United States. Results indicate the existence of long-run relationships between the public and private real estate markets of each of the countries under consideration. This implies that for all countries, investors would not have realized long-term portfolio diversification benefits from allocating funds in both the private and public real estate markets since these assets are substitutable over the long run. Short-run analyses also reveal significant causal relationships between private and public markets of all countries under consideration. As expected, it was found that price discovery occurred in the public real estate market in that it leads but is not led by its private real estate market counterpart.

The causes of the housing bubble are investigated using Granger causality analysis and VAR modeling methods. The study employs the S&P/Case-Shiller aggregate 10 city monthly housing price index, available in the period 1987–2010/8, the 20 city monthly housing price index for 2000–2010/8, and the federal funds rate data for the period 1987–2010/8. The findings are consistent with the view that the interest rate policy of the Federal Reserve in the period 2001–2004 that pushed down the federal funds rate and kept it artificially low was a cause of the housing price bubble.

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.

Carbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time‐dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time‐dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios. Plain Language Summary Carbon dioxide (CO2) emissions impact surface temperature. It is well established that the global mean temperature change is proportional to the cumulative emissions of CO2. This has led to the creation of carbon budgets to reach temperature goals. We test this relationship at the spatio‐temporal scale, quantifying a simple approach that estimates the local temperature response to CO2 emissions alone. We use an approach built from the Climate Model Intercomparison Project Phase 6 (CMIP6) Earth System Models, based on the concept that an additional unit of CO2 can be scaled for larger emissions and summed over time to estimate cumulative impacts. We evaluate this with additional CMIP6 experiments, showing that this approach captures the temperature response in most locations with lower accuracy in the Arctic and Southern Ocean. This type of approach may be useful to evaluate many policy scenarios and to better understand earth system processes that are represented in the models, as it takes around one second to quantify 90 years' worth of temperature change on a local computer, while Earth System Models can require weeks of runtime on supercomputers. Key Points With a Green's Function approach, we emulate the linear component of the spatially resolved temperature response to CO2 emissions We reproduce the temperature response well within multi‐model uncertainty except in the Arctic and Southern Ocean This approach allows expedient quantification of the spatial and temporal temperature response to varying CO2 emissions pathways

We develop flexible semiparametric time series methods for the estimation of the causal effect of monetary policy on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macrodynamic setting, without the need for assumptions about the process generating macroeconomic outcomes. The proposed estimation strategy, based on propensity score weighting, easily accommodates asymmetric and nonlinear responses. Using this estimator, we show that monetary tightening has clear effects on the yield curve and on economic activity. Monetary accommodation, however, appears to generate less pronounced responses from both. Estimates for recent financial crisis years display a similarly dampened response to monetary accommodation.

Impulse response functions (IRF), the response in a climate parameter to an emission pulse of CO2, are used to characterize Earth system response timescales and to calculate Global Warming Potentials (GWPs). GWPs are widely used to compare emissions of different greenhouse gases and to compute CO2 equivalent emissions as reported by governments to the United Nations Framework Convention on Climate Change (UNFCCC). The GWP of any gas x is the absolute GWP of gas x Absolute and relative Global Warming Potential (AGWPx) divided by AGWP of CO2. Ideally, AGWP CO2 and GWPx would be independent of atmospheric CO2 and climate. However, AGWP CO2 , and, in turn, GWPx change under rising atmospheric CO2 and global warming, affecting the emission reporting under the UNFCCC. Here, we apply perturbed parameter ensemble simulations, constrained in a Bayesian approach by observational data, to investigate how AGWP CO2 and IRF vary under different atmospheric background CO2 levels (CO 2,bg ). We provide analytical formulations to compute AGWP CO2 and IRF for CO2, ocean and land carbon uptake, global mean surface air temperature, steric sea level, and ocean heat content, and to adjust these metrics to different CO 2,bg . AGWP CO2 , given by the time-integrated response in CO2 at year 100 multiplied by its radiative efficiency, is 101.8(±13.5) 10−15 yr W m−2 kg-CO 2−1 for CO 2,bg = 425 ppm and decreases by 7% for CO 2,bg = 500 ppm. The decrease is driven by a decrease in the radiative efficiency of CO2, partly canceled by a concomitant increase of IRF CO2 due to muted ocean and land carbon uptake under higher CO2 levels. We recommend regularly adjusting AGWP CO2 and, in turn, GWPs of long-lived gases to contemporary atmospheric CO2 and climate.