This study uses a gravity framework to explore the impact of diverse sources of linguistic influence on international tourism flows. The diverse sources of linguistic influence are captured using a common language index derived from a common native language, a common official language, and linguistic proximity. Our results reveal that linguistic factors as captured by common language index promote international tourism flows. The positive impact of linguistic ties on tourism flows is not only observed in the country-pairs that share an official language, it is also found in pairs that share unofficial native languages, that contain minority groups united by a common language, or that use closely related languages—all features captured by the language index. The analysis demonstrates that an index of common language is methodologically superior to other measures of linguistic similarity, including the dummy variable for a common official language that currently dominates the international tourism literature.

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues.  As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020. Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020. Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits better in forecasting prices. The production of local rice is inconsistent in Nigeria because of erratic rainfall and stiff competition from the importation of rice from other countries. Therefore, and consistent with the analysis, the hybrid model is the best model applicable to local rice because it captures varying trends exhibited in the data. Originality / scientific novelty. This study suggests most accurate forecasting techniques for specific agricultural commodities in sub-Saharan African countries. It considers forecasting prices of commonly consumed grains and legumes in Nigeria and traded worldwide, such as imported rice, local rice, beans, and maize. Practical value / implications. The study highlights the importance of appropriate forecasts for policymakers, producers, and consumers to enhance better decision making and serve as an underlying incentive to guide the allocation of financial resources to the agricultural sector, which determines the structure and degree of sectoral growth.

In this study we use the stochastic frontier model in estimating total factor productivity (TFP) growth and technical efficiencies for manufacturing industries in Bangladesh by using surveys collected during five rounds in 1982/83, 1984/85, 1988/89, 2005/06 and 2012. To detect the source of growth, we further decompose Total Factor Productivity growth into efficiency growth, scale component, and technological progress. Our result establishes that, on average, technical efficiency was 80%, noting that the export focused industries are comparatively more efficient than non-export focused industries. Furthermore, medium, and large-scale industries have a lower TFP growth than small scale industries. Furthermore, the Total Factor Productivity growth estimations shows that productivity in the manufacturing industries in Bangladesh was approximately 5.5% in the review period illustrating that technological progress is a major driver of growth. Additionally, this study provides evidence on the likelihood of TFP growth convergence over time among the manufacturing industries in Bangladesh. JEL classification: D24; O14