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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]

Copyright of Ensayos de Economia is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This comparative study is an attempt to explore the determinants of capital structure for Malaysian firms listed in various sectors level. Within the framework of traditional and moderate dynamic capital structure theories, the key determinants such as fixed assets, current assets, return on equity, size, earning per share and total assets are tested in relation to the debt-equity ratio. The large-scale study entails data collected from 551 listed firms of Bursa Malaysia main market over 12 years period i.e. 2005-2016. Notably, this study combines Time Series econometrics with Panel Data analysis to enhance methodological robustness. Moreover, the comparative analysis approach is designated to recognize the most persistent capital structure determinants. In the first place, the Multiple Regression analysis (MRA) is selected as a baseline estimation method. Subsequently, the Auto Regression Distributed Lag model (ARDL), the Panel Data Static models, and Dynamic model via the Generalized Method of Moments (GMM) are employed to identify the capital structure determinants for the firms listed at Bursa Malaysia. The outcomes are surprising and indicate that the entire market is primarily controlled by the studied determinant total assets, which is significant in both construction and property sectors through MRA, ARDL, and GMM analysis. Technically, the significant role of tangibility and the existence of speed of adjustment across sectors imply that the Dynamic Capital Structure is the most prominent among all, followed by the Dynamic Trade-off theory.

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]

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 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 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 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]

Covid-19 pandemic has adversely affected the different industrial sectors of Indian economy. It has thus adversely disturbed the parameters of the sustainable development in this country. This paper shows how different macroeconomic sectors of India have reacted to the adverse impact in the Indian Economy differently. Some sectors are highly affected while some sectors are not appreciably affected by the pandemic. Few major sectors have been affected badly by the pandemic. For example the highest impact on industrial growth during the pandemic has been assessed for the sectors like, mining -26.9%, manufacturing -66.6%, infrastructure -37.9%, steel - 65.2%, cement - 40%, crude oil -5.22% etc. I have studied in this paper the impact of Covid-19 pandemic on different sectors and got different findings for different sectors. The study involved with the Cointegration and Vector Error Correction Estimation of Time Series Econometrics. It may be noted that the pandemic has been going on for one and half year and therefore it is expected to be a transitory phenomenon. We, therefore, cannot expect a very long run relationship between pandemic and other sectors of the economy. That's why, the Cointegration study has been indirectly established through the estimation of Vector Error Correction model. In the estimated VEC model it has been found that there is a strong relation between changes in macroeconomic parameters and the pandemic in the short run. I have found that the growth rate in manufacture, infrastructure and mining etc have been affected adversely by the pandemic. Growth rate of different sectors have been significantly related to the 1st and 2nd lag variation of the Covid-19 dataset. The Covid-19 dataset have been found to exert negative impact on the sectors under study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article explores how media coverage of a price war affects customer, retailer, and investor reactions over time. Using data covering a Dutch supermarket price war (2003-2005), the authors find that price reductions, especially deep reductions, trigger media coverage of the price conflict. This sets off a chain of reactions. Press messages have a significant effect on market share and abnormal stock returns, beyond retailers' own price and advertising. Importantly, this study uncovers striking asymmetries regarding the kind of coverage to which stakeholders react: whereas consumers only respond to the tone of pricerelated press coverage, retailers and investors only react to its quantity. Next, media coverage feeds back into the retailers' pricing actions: more media coverage triggers new price cuts in addition to those dictated by competitive reactions. As such, media coverage triggers a deeper spiral of price cuts, intensifying the competitive price battle. However, as the price war progresses, media coverage becomes less frequent and less favorable, which decelerates the downward price spiral. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a longstanding belief among motion-pictures industry observers that box-office revenues are largely unaffected by poor macroeconomic conditions. This belief, which primarily grew out of the success of the film industry in the early years of the Great Depression, has been pervasive in the industry, and, to a lesser degree, among academics, despite a lack of empirical evidence to support it. This paper seeks to empirically evaluate the effect of recessions on box-office revenues of the U.S. motion-pictures industry using data from Box Office Mojo and the St. Louis Federal Reserve Economic Database from 1990 to 2019. This period, which includes three periods of recession, is analyzed using a number of time-series econometric methods. Results show that macroeconomic fluctuations do have a significant effect on film-industry revenues and a declining economy curtails growth, as measured in terms of the mean of revenue and persistence over time. The depth of a recession appears to be an especially important determinant. These results inform future models of the film industry by highlighting the importance of macroeconomic effects on the industry as a whole. [ABSTRACT FROM AUTHOR]

This paper reexamines the role of the Federal Reserve in triggering the recent housing crisis. Specifically, we explore if the relationship between the federal funds rate and the housing variables underwent structural changes in the wake of the housing crisis. Using quarterly data spanning 1960-2017, we estimate a VAR model involving federal funds rate, real GDP growth and a housing variable (captured by house price inflation or residential investment share or housing starts) and conduct time series analysis for the pre- and post-crisis periods. While previous studies mostly set break-dates based on events known a priori to split the full sample to subsamples, we endogenously determine structural break points occurring at multiple unknown dates. Our Granger causality analysis indicates that the federal funds rate did not cause house price inflation, although it caused residential investment share and housing starts in the pre-crisis period. In the post-crisis period, the real GDP growth caused residential investment and housing starts while house price inflation had a momentum of its own. Our impulse response and forecast error variance decomposition analysis reinforce these results. Overall, our findings suggest that housing volume fluctuates more than house prices over the business cycle. [ABSTRACT FROM AUTHOR]

California legislation outlawed the use and sale of battery cages for egg-laying hens in 2015. While a number of ex ante studies projected the effects of the housing prohibitions, the ultimate ex post effects are unknown. Using a price series reported by the USDA, we study the movement of daily egg prices in California and the United States before and after the law's implementation. Depending on the methods used, we find that Californians now pay between $0.48 and $1.08 more for a dozen eggs. The estimates suggest an annual reduction in California consumer surplus of between $400 million and $850 million.

RESUMO Este artigo tem como objetivo investigar empiricamente quais os principais condicionantes das emissões de CO2 no Brasil no período 1971-2011. Além disso, este trabalho procura evidências da hipótese da Curva de Kuznets Ambiental (CKA) para o Brasil. As variáveis utilizadas para explicar as emissões de CO2 foram a renda per capita, consumo per capita de energia, densidade populacional e o grau de abertura da economia. Também foi adicionada uma dummyexógena ao modelo para captar os efeitos do protocolo de Kyoto sobre as emissões brasileiras. O trabalho utilizou-se da metodologia de vetores autorregressivos com correção de erros (modelos VEC) para atingir seu principal objetivo. Os resultados do modelo VEC, para o longo prazo, apontaram para significância estatística de todas as variáveis explicativas do modelo. Além disso, não foram encontradas evidências de uma CKA para o Brasil. Ao contrário, encontraram-se evidências de uma curva no formato de “N”, mostrando que as emissões de CO2 diminuem apriori, mas voltam a subir a partir de determinado patamar de renda per capita. O ponto de máximo da curva ocorre quando a renda per capita atinge US$ 2.729,27 e o ponto de mínimo se dá quando a renda per capita chega a US$ 9.122,54.

Through the structural decomposition developed by Blanchard and Quah (1989), this paper investigates how the local supply and demand impulses and the monetary policy of the United States affect the evolution of economic variables such as the unemployment rate and inflation. The variables to be used arise from the development of a theoretical model that incorporates aspects of the particularities of the economy of Puerto Rico with the economic theory. According to the results the shocks of supply and demand have transitory effects, however, the effects of monetary policy remain persistent. Aspects related to the regional and sectorial effects of monetary policy, as well as the way in which the economic system is constituted, the scope of fiscal policy and the use of monetary rules are relevant aspects when analyzing empirical evidence and also represent a contribution in examining the effects of US monetary policy on unincorporated regions and territories. [ABSTRACT FROM AUTHOR]

Copyright of Economía Coyuntural, Revista de Temas de Coyuntura y Perspectivas is the property of Instituto de Investigaciones Economicas y Sociales Jose Ortiz Mercado (IIES-JOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the existence of β-Convergence between per-capita incomes of selected Islamic countries. For this purpose, data over the period 1965-2006 and a time series approach proposed by Vogelsang (1998) are applied. Robustness of the estimated parameters to the presence of unit roots and/or serial correlations in the residuals is the main advantage of this method. The results show that per-capita income of most countries is converging to the average per-capita income of the selected Islamic countries, which provide evidence of β-Convergence. Cameroon, Indonesia, Malaysia, Niger, Chad, and Togo are the countries that have shown some forms of divergence either before the break date or after that. The estimated break dates are clustered and mostly related to the energy shocks in 1974, 1979, and 1986.

In this study, dynamic links between central bank reserves (CBR), credit default swap (CDS) spreads, and foreign exchange (FX) rates are investigated. So, Turkey, which is a negative outlier country among other peer emerging countries, is examined by considering recent developments on these indicators. In doing so, the study covers relatively high frequency (i.e., weekly) data from January 2, 2004 to November 12, 2021, performs various econometric approaches as Wavelet Coherence (WC), Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ) as main models, and applies Toda-Yamamoto (TY) causality and Quantile Regression (QR) for the robustness. The results show that (i) there is a time-frequency dependency between the CBR, CDS spreads, and FX rates; (ii) a bidirectional link exists between the CBR and FX rates; between the FX rates and CDS spreads; and between the CDS spreads and CBR; (iii) the link exists in most quantiles except for some lower and middle quantiles for some indicators; (iv) explanatory effect of the indicators on each other varies based on quantiles; (v) the robustness of the results are validated by the TY causality test for the WC model and by the QR approach for the QQR model. The results suggest the significance of the CBR for the FX rates, the FX rates for the CDS spreads, and the CDS spreads for the CBR., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

This paper investigates the price transmission in the world market for soybeans using time series econometrics models. The theoretical model developed by Mundlack and Larson (1992) is based on the Law of the One Price, which assumes price equalization across all local markets in the long run and allows for deviations in the short run. The international market was characterized by three relevant soybean prices: Rotterdam Port, Argentina and the United States. The paper estimates the elasticity of transmission of these prices into soybean prices in Brazil. There were carried causality and cointegration tests in order to identify whether there is significant long-term relationship among these variables. There was also calculated the impulse-response function and forecast error variance decomposition to analyze the transmission of variations in the international prices over Brazilian prices. An exogeneity test was also carried out so as to check whether the variables respond to short term deviations from equilibrium values. Results validated the Law of the One Price in the long run. In line with many studies, this paper showed that Brazil and Argentina can be seen as price takers as long as the speed of their adjustment to shocks is faster than in the United States, the latter being a price maker.

California legislation outlawed the use and sale of battery cages for egg-laying hens in 2015. While a number of ex ante studies projected the effects of the housing prohibitions, the ultimate ex post effects are unknown. Using a price series reported by the USDA, we study the movement of daily egg prices in California and the United States before and after the law's implementation. Depending on the methods used, we find that Californians now pay between $0.48 and $1.08 more for a dozen eggs. The estimates suggest an annual reduction in California consumer surplus of between $400 million and $850 million. [ABSTRACT FROM AUTHOR]

Nowadays, when companies are dealing with severe global competition, they are making serious investments in Customer Relationship Management (CRM) strategies. One of the cornerstones in CRM is customer churn prediction, the practice of determining a mathematical relation between customer characteristics and the likelihood to end the business contract with the company. This paper focuses on how to better support marketing decision makers in identifying risky customers in telecom industry by using Predictive Models. Based on historical data regarding the customer base for a telecom company, we proposed a Predictive Model using Logistic Regression technique and evaluate its efficiency as compared to the random selection. In the future, we will focus on extending our study by integrating more business considerations and mining models in order to adjust the churn models or redesign marketing activities for the telecom industry. [ABSTRACT FROM AUTHOR]

This paper analyses the cyclical behaviour of the German annual aggregate wage earnings over 179 years. Our results show that there are transitory exogenous shocks which contain predictive information for aggregate wages in a non-linear framework. [ABSTRACT FROM AUTHOR]

Experience from the United Kingdom and the United States suggests that expert evidence is often reshaped and repackaged by governments so that it supports existing policy rather than informing policy decisions. The Australian Government based its decision to introduce FuelWatch on evidence in the form of econometric work by the ACCC. This paper asks two questions about that decision. First, was the policy shaped by the econometric evidence or was the Government's presentation of the evidence shaped by the pre-determined policy? Second, is the econometric evidence sufficiently robust as to support the FuelWatch policy? I find that some of the facts suggest that evidence was reshaped and repackaged to support the FuelWatch policy. I also find that the ACCC analysis was not robust. Specifically, they studied the nominal retail margin when economic theory suggests that analysis should focus on the real retail margin to producers. Using data digitized from a graph in the ACCC Report I redo the econometric analysis and find that the evidence no longer unambiguously supports the FuelWatch policy. The ACCC claim that their analysis is robust because it has been subject to scrutiny within the ACCC and by Treasury but such claims of robustness cannot be verified because they refuse to release the data for public scrutiny. Publication of data and analysis underpinning government decisions and independent review of econometric work provides a more credible evidence base for future policy decisions. [ABSTRACT FROM AUTHOR]

In order to handle the increasing problem of abandoned end-of-life vehicles (ELVs) in Sweden, an incentive system was introduced in 1975. One of the cornerstones of the system is a premium received by the owner when an ELV is returned for dismantling. The premium is intended to create an economic incentive to return the ELV. The European Union (EU) directive regarding extended producer responsibility for ELVs does not demand any incentive system. This study applied time series econometrics to model the relationship between the number of returned ELVs and the size of the premium using Swedish data. The main contribution of this study is that a positive relationship between the number of returned ELVs and the size of the premium was established. The results indicate that an increased premium would result in an increased number of returned ELVs. This implies that the efficiency of the EU directive could be increased if combined with an incentive system. [ABSTRACT FROM AUTHOR]