On Some Weaknesses of Econometrics (Part 1)

IRJET, 2023

This research paper discusses the econometric modeling approach of linear regression using statistics. Linear regression is a widely used statistical technique for modeling the relationship between a dependent variable and one or more independent variables. The paper begins by introducing the concept of linear regression and its basic assumptions. The univariate and multivariate linear regression models are discussed, and the coefficients of the regression models are derived using statistics. The matrix form of the Simple Linear Regression Model is presented, and the properties of the Ordinary Least Squares (OLS) estimators are proven. Hypothesis testing for multiple linear regression is also discussed in the matrix form. The paper concludes by emphasizing the importance of understanding the econometric modeling approach of linear regression using statistics. Linear regression is a powerful tool for predicting the values of the dependent variable based on the values of the independent variables, and it can be applied in various fields, including economics, finance, and social sciences. The paper's findings contribute to the understanding of the linear regression model's practical application and highlight the need for rigorous statistical analysis to ensure the model's validity and reliability.

MOHAMMAD ABDUS SALAM JOURNAL OF PHYSICS SCIENTIFIC RESEARCH, 2014

Abstract: The term econometrics is believed to have been crafted by Ragnar Frisch (1895-1973) of Norway, one of the three principle founders of the Econometric Society, first editor of the journal Econometrica, and co-winner of the first Nobel Memorial Prize in Economic Sciences in 1969. Its main object is to promote studies that aim at a unification of the theoretical-quantitative and the empirical-quantitative approach to economic problems.

This course builds and expands on the knowledge acquired in Econometrics I. As such, it emphasizes both the theoretical and the practical aspects of statistical analysis, focusing on techniques for estimating econometric models of various kinds and for conducting tests of hypotheses of interest to economists. It is designed in such a way so as to help the individual to develop a solid theoretical background in introductory level econometrics, the ability to implement the techniques and to critique empirical studies in economics. 11. Aim of the Course The aim of this course is to provide the basic knowledge of econometrics that is essential equipment for any serious economist or social scientist, to a level where the participant would be competent to continue with the study of the subject in a graduate programme. While the course is ambitious in terms of its coverage of technical topics, equal importance is attached to the development of an intuitive understanding of the material that will allow these skills to be utilised effectively and creatively, and to give participants the foundation for understanding specialized applications through self-study with confidence when needed.

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.