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This research aims to analyze the impacts of the new COVID-19 pandemic on Brazilian exports of fresh beef. For this purpose, time series econometrics was used, based on the Vector Auto-Regression (VAR) model, from 1999 to 2021. In general, there is a direct variation between Brazilian beef exports and the domestic price of beef and the exchange rate. However, an inverse relationship is observed with the foreign income and pandemic proxies. The results indicate that the variables related to beef exports, internal prices, exchange rate, external income and the pandemic are significant at the 5% level. The relationship is direct for the internal price and exchange rate variables and inverse for the external income and pandemic variables. With regard to the pandemic, it was found that although the shock has an effect on exports, this effect is limited, and the behavior of the series oscillates around the original trajectory of its historical average. Likewise, from the variance decomposition, the observed impact was uniform over the quarters, being reduced in the last two estimated quarters. In the future, the determinants of Brazilian exports, specifically to the Chinese market, the segment's main trading partner in the last five years, with an important relative share in total exports, should be explored in more depth. [ABSTRACT FROM AUTHOR]

This paper aims to analyse the relationships among tourism, sustainability, and innovation in the Bangladesh economy. Based on annual data covering 1980 to 2019, we apply robust and standard time series econometric techniques and reveal that innovation and sustainability are significantly linked with tourism in Bangladesh. Furthermore, we use Computable General Equilibrium (CGE) modelling method to find the impact of the Covid-19 pandemic on the tourism industry. We find that the tourism industry is one of the highly affected industries in Bangladesh. As a recovery strategy, we reveal that innovation efficiency and government stimulus packages are much more effective in tackling the pandemic's adverse impacts. Based on the holistic analysis, we also provide key policy suggestions for the sustainable development of the tourism industry. [ABSTRACT FROM AUTHOR]

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Analysing the mass of time series data accumulating daily and weekly from the coronavirus pandemic has become ever more important as the pandemic has progressed through its numerous phases. Econometric techniques are particularly suited to analysing this data and research using these techniques is now appearing. Much of this research has focused on short-term forecasting of infections, hospital admissions and deaths, and on generalising to stochastic settings compartmental epidemiological models, such as the well-known "susceptible (S), infected (I) and recovered or deceased (R)", or SIR, model. The focus of the present paper is rather different, however, in that it investigates the changing dynamic relationship between infections, hospital admissions and deaths using daily data from England. It does this using two approaches, balanced growth models and autoregressive distributed lag/error correction models. It is found that there has been a substantial decrease over time in the number of deaths and hospital admissions associated with an increase in infections, with patients being kept alive longer, as clinical practice has improved and the vaccination program rolled out. These responses may be tracked and monitored through time to ascertain whether such improvements have been maintained.

The paper aims to develop a time series model fitted on the quarterly evolution of total budget revenues in Moldova for monitoring and forecasting purposes. While the developed model is specific to Moldova it may be of interest to use the methodology discussed in the paper in order to develop similar time series models in other countries to serve as a benchmark for monitoring and forecasting budget revenues. Following a brief analysis of the properties and estimation of time series models, the paper presents the data set to be used for the estimation exercise and analyses the data’s stationarity and the correlogram of the stationary series to be modelled. The data sample comprises the quarterly evolution of total budget revenues in Moldova from the first quarter of 2016 to the first quarter of 2023. The paper proceeds to provide the econometric estimates of the preferred time series model, as well as use the estimated model to generate the forecast of the quarterly evolution of budget revenues from the second quarter of 2023 to the fourth quarter of 2024. The model’s annual forecast of budget revenues for 2023 is slightly more optimistic than the Ministry of Finance’s estimate for 2023 contained in the recently approved Medium Term Budget Framework document and is almost identical with the projection of the International Monetary Fund contained in its latest country report for Moldova. The paper concludes by summarising the uses and limitations of time series models for monitoring and forecasting purposes and suggesting areas for further work.

The demand for international tourism in Australia and New Zealand is vital to the South Pacific's tourism-reliant islands. However, at the time of this study these two countries find themselves in precarious economic situations. The question addressed by this study is, will tourism imports in these two countries pick up on the back of economic recovery? We answer this question using time-difference analysis and the newly developed Hamilton filter. The short answer is yes, but more so in New Zealand than Australia. The key findings of this study are that tourism demand in both Australia and New Zealand is pro-cyclical, tourism demand cycles in New Zealand strongly lag business cycles by 1 year, whereas in Australia, they weakly lag business cycles by one quarter, and, overall, tourism demand and business cycles in New Zealand share a stronger association than they do in Australia. [ABSTRACT FROM AUTHOR]

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed. [ABSTRACT FROM AUTHOR]

Recent advances in supply chain research point to the vital but often overlooked role of financial institutions, such as banks and financial markets, in the execution of supply chain activities. We extend this incipient research stream by drawing on information economics and Penrose's resource‐based view of the firm to theorize about how financial markets act as a source of information and influence exchange activities in the agricultural commodity supply chain. We test our hypotheses in the U.S. agricultural commodities context, specifically the U.S. rice industry, using a novel data set that combines financial market data with proprietary data on exchanges between farmers and customers. Time series econometric analyses reveal that information from financial markets influences exchanges between farmers and customers, but it has asymmetric effects depending on exchange dynamics and local market conditions. Overall, our analyses support our hypotheses and advance supply chain research by building theory about market‐based exchange dynamics and by broadening the scope of mechanisms through which financial institutions impact exchange activities. For practice, we offer quantitative insights that can be leveraged by farmers and purchasing managers. For policymakers, we offer timely guidance related to the provision of information and the 'real' implications of financial markets. [ABSTRACT FROM AUTHOR]

The empirical literature has studied linkages in the cryptocurrency market because knowing how shocks pass from one currency to another helps policymakers and practitioners better counter their propagation in these and related markets. This paper contributes to this literature by proposing a methodology based on Granger causality and network analysis. Using the daily log-returns of 22 cryptocurrencies over the period 2018–2023, I develop a VAR model to infer unidirectional or bidirectional Granger causality among cryptocurrencies. These relationships are then transformed into a directed network and several centrality measures are calculated. The centrality measures are also observed over the years to understand the dynamics of the cryptocurrency network. I find out that each one unit increase in eigencentrality is associated with a 0.22 percent increase in log-returns. Cryptocurrencies are nontrivially connected, and in this sample Cardano, Dogecoin, Gridcoin, and Neo are amongst the most central in the network throughout the period. Some cryptocurrencies, such as Dogecoin or Neo, show decreasing centrality over the years, while others, such as Gridcoin, Litecoin, Namecoin, or Ripple, gain centrality. These results support the idea that the cryptocurrency market is no longer exclusively associated with Bitcoin and lay the groundwork for further study of shock propagation in financial markets. [ABSTRACT FROM AUTHOR]