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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.

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 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.

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.

The present paper provides a comprehensive and consolidated analysis of the business cycle synchronicity between European regions and EU-14. Our study is conducted in three levels. First, we analyse regional business cycle synchronization with the EU-14 benchmark cycle, using real GDP in 200 NUTS II regions for a period of 30 years (1980–2009), detrended by Hodrick–Prescott filter. Secondly, we employ a VAR type methodology as a measurement devise to examine the dynamic relationship of the regional business cycles. Our main interest is to study the dynamics of business cycles as well as the pattern of the transmission mechanism to regions with different level of development. Finally, we empirically extend the research on identifying factors which might drive regional business cycle synchronization. In particular, we analyse the role of trade integration-cum- the sectoral patterns of specialisation as determinants of regional growth cycle correlations with the EU-14. Moreover, we draw attention to regional productivity as another possible determinant of business cycle synchronisation associated with the pattern of the spatial distribution of economic activities across regions. Panel three-stage least-squares estimation is implemented for the simultaneous equations between determinants and regional business cycles synchronisation.

The author proves that Wold-type decompositions with strong orthogonal prediction innovations exist in smooth, reflexive Banach spaces of discrete time processes if and only if the projection operator generating the innovations satisfies the property of iterations. His theory includes as special cases all previous Wold-type decompositions of discrete time processes, completely characterizes when non-linear heavy-tailed processes obtain a strong-orthogonal moving average representation, and easily promotes a theory of non-linear impulse response functions for infinite-variance processes. The author exemplifies his theory by developing a non-linear impulse response function for smooth transition threshold processes, and discusses how to test decomposition innovations for strong orthogonality and whether the proposed model represents the best predictor. A data set on currency exchange rates allows him to illustrate his methodology. /// L'auteur démontre que pour qu'un espace de Banach réflexif lisse de processus à temps discret admette une décomposition de type Wold à innovations prévisionnelles fortement orthogonales, il faut et il suffit que l'opérateur projectif engendrant les innovations possède la propriété d'itérations. Son résultat, qui englobe comme cas particuliers toutes les décompositions de type Wold obtenues antérieurement pour des processus à temps discret, caractérise complètement les situations où les processus non linéaires à queues lourdes possèdent une représentation en moyenne mobile fortement orthogonale, en plus de conduire naturellement à une théorie des fonctions réponses à impulsion non linéaire pour les processus à variance infinie. Il se sert de cette théorie pour développer une fonction à impulsion non linéaire pour des processus à seuil de transition lisse et montre comment tester l'orthogonalité forte des innovations de la décomposition et vérifier si le modèle proposé est bel et bien le meilleur prédicteur. Un jeu de données sur des taux de change de devises lui permet d'illustrer son propos.

Vector Error Correction Model is a cointegrated VAR model. This idea of Vector Error Correction Model (VECM), which consists of a VAR model of the order p - 1 on the differences of the variables, and an error-correction term derived from the known (estimated) cointegrating relationship. Intuitively, and using the stock market example, a VECM model establishes a short-term relationship between the stock prices, while correcting with the deviation from the long-term comovement of prices. An Impulse Response Function traces the incremental effect of a 1 unit (or one standard deviation) shock in one of the variables on the future values of the other endogenous variables. Impulse Response Functions trace the incremental effect of the marketing action reflected in the shock. The data used in this analysis are 4 (four) daily plantation stocks prices in Indonesia with time period of January to July in three years which are 2018, 2019, and 2020. The objective of this study is to determine the relationship among 4 (four) stocks prices with VECM and to know the behaviour of each stocks prices with Impulse Response.