Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

Quantification and Forecasting of Cumulonimbus (Cb) Clouds Direction, Nebulosity and Occurrence With Autoregression Using 2018-2020 Dataset From Yaounde-nsimalen

This works reports the quantification and forecasting of Cumulonimbus (Cb) clouds direction, nebulosity and occurrence with auto regression using 2018-2020 dataset from Yaoundé –Nsimalen of Cameroon. Data collected for October 2018-2020 consisted of 2232 hourly observations. Codes were written to automatically align, multi-find and replace data points in excel to facilitate treating big datasets. The approach included quantification of direction generating time series from data and determination of model orders using the correlogram. The coefficients of the SARIMA model were determined using Yule-Walker equations in matrix form, the Augmented Dickey Fuller test (ADF) was used to check for stationarity assumption, Portmanteau test to check for white noise in residuals and Shapiro-Wilk test to check normality assumptions. After writing several algorithms to test different models, an Autoregressive Neural Network (ANN) was fitted and used to predict the parameters over window of 2 weeks. Autocorrelation Function (ACF) shows no correlation between residuals, with p ≤ 0.05, fitting the stationarity assumption. Average performance is 80%. A stationary wavelike occurrence of the direction has been observed, with East as the most frequented sector. Forecast of Cb parameters is important in effective air traffic management, creating situational awareness and could serve as reference for future research. The method of decomposition could be made applicable in future research to quantify/forecast cloud directions.

The Effect of Net FDI Inflow on Economic Growth in Turkey

Capital is one of the first and foremost requisites of economic development for every country in this world. However, not every country is given abundant capital. Foreign direct investment (FDI) occurs as a good cure to solve capital-related issues. In this study, the net FDI inflow and economic growth correlation was researched in Turkey for the period of 2010:1-2018:3 by employing quarterly data as well as applying the Augmented Dickey Fuller Test (ADF); Phillips-Perron (PP); Kwiatkowski, Phillips, Schmidt, Shin (KPSS); Elliott, Rothenberg, and Stock (ERS) Point Optimal; Ng-Perron Unit Root Tests; and Toda-Yamamoto Causality Tests. According to the findings of the study, there is a unidirectional causality running from net FDI to economic growth in Turkey.

Exploration of potential regional resources for beef cattle farming development in West Java, Indonesia

More than 90% of cattle in Indonesia are raised by smallholder farmer. They are facing limited land resources so that the input of feed mostly depend on the agriculture waste and its environment. The aimed of this study was to determine the potential agricultural resources which influence to the development of beef cattle. The sample locations were 3 districts from the eastern part of West Java province. Panel data of variables that were thought have an effect on beef cattle development were taken from 2012 to 2019 in three regencies. These data were analyses by Multiple Regression Model using the OLS method. The stationary test was done before using time series data by unit root tests of Augmented Dickey-Fuller. The results of this research showed that the corn and Cassava production, as well as the population as a source of agriculture labor in a region was potential resources to increase of beef cattle population. The integration of cattle and corn farming on suitable land in a region needs to be considered as the strategy in beef cattle farming development, because the corn production per unit of land can produce the highest feed compared to the other food crop residues.

Volatility in Indian Stock Markets During COVID-19

The aim of the paper is to evaluate the impact of novel COVID-19 on the returns and volatility of Indian stock markets with special reference to equity investment strategies of Bombay Stock Exchange. For the purpose of evaluating the impact, the study has applied GARCH) The research has considered a time frame from March, 2015 to January, 2021. Prior to implementing GARCH model, pre-estimation tests i.e., Augmented Dickey-Fuller and ARCH-Lagrange Multiplier, were conducted. Outcomes clearly indicate that the returns during the crisis for all the strategy indices have been negative which means that the COVID-19 outbreak resulted in massive losses. Additionally, 'during crisis' period showed increase in volatility for all the strategy indices depicting that the pandemic has a long-lasting effect and will take time to fade off. This research will help the investors in the investment decision process by giving them insights about the different strategies.

Energy Consumption and Economic Growth Nexus in Pakistan

The study explored the link between energy consumption and economic growth in Pakistan covering the period from 1980 to 2018. This study used an augmented production function and combined the two neo-classical and ecological points. Most important is that this study used three different proxies of energy to check whether the relationship is proxy specific or not in Pakistan. Furthermore, there are some controls in terms of trade and foreign direct investment to check the robustness of the relationship. The time series approaches as augmented dickey fuller (ADF) unit root test and ARDL bound test approach has applied. The results indicated the long-run positive relationship between energy and growth in Pakistan and the relationship is not proxy specific. Therefore, it has suggested enhancing energy efficient policies, better resource allocation for energy supply.

Maize Output Supply Response to Climatic and other Input Variables in Ethiopia

Climate change is among the major challenges to sustainable agricultural production in Ethiopia. Production of cereal crops, especially maize, is very responsive to changes in rainfall and temperature, as climatic parameters influencing productivity. This paper analyzes how climatic and other variables affect the supply of maize in Ethiopia. The data were obtained from secondary sources and cover the period 1981–2018. Data were analyzed using the Autoregressive Distributed Lag (ARDL) approach. The Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan-Quinn Information Criterion (HQ) were used to select the optimum number of lags. In order to detect whether unit root is present in the series, Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests were carried out. The presence of long-run equilibrium was found between maize output and temperature, rainfall, and other included variables. The results show that, in both the long and shortrun, all included climatic variables had a negative relationship with maize output supply, although temperature showed statistical insignificance (P>0.10). The result showed that maize crops are highly sensitive to extremes of rainfall – both shortage in the initial growing period and excessin the vegetative and fruiting stages. It was concluded that farmers face climate-related risk due to variations, particularly in rainfall. Therefore, farmers should adapt by using short-duration and climate-tolerant varieties of maize, along with engagement with eco-friendly production systems.

Unit-root tests for explosive behavior

We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011, International Economic Review 52: 201–226) and recursive flexible windows (Phillips, Shi, and Yu, 2015, International Economic Review 56: 1043–1078). It allows for the lag length in the test regression and the width of rolling windows to be either specified by the user or determined using data-dependent procedures, and it performs the date-stamping procedures advocated by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015) to identify episodes of explosive behavior. It also implements the wild bootstrap proposed by Phillips and Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61–80) to lessen the potential effects of unconditional heteroskedasticity and account for the multiplicity issue in recursive testing. The use of radf is illustrated with an empirical example.

Capital Flight and Unemployment Rate in Nigeria

This study examined Capital Flight and unemployment rate in Nigeria. Capital flight was proxied by foreign direct investment abroad, external debt servicing, external reserves and capital and financial account deficits. Based on study objectives, relevant literatures were reviewed and evaluated. Relevant data were extracted from the annual Statistical Bulletin of the Central Bank of Nigeria and the National Bureau of Statistics. Unit root test was conducted using Augmented Dickey Fuller method which revealed that the variables were integrated at level and first difference necessitating the use of autoregressive distributive lag/bond test to explore the long run relationship existing among the variables in the model and the result showed that the variables in the model were co-integrated thus we proceeded in evaluating the long run as well as the co-integrating form in the model. From the result of the various tests, it was revealed capital flight did not actually increase unemployment rate in Nigeria within the periods studied by the researchers. Based on the findings from the analysis, the study recommended amongst others, that external debt acquired should be judiciously used for infrastructural development that would encourage investments which would ultimately bring about economic growth as well as enhance human development in Nigeria.

THE NEXUS BETWEEN THEEXTERNAL SECTOR AND INFLATION IN NIGERIA, 1985 -2018

The interaction between different economies in the global market could have impact on their key economic variables. Hence, this study set out to examine the impact of the external sector on a key economic variable in Nigeria being Inflation. The study utilized time series data from 1985 to 2018 which was subjected to Augmented Dickey Fuller Test. The next step was to subject the data to an ARDL cointegration test. The result showed that in the short run and long run external sector variables do not have any significant impact on inflation in Nigeria. The paper suggests that money supply and fiscal policy should be used to control inflation in Nigeria.