Trend Analysis of Students Performance in Econometrics 2018-2022 in Nasarawa State University Keffi, Nigeria (original) (raw)
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International Journal of African Sustainable Development Research Vol 12, No2, pp1-12. ISSN:2067-4112, 2023
Trend analysis is the widespread practice of collecting information and attempting to spot a pattern over time series, hence, the study investigated trend analysis of Economics students' performance in NECO 2013-2022 in NorthEast Geopolitical Zone of Nigeria. Two research questions and its corresponding hypotheses guided the study. The study adopted Ex-post factor research design. The population of study consist of all 18,882 students in the public senior secondary schools Examination (SSCE/NECO) 2013-2022, and 2,443 students where used as a sample size for the study using multistage sampling techniques. Examination Records of Economics Students Performance (ERESP) was used as an instrument for data generation. The ERESP was valid and reliable which yielded 0.86 reliability indexes. Time series, percentage was used to answer the research questions while Regression analysis was used to test the hypotheses at 0.05 level of significance. The findings revealed that the trend of students' performance in SSCE/NECO Economics 2013-2022 was stochastic with random walk and there is a significant difference in the percentages of males and females' students' performance that obtained credit (A1-C6), pass (D7 & E8) and fail (F9) in NECO Economics 2013-2022. Based on the findings, it was recommended that educational policy makers should employ more qualified Economics teachers, teachers should be encouraged by the school principals toward teaching learning Economics and teachers should be encouraged to allocate more time to the teaching learning of Economics in school timetable to enable full coverage of teaching syllabus.
Factors Influencing Academic Achievement of Econometrics Students in Quantitative Courses
2017
Bu makalede Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Ekonometri bölümü üst sınıf öğrencilerinin sayısal derslerdeki başarısını etkileyen faktörlerin neler olduğu araştırılmıştır. Önkoşul olması düşünülen sayısal ders notlarının üçüncü ve dördüncü yıl sayısal ders başarısını ne ölçüde etkilediği araştırılmıştır. Ayrıca sosyoekonomik (SE) değişkenlerin genel not ortalaması ile ilişkisi ve alt dönem sayısal ders notları ile branş ders notları arasındaki ilişki incelenmiştir.
Improving the teaching of econometrics
Cogent Economics & Finance, 2016
We recommend a major shift in the Econometrics curriculum for both graduate and undergraduate teaching. It is essential to include a range of topics that are still rarely addressed in such teaching, but are now vital for understanding and conducting empirical macroeconomic research. We focus on a new approach to macro-econometrics teaching, since even undergraduate econometrics courses must include analytical methods for time-series that exhibit both evolution from stochastic trends and abrupt changes from location shifts, and so confront the 'non-stationarity revolution'. The complexity and size of the resulting equation specifications, formulated to include all theory-based variables, their lags and possibly non-linear functional forms, as well as potential breaks and rival candidate variables, places model selection for models of changing economic data at the centre of teaching. To illustrate our proposed new curriculum, we draw on a large UK macroeconomics database over 1860-2011. We discuss how we reached our present approach, and how the teaching of macroeconometrics, and econometrics in general, can be improved by nesting so-called 'theory-driven' and 'data-driven' approaches. In our methodology, the theory-model's parameter estimates are unaffected by selection when the theory is complete and correct, so nothing is lost, whereas when the theory is incomplete or incorrect, improved empirical models can be discovered from the data. Recent software like Autometrics facilitates both the teaching and the implementation of econometrics, supported by simulation tools to examine operational performance, designed to be feasibly presented live in the classroom.
ECONOMETRICS I MODULE TEACHING MATERIAL FOR UNDER GRADUATE ECONOMICS STUDENTS
Abdella Mohammed Ahmed (M.Sc.), 2024
Ragnar Frisch is credited with literally coining interpreted, the econometrics means “economic measurement”, but described by leading econometricians. An econometrician has to be a competent mathematician and statistician who is an economist by training. Fundamental knowledge of mathematics, statistics and economic theory are a necessary prerequisite for this field. As Ragnar Frisch (1933) explains in the first issue of Econometrica, it is the unification of statistics, economic theory and mathematics that constitutes econometrics. Each view point, by itself is necessary but not sufficient for a real understanding of quantitative relations in modern economic life. Econometrics aims at giving empirical content to economic relationships. The three key ingredients are economic theory, economic data measurement’, nor ‘measurement without theo phenomena. It is as Frisch emphasized their union that is the key for success in the future development of econometrics. In general, Econometrics is the science which integrates economic theory, economic statistics, and mathematical economics to investigate the empirical support of the general schematic law established by economic theory. It is a special type of economic analysis and research in which the general economic theories, formulated in mathematical terms, is combined with empirical measurements of economic phenomena. Starting from the relationships of economic theory, we express them in mathematical terms so that they can be measured. We then use specific methods, called econometric methods in order to obtain numerical estimates of the coefficients of the economic relationships. [ In short, econometrics may be considered as the integration of economics, mathematics, and statistics for the purpose of providing numerical values for the parameters of economic relationships and verifying economic theories. What is a model? A model is a simplified representation of a real-world process. For instance, ‘the demand for oranges depends on the p there are a host of other variables that one can think of that determine the demand for oranges. These include: Income of consumers An increase in diet consciousness (e.g. drinking coffee causes cancer; so better switch to orange juice) Increase or decrease in the price of substitutes (e.g. that of apple) However, there is no end to this stream of other variables! Many have argued in favour of simplicity since simple models are easier: to understand to communicate to test empirically with data The choice of a simple model to explain complex real-world phenomena leads to two criticisms: The model is oversimplified The assumptions are unrealistic For instance, to say that the demand for oranges depends on only the price of oranges is both an oversimplification and an unrealistic assumption. To the criticism of oversimplification, many have argued that it is better to start with a simplified model and progressively construct more complicated models. As to the criticism of unrealistic assumptions, the relevant question is whether they are sufficiently good approximations for the purpose at hand or not. In practice we include in our model: Variables that we think are relevant for our purpose. A ‘disturbance’ or ‘error’ transmitted which well as all unforeseen forces.
Introduction to symposium on teaching undergraduate econometrics
The Journal of Economic Education, 2019
Over the past decades, econometrics and formal empirical methods have become more and more important to economics and hence to the teaching of economics. This is a natural movement reflecting the enormous computational and analytic technological advances in data collection and analysis. Within the economics major today there are many more courses in econometrics and statistics than there were in the past; in addition, most upper-level field courses include an almost mandatory econometrics component. Because the introductory and intermediate macro and micro courses currently do not include an introduction to econometrics, and instead concentrate on teaching students theory, this means that in addition to their core theory courses, undergraduate economics students need a core metrics course, or, better yet, a set of core metric courses, in addition to their introductory and intermediate theory core courses to properly prepare them for their field courses. Today, the two core pillars of economic teaching are theory courses and metrics courses. The increasing importance of econometrics can be seen in the way economists think of themselves in relation to other social scientists. Even as late as the early 1980s, when I interviewed graduate students for my "Making of an Economist" article (Colander and Klamer 1987), students told me that it was economists' use of rational choice theory that defined an economist. 1 By the 2000s that had changed, and in interviews I did for my "Making of an Economist, Redux" article (Colander 2005), students told me with pride that, from their point of view, what differentiated economists from other social scientists was their high-powered econometric methods that allowed them to pull information from data, not their use of rational choice theory. Modern economists consider themselves best among social scientists at processing and analyzing data. 2 In reflecting on these developments, it is useful to remember that good economic policy analysis, and hence, good economic training, has always been empirical and evidenced-based, at least in the eyes of the economists doing it. It is just that, before the recent formal empirical turn in economics, the empirical methods used to apply the theory to the real world were informal and involved interpretation and judgments that went far beyond formal scientific analysis. Applied economics of the past relied on reflective reasoning, or as Deirdre McCloskey classifies them-rhetorical skills. Good old-style economists could process poorly specified empirical data and theory jointly, creatively capturing the essence of theories, and relating them to the real world in a way that left others with an "aha" sense-yes, that is the way it works. Those economists who were superb at it, such as J. M. Keynes, Jacob Viner, Joan Robinson, Fritz Machlup, or Armen Alchian, were seen as role models for how good applied economics was done. Their methods were discursive;
Abdella Mohammed Ahmed (M.Sc.), 2024
What is econometrics? • Literally speaking, the word ‘econometrics’ means measurement in economics. • In general, econometrics is the application of statistical and mathematical methods to the analysis of economic data with a purpose of giving empirical content to economic theories and verifying or refuting them. • More specifically, it is concerned with the use of statistical methods to attach numerical values to the parameters of economic models and also with the use of these models for prediction. • The techniques of econometrics consist of a blend of economic theory, mathematical modelling and statistical analysis. Before any statistical analysis with economic data is performed, one needs a clear mathematical formulation of the relevant economic theory. For example, saying that the demand curve is downward sloping is not enough. We have to write the statement in mathematical form as: q = a + b p, b < 0 or , b < 0 Where q is the quantity demanded and p is the price. One major problem: economic theory is rarely informative about functional forms. Thus, we have to use statistical methods to choose the functional form as well. Putting it differently, we are often presented with no more than the data themselves and the theory behind the data generation process is non-existent or far from being complete. Thus, what we do in practice is: • Investigate the important features of the observed data • Construct an empirical model (incorporating as much available background theory as possible) • Check that the constructed model is capable of capturing these important features The model construction phase is facilitated by specifying a fairly wide class of models within which some optimal search technique may then be applied. The main aim of this course is to provide a good understanding of the properties of linear econometric models and techniques. Although some features of macroeconomic time series cannot be adequately described and analysed using linear techniques, much econometric model building is dominated by linear models. The reasons include: • The theory of statistical inference is most developed for linear models. • Linear approximations to economic relationships have been quite successful in empirical work. Economic and econometric models The first task an econometrician faces is that of formulating an econometric model. What is a model? A model is a simplified representation of a real-world process. For instance, ‘the demand for oranges depends on the price of oranges’ is a simplified representation since there are a host of other variables that one can think of that determine the demand for oranges. These include: • Income of consumers • An increase in diet consciousness (e.g. drinking coffee causes cancer; so better switch to orange juice) • Increase or decrease in the price of substitutes (e.g. that of apple) However, there is no end to this stream of other variables! Many have argued in favour of simplicity since simple models are easier: • To understand • To communicate • To test empirically with data The choice of a simple model to explain complex real-world phenomena leads to two criticisms: • The model is oversimplified • The assumptions are unrealistic For instance, to say that the demand for oranges depends on only the price of oranges is both an oversimplification and an unrealistic assumption. • To the criticism of oversimplification, many have argued that it is better to start with a Simplified model and progressively construct more complicated models. • As to the criticism of unrealistic assumptions, the relevant question is whether they are sufficiently good approximations for the purpose at hand or not. In practice we include in our model: • Variables that we think are relevant for our purpose. • A ‘disturbance’ or ‘error’ term which accounts for variables that are omitted as well as all unforeseen forces. This brings us to the distinction between an economic model and econometric model. An economic model is a set of assumptions that approximately describes the behaviour of an economy (or a sector of an economy). An econometric model consists of the following: a) A set of behavioural equations derived from the economic model. These equations involve some observed variables and some ‘disturbances’. b) A statement of whether there are errors of observation in the observed variables. c) A specification of the probability distribution of the ‘disturbances ‘. With these specifications, we can proceed to test the empirical validity of the economic model and use it to make forecasts or use it in policy analysis. Methodology of econometric research The aims of econometrics are: a) Formulation of econometric models, that is, formulation of economic models in an empirically testable form (specification aspect). b) Estimation and testing of these models with observed data (inference aspect). c) Use of these models for prediction and policy purpose.
The Role of Econometrics Data Analysis Method in the Social Sciences (Education) Research
This paper examines the role of econometrics data analysis as one the method used in the social sciences (education) to provide factual evidence. How to understand the power of these procedures, the limits to them and the implications of this in terms of standards of evidence in the social sciences (education). Early attempts at quantitative research in economics, the birth of econometrics, and the econometric model. How econometrics and Experimental Methodologies Complement One Another. The specific subjects of these studies cover virtually all parts of economic theory, macroeconomics, accounting and economics of education. It also includes the effects of public policies in all of these areas.