Case Study: ARL Bounds Test For Co-Integration

Under National Agro-Food Policy, agriculture sector has been identified as a National Key Result Area. Under this initiative, the agriculture sector is targeted to increase the Gross National Income by RM28.9 Billion (USD9.1 billion) to reach RM49.1 billion (USD15.4 billion) by 2020. The agricultural sector is also targeted to create more than 109,000 job opportunities by 2020, primarily in the rural areas.

In the 1990’s cereals and grain were popular in Africa, with over 60 percent of the land used for these crops (ita 2004). From the 1930’s to the 1990’s Africa saw a reduction of 13 percent in agriculture’s share (ita 2004). During the 1970’s and 1980’s, there was a period of drought that affected both the crops and native plant in the area (Boffa Dixon Garrity 2012), which may have contributed to the decline of agriculture’s share. Also, in 1992 the production of corn fell from 10 million to 3 million tons, due to the drought (ita 2004).

In Ethiopia, about 4.9 million acres of land is devoted to its production every year. From 2003-2005 production statistics indicated that tef accounted for about 29% of the land and 20% of the gross grain production of all major cereal cultivation in the country (National Research Council).

Slide 17: This curve demonstrates a one-tail hypothesis with the critical region representing 5% showing a negative relationship.

The critical value represents the point on the scale of test statistic value in which the null

One-sample t-test are used in the parametric test which analyzes the means of populations. The t-test for independent groups are statistics that relates difference between treatment means to the amount of variability expected between any two samples of data within the same population (Hansen & Myers, 2012). Critical values are used in significant testing provide a range of t distribution that is used in whether a null hypothesis is rejected. Based on the data below as the level of significance is at .05, thus the critical values would fall under ±1.860 and the t value for this is 1.871 would suggest for the null to be rejected as it is greater than the critical value (Privitera, 2015, p. 267). Based on the population mean of 70 there was a mean difference of

One approach to comprehend the economy in Somalia is to consider appraisals of per capita Gross Domestic Product (GDP). Gross domestic product is a measure of the aggregate yield of a

Figure 1 calculates a value of .1739 to be compared to the critical value. The critical value for 2 phenotypes is 3.84 (Lab Manuel pg 135). The two values are then substituted into the equation.

located down the row of the t-table. The critical value result is the point where the a-level and degrees of

performance of both irrigated and rain-fed agricultural production systems. Production of more food to feed the

Accept Ho at the .05 significance level as, -0.65 falls within the critical value criteria.

In this case α=0.482421 which reflects that the performance of this portfolio has outperformance against the market portfolio. But the market portfolio should perform better than any others portfolio if the market is efficiency. Further, α has a 2.70807 t-stat which is larger than 1.96 that suggests the intercept is significant at the 5% significant level. Moreover, the p-value of the intercept is 0.00692 which is less than the 5% significant level therefore α is significant.

Production and Productivity Trends Labor productivity. Up until the 1970s, the Philippines’ agricultural performance, in terms of both agricultural Gross Value Added (GVA) and agricultural exports, compared well with its neighbors and other Asian countries (Figure 3a). But by the 1980s and 1990s, the country had lagged behind most of the countries in the region (Figures 3b and 3c). This came as agricultural output growth had slowed down dramatically through the decades (Figure 4). Moreover, the sector’s growth had been rather erratic in the 1990s, especially with the periodic occurrence of the El Niño phenomenon that had appreciable impact on weather patterns and, consequently, agricultural performance. Table 1 shows the average annual growth in GVA of major agricultural commodities since 1960. What is clear from the table is that growth rates of all commodities, except for livestock and poultry, have been slowing down over time. Furthermore, growth rates have been below the population growth rate, implying that production has not been able to keep up with increasing population. Erratic and decelerating growth over the past two decades is a major concern, as agriculture continues to employ a large

Growth in the agricultural sector has been driven by increased production of major food crops such as maize, sorghum and cassava, but the sector’s performance remains below potential. In turn, the services and industrial sectors have shown strong growth. The nascent banking sector and expanding telecommunications sector are key drivers behind services growth, while construction, electricity generation, manufacturing and mining are salient sub-sectors in industrial activity. Looking ahead, the banking and telecommunication sectors will continue to support services growth, while increased electricity generation capacity will benefit the expansion of the manufacturing

Political factors impact the agricultural sector in factors relating to regulation, distribution, and consumption of foods in a given country. Government policies and imposed regulations have a direct effect on nutritional choices that a consumer makes, and this, in turn, affects the agriculture market (KPMG, 2012). For example, policies governing food prices or the amount of information that a consumer will receive affects the choice of the consumer. Food regulation and safety measures implemented influence the supply of food products, and ultimately determines the market choice for consumers (KPMG, 2012). Economic factors have a direct effect on the agricultural industry. On one hand, the input cost such as the price of seeds, fertilizers, and cost of labor affect the productivity of the industry. The economic status of a country also affects the industry’s productivity. For example, in developing countries, the agricultural sector is less developed owing to limited resource input and poor infrastructure (KPMG, 2012).