This study analyses the impact of agricultural technology adoption on income inequality. Primary data has been collected from 400 sample households in Awi zone of Ethiopia through household survey during agricultural season of 2017/18. The collected data were analysed by using propensity score matching method. The estimated results revealed that adoption of agricultural technologies such as chemical fertiliser and improved seeds significantly increase total household income but worsen income distribution. After adoption of agricultural technologies, income inequality measured by Gini coefficient increased ranged from 0.047 to 0.087. Hence, the government and other concerned authorities should exert more efforts in order to enhance technology adoption status of the poor households by increasing their accessibility for extension and credit services.

Objectives: The main objective of the study is to investigating the dynamic relationship between FDI and different Macro Economic Variables (MEVs) using the ARDL procedure, providing a more comprehensive understanding of the association between FDI and MEVs and to evaluate their relative importance for FDI. Methods: This study utilizes annual data from 1991 to 2021 from the World Bank (2021) and the Reserve Bank of India (2021). Data on GDP, Export, Inflation and interest rate, and FDI are collected from the World Bank. Autoregressive Distributive lag Model procedure has been used for the study in order to establish relationship between Macro economic variables and FDI. Results: Our methodological approach using ARDL model and finds: (a) a positive correlation between exports and FDI, (b) a negative impact of inflation and exchange rates on FDI in the long run. Non-linear ARDL analysis reveals the asymmetric impact of inflation and interest rate on FDI, which includes the effect of positive and negative shock of interest rate and inflation on FDI. d) 1% increase in inflation reduces FDI by 0.4% and if Inflation is reduced by 1%, FDI is increased by 0.2%. At the same time, the non-linear estimation of interest rates concludes that there is an asymmetric and significant association between interest rates and FDI. e) If Interest rate has increased by 1% FDI is decreased by 0.9% and if interest rate has reduced by 1% FDI has deceased by 1.63%. f) The causality analysis reveals that exports, GDP, and exchange rates are the significant economic variables that affect FDI. Conclusion: The study's findings have practical implications for policymakers and investors looking to attract more FDI in India. The results indicate that exports play a critical role in attracting FDI and that the government should focus on improving export performance to increase FDI inflows. Additionally, the study highlights the importance of controlling inflation and exchange rates to attract more foreign investment. The finding that interest rates have an asymmetric relationship with FDI suggests that policymakers should be cautious when implementing monetary policies that may impact interest rates. Overall, the study provides valuable insights for policymakers and investors looking to attract more FDI in India and highlights the importance of considering the country's macroeconomic conditions when making investment decisions.

Stocks are traded continuously in the financial market, creating huge time series data. Stock market time series is very volatile and highly complex to model. There are many methods to forecast a time series. This study predicts and compares the performance of two statical methods, linear regression, and ARIMA also referred to as the Box Jenkins method, which stands for Autoregressive Integrated Moving Average, is a robust time series forecasting technique used frequently by researchers. Simple linear regression is generally assumed unsuitable for non-linear stock time series data. However, a literature gap exists in comparing linear regression and the ARIMA method for forecasting stock prices. Hence, this study compares the ARIMA and linear regression methods to forecast stock prices using daily and weekly NIFTY data. Further, this study also experiments using a different length of time series, namely 1-year and 2-year data. The result shows that ARIMA outperformed regression on daily and weekly data when the test for 1 year of data. When 2-year data is taken, linear regression outperformed ARIMA on both daily and weekly data. Hence, the linear regression model and ARIMA are sensitive to input parameters such as the number of days for training and forecasting. An automated method or algorithm could improve the robustness of the model. Further, as stock price data is non-linear, machine learning algorithms such as neural networks and support vector regression can be more suitable for prediction.