Footprints of Climate Change, Global Warming, Government Economic Policies on Crops Production: Adaptation Strategies

Climate change has become one of the most imperative snags of this century. Climate change is a grave global threat, and Pakistan has been ranked eighth on the list of countries most vulnerable to climate change by the German watch Global Climate Risk Index. The increasing amount of carbon dioxide (CO2), the foremost contributor to global warming, seems to provoke this problem. Agriculture-based economies have always been dependent on the vagaries of nature and climate. This study in your hand underscores climate change, global warming and government economic policies footprint on four major crop production (Wheat, Rice, Sugarcane, and Cotton) in Pakistan with the help of time-series data acquired from 1977 to 2016. The ARDL bounds espouse test technique relied on ADF test results. The study concluded that climate change and government economic policies have a positive influence on crops produced in the long as well as short-run except global warming and input index, which have a negative influence in the short run. Policymakers and researchers must drive attention to adopt advanced cultivation processes through farming practice through international research institutions' assistance.

A Random walk down the Dhaka Stock Exchange lane

Market efficiency has always been a matter of keen interest to the researchers of finance. Since the advancement of this concept, researchers are consistently investigating the market efficiency of different financial markets. Bangladesh, being one of the emerging economies, has also attracted the attention of many researchers. The researchers have investigated the realities regarding the market efficiency of both the stock exchanges of the country. Most of their investigations reveal that the Dhaka Stock Exchange (DSE) and the Chittagong Stock Exchange (CSE) are inefficient. This research, however, did not stop at revisiting market efficiency alone. Whether the return series follows a long-memory process, has also been tested. Besides, non-parametric tests have also been conducted to confirm the results of the parametric tests and vice versa. It generated a more reliable estimate of market efficiency for the period under study. Results of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model confirm that the return series does not follow a long memory process, and any shock in the system will eventually vanish. The findings of other tests (the run test, the Augmented Dickey-Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Kolmogorov-Smirnov (K-S) test) suggest that the return series of the DSE are time-series stationary, non-normal, and do not follow a random walk. Given these results, we must echo the prior researchers to conclude that the stock market of Bangladesh is not efficient for the period of 2015 to 2020. These findings add new knowledge to the existing knowledge pool about market efficiency and long memory of the stock market of Bangladesh.

Success story of controlling COVID-19 in East Asia: lessons for South Asia

The novel coronavirus is an issue of life and death. The main purpose of the study is to know the East Asian success story of controlling Covid-19 and identify which strategies could be a lesson for South Asia and to examine the influence of good governance on controlling COVID-19. Total daily cases of COVID-19 are collected from March 10 to June 15 for East Asian and March 4 to June 15 for South Asian countries. ARIMA forecasting, ADF test, stability test, and diagnostic tests are applied. The minimum value of AIC and BIC shows the appropriate model is ARIMA (0, 1, 1) for both regions. In the East and South Asian model, the coefficients of the constant term are -0.759451 and 198.0155, and coefficients of MA (1) are -0.715686 and -0.339701 respectively for both regions. It's significant at a 1% significance level and support our hypotheses that the total daily cases of COVID-19 decreasing into East Asia but increasing into South Asia and prove that the South Asia region has faced a lot of difficulties to tackle COVID-19 as most of the countries have not enough government capacity, weak institutions, limited resources, narrow government reaches to the vulnerable people and corruption compare to East Asian region and no actual strategies are yet noticeable from the governments of South Asia as a result transmission increases day by day. That is why; we think that South Asian countries could take lessons from East Asian countries as these countries are more successful to control COVID-19.

IMPACT OF OFFICIAL DEVELOPMENT ASSISTANCE ON SUSTAINABLE DEVELOPMENT OF PAKISTAN

This research paper aims to find out the relationship between Official Development Assistance and sustainable development in Pakistan. Time series data was taken for the period of 42 years (1976 -2017). Sustainable Development is a dependent variable for which proxy variable of Adjusted Net Savings has been deployed. ODA (% of GNI), Inflation, Per Capita GDP and Trade (GDP %) have been used as explanatory variables. Augmented Dickey-Fuller Test has been applied to examine the nature of the data as time series data may contain unit root problems. ADF test confirms mixed order of integration for the selected variables, hence Autoregressive Distributed Lag (ARDL) Approach was applied to find out the long-run relationship among the considered variables. Estimation of Error Correction Regression resulted in a significant long-run relationship between ODA and Sustainable Development. ECM Regression also signifies the negative and significant value of the speed of adjustment term confirming that the model is stable and convergent towards the equilibrium. Overall results of this study confirm a positive and highly significant relationship between ODA and the measure of sustainable development in Pakistan. Therefore it is recommended that attention should be given to drawing on foreign assistance and it should be subject to the transparent and efficient practices applied in the Aid Allocation. It significantly improves the overall welfare of Pakistan.

Impact of Human Capital Underutilization on Economic Growth: the Case of India

This study investigated the effect of human capital underutilization on the economic growth of India. It has used time-series data accessed from the International Labor Organization (ILO) and World Bank database. This paper estimated the relationship between the underutilization of human capital on economic growth by applying the econometric tests like Augmented Dickey-Fuller (ADF) Test, Johansen Integration Test, and the Autoregressive Distributed Lag (ARDL) model. The results revealed that in the long run human capital underutilization has a negative relationship on GDP and labor productivity and it does not in the short run. The study recommends that specific policy legislations in the Indian labor markets are required for addressing the problem of human capital underutilization and thereby accelerating the economic growth and productivity for the current and future generations.

Impact of Macro-Economic Variable on Pakistan Stock Exchange KSE-100 Index

The researcher investigates the Impact of inflation, exchange rate and interest rate on Pakistan stock Exchange performance KSE-100 index by using monthly time series data which covers the period of 2013 to 2020. The econometrics techniques which are employed includes ADF test, Ordinary Least squares regression Model, testing for Multi-collinearity, Residual analysis serial correlation, testing for co-integration, Error correction model (ECM), variance decomposition (VAR) and Pair wise granger causality test. The results indicate that there is positive impact of exchange rate on PSX 100 index and the impact of inflation and interest rate is fond negative but inflation have insignificant relationship with PSX 100 index and the other two relationships are found significant. From the ECM result it is found that in short run 20% of the variation in dependent variable is due to inflation, exchange rate and interest rate and 80% variation is unexplained in short run. Form the results of VAR test it is concluded that exchange rate 1.67, inflation 14.25%, and interest rate 3.90% variation cause in PSX 100 index performance due to these three independent variables.

Lamb Wave Based Structural Damage Detection Using Stationarity Tests

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

Investigating the Impact of External Debt on Economic Growth: A Case Study of Pakistan

This article is an attempt to evaluate the effect of external debt on economic growth for during the period of 1980–2016. The Augmented Dickey Fuller (ADF) test is used for determining stationarity, whereas the ADF test results exhibit that the variables used found are . The empirical results indicate that external debt and total debt service have deleterious and statistically significant impacts on GDP growth rate. The other explanatory variables namely human capital by life expectancy, exports, and Foreign Direct Investment (FDI)reveals significantly positive significant influence on GDP growth rate. Appropriate policy should be adopted by the policy makers to reduce external debt, increase volume of exports and enhance more foreign investment, it will boost economic growth in the country.

Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

Real Estate Bubbles and Contagion: Evidence from Selected European Countries

Using quarterly housing price-to-rent ratios from 1970 to 2018, this paper investigated the presence of real estate bubbles at a national level in eight selected European countries, namely Belgium, France, Germany, Italy, the Netherlands, Portugal, Spain, and the United Kingdom. Then, we analyzed bubbles contagion among these countries. We applied the generalized sup ADF test developed by Phillips et al. (2015) to detect explosive behavior in house prices. Subsequently, we implemented the non-parametric model with time varying coefficients developed by Greenaway-McGrevy and Phillips (2016) to estimate bubbles contagion among European real estate markets. We found evidence of at least one historical bubble in all these countries, with Germany, the Netherlands, Portugal, and Spain currently experiencing a rising bubble. The results also suggest that bubbles are contagious between these real estate markets.