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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.

This study examines whether user-generated content (UGC) is related to stock market performance, which metric of UGC has the strongest relationship, and what the dynamics of the relationship are. We aggregate UGC from multiple websites over a four-year period across 6 markets and 15 firms. We derive multiple metrics of UGC and use multivariate time-series models to assess the relationship between UGC and stock market performance. Volume of chatter significantly leads abnormal returns by a few days (supported by Granger causality tests). Of all the metrics of UGC, volume of chatter has the strongest positive effect on abnormal returns and trading volume. The effect of negative and positive metrics of UGC on abnormal returns is asymmetric. Whereas negative UGC has a significant negative effect on abnormal returns with a short \"wear-in\" and long \"wear-out,\" positive UGC has no significant effect on these metrics. The volume of chatter and negative chatter have a significant positive effect on trading volume. Idiosyncratic risk increases significantly with negative information in UGC. Positive information does not have much influence on the risk of the firm. An increase in off-line advertising significantly increases the volume of chatter and decreases negative chatter. These results have important implications for managers and investors.

【Objective】 Net Primary Productivity (NPP) is a key indicator of ecosystem carbon cycle. It is influenced by both climate change and human activities. This paper analyzes the spatiotemporal dynamics of NPP from 1983 to 2021 in the Yellow River Water-pumping Irrigation Areas of Ningxia, distinguishes the contributions of climate change from human activities to NPP.【Method】Using the CASA (Carnegie-Ames-Stanford Approach) model, scenario simulations, structural equation model (SEM), and a vector autoregression (VAR) model, we assessed the NPP trends, identified key influencing factors, and explored the interactions between NPP, socio-economic factors and irrigation infrastructures.【Result】① NPP in the region increased significantly during the study period, with an average annual growth rate of 10.95 g/(m2·a). Human activities were the dominant influencing factor, contributing 70.27% to the NPP changes, compared to 29.73% from climate changes. ② Among climate-related factors, precipitation, solar radiation, evapotranspiration and irrigation water most strongly influenced NPP. In human factors, irrigation water and soil moisture were the key drivers. ③ Socio-economic development positively influenced NPP, while the irrigation projects significantly boosted socio-economic progresses, indirectly enhancing NPP. 【Conclusion】 NPP in the study areas shows noticeably spatiotemporal variation, driven mainly by human activities and water-temperature conditions. The Yellow River Water-pumping Irrigation Project plays a vital role in supporting both ecological health and economic development in these areas.

The urgency of transitioning to sustainable energy has accelerated amid climate change concerns and fossil fuel depletion. This study introduces a novel comparative framework that integrates Time-Varying Parameter Vector Autoregression (TVP-VAR) and Quantile Vector Autoregression (QVAR) models to examine both returns and realized volatility across renewable-energy tokens (Powerledger and Wepower), clean-energy stocks, and crude oil. This dual-method approach uniquely captures time-varying and tail-specific spillovers, extending previous studies that relied on a single model or ignored volatility interactions. Using daily data from February 2018 to January 2023, we reveal moderate but significant interconnectedness—about 30% on average—with stronger linkages during global crises such as COVID-19 and the Russia–Ukraine conflict. Renewable-energy tokens act mainly as net receivers of shocks, implying their role as protective diversification assets, while clean-energy stocks are net transmitters and oil alternates between both roles. These results highlight how digital assets interact with traditional energy markets under varying conditions. The study offers practical implications for portfolio diversification and emphasizes the need for transparent, supportive regulation to prevent tokens from amplifying systemic risk while promoting the stability of sustainable-energy investment markets.

The short-run increase in prices following an unexpected tightening of monetary policy constitutes a puzzle frequently reported in empirical studies. Yet the puzzle is easy to explain away when all published models are quantitatively reviewed. We collect and examine about 1,000 point estimates of impulse responses from 70 articles that use vector autoregressions to study monetary transmission in various countries. We find that the puzzle is created by model misspecifications: especially by the omission of commodity prices, neglect of potential output, and reliance on recursive identification. Our results also suggest that the strength of monetary policy depends on the country's openness, phase of the economic cycle, and degree of central bank independence.

This study attempts to examine the convergence development of the marine fishery (MF) and marine tourism (MT) industries of Japan through the theory of industrial relevance. First, the current MF and MT situation in Japan is introduced to analyze the mechanism of the integration of the two industries. Second, a Vector Autoregression Model (VAR) is built to examine the relationship between MF and MT. In addition, the shock potential contributions of the MF and MT industries are identified using impulse response and variance decomposition. Results show that the impact of MF on MT is more significant than that of MT on MF. However, the interaction between MF and MT tends to stabilize in the long run. Third, the industrial integration case of Japan’s Himakajima Island is selected to analyze the MF and MT integration mechanism. The integration of MF and MT can reduce transaction costs, make full use of labor, and promote the development of the local economy. Therefore, attention should be paid to the integration of the MF and MT industries, rather than partial implementation, to balance the development of the marine economy. Finally, relevant suggestions and measures are presented for marine industry transformation and upgrading, industrial integration, and green ecological development.

In this paper, the impact of monetary policy on industrial production is investigated for Malawi. Using the ARDL bounds testing approach, and VAR models, it is shown that tight monetary conditions negatively affect industrial production both in the short run and in the long run. This is the case whether the central bank's policy rate or reserve money is used as the policy tool. The study further establishes the interest rate channel, and money supply channel as the main mechanisms through which this effect of monetary policy is transmitted to industrial production. Given these results, a recommendation is made that the Reserve Bank of Malawi should refrain from prolonged use of tight monetary policy in their quest to achieve stability of prices as this stifles growth of the industrial sector. Rather monetary policy should be used as a temporary stabilization tool when faced with temporary shocks to the bank's policy objectives.

In recent years, the external shock represented by COVID-19 has caused significant fluctuations in global corn prices. Based on the weekly data on international corn prices from 2020 to 2023, this paper constructs autoregressive conditional heteroskedasticity (ARCH) class and time-varying parameter – vector autoregression (TVP-VAR) models. After analysing the characteristics of corn price fluctuations, it further analyses the influence of external uncertainties such as COVID-19, international finance, the corn futures market, and international exports of corn on corn price fluctuations. The results show that international corn price fluctuations always have significant asymmetry. Nevertheless, the influence of past changes on the future will gradually disappear, and the corn market is not characterised by high risk and high return because of the phenomenon of flat or declining absolute returns during the periods of high volatility. All the selected external shocks also have a time-varying impact on corn price fluctuations, and there are differences in the impact size, impact direction, and impact duration. The external shocks led by COVID-19 had a transmission effect on other factors and then affected corn price fluctuations.

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.

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.