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. Key words: Role, Econometrics, Data Analysis, Method, Social Sciences, Researc

Laws and Limits of Econometrics

We start by discussing some general weaknesses and limitations of the econometric approach. A template from sociology is used to formulate six laws that characterize mainstream activities of econometrics and the scientific limits of those activities, we discuss some proximity theorems that quantify by means of explicit bounds how close we can get to the generating mechanism of the data and the optimal forecasts of next period observations using a finite number of observations. The magnitude of the bound depends on the characteristics of the model and the trajectory of the observed data. The results show that trends are more elusive to model than stationary processes in the sense that the proximity bounds are larger. By contrast, the bounds are of smaller order for models that are unidentified or nearly unidentified, so that lack or near lack of identification may not be as fatal to the use of a model in practice as some recent results on inference suggest, we look at one possible future of econometrics that involves the use of advanced econometric methods interactively by way of a web browser. With these methods users may access a suite of econometric methods and data sets online. They may also upload data to remote servers and by simple web browser selections initiate the implementation of advanced econometric software algorithms, returning the results online and by file and graphics downloads.Activities and limitations of econometrics, automated modeling, nearly unidentified models, nonstationarity, online econometrics, policy analysis, prediction, quantitative bounds, trends, unit roots, weak instruments

Realist econometrics? Nell and Errouaki on methodological institutionalism, regularity and uncertainty

Nell and Errouaki state that their intent is to reformulate econometrics along more realistic lines. In so doing they also explicitly acknowledge a debt to realist debates in methodology, notably in relation to the construction of argument regarding the nature of science, objectivity and laws in Chapter 4 (Nell and Errouaki, 2013: xii). This requires some context, since seeking to be more realistic and accepting the tenets of realism are not necessarily the same. One is theory and application, and the other is argument regarding the grounds of theory and application. Moreover, the status of econometrics in relation to realism is not an uncontroversial subject. Nell and Errouaki’s work is worth careful consideration because its themes are so little considered within econometrics, despite their relevance for the role and realisticness of econometrics. The vast majority of work on econometrics concerns problems of method only – the technical limits of methods and resolutions to technical problems – very little of it concerns how (and whether) an econometric inquiry should be structured and pursued in order to be adequate

A bootstrap view on dickey-fuller control charts for AR(1) series

Dickey-Fuller control charts aim at monitoring a random walk until a given time horizon to detect stationarity as early as possible. That problem appears in many fields, especially in econometrics and the analysis of economic equilibria. To improve upon asymptotic control limits (critical values), we study the bootstrap and establish its a.s. consistency for fixed alternatives. Simulations indicate that the bootstrap control chart works very well. --Autoregressive time series,control chart,invariance principle,least squares,resampling,unit root

Econometrics Applying to the Interdisciplinary Studies

The paper deals to the idea that econometrics represents an useful instrument for thye economic analysis, even at regional level. According to the labor market conditions, econometrics allows several techniques in order to estimate the structural parameters of an a priori specified system of simultaneous stochastic equations. Moreover, the econometric approach highlights the labor system function as to maximize the employees’ number and labor demand, or to minimize the unemployment rate. A distinct part of the analysis covers the regional econometric approach in connection to regional location and optimum models. The main conclusion of the analysis is that econometrics is able to force the knowledge limits not only in regional economics. The analysis and the conclusions are supported by pertinent diagrams and mathematical relations

Econometrics Applying to the Interdisciplinary Studies

The paper deals to the idea that econometrics represents an useful instrument for thye economic analysis, even at regional level. According to the labor market conditions, econometrics allows several techniques in order to estimate the structural parameters of an a priori specified system of simultaneous stochastic equations. Moreover, the econometric approach highlights the labor system function as to maximize the employees’ number and labor demand, or to minimize the unemployment rate. A distinct part of the analysis covers the regional econometric approach in connection to regional location and optimum models. The main conclusion of the analysis is that econometrics is able to force the knowledge limits not only in regional economics. The analysis and the conclusions are supported by pertinent diagrams and mathematical relations

Growth Econometrics

This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and second, drawing inferences on long-run economic outcomes from cross-section and temporal variation in growth. We describe the main stylized facts that have motivated the development of growth econometrics, the major statistical tools that have been employed to provide structural explanations for these facts, and the primary statistical issues that arise in the study of growth data. An important aspect of the survey is attention to the limits that exist in drawing conclusions from growth data, limits that reflect model uncertainty and the general weakness of available data relative to the sorts of questions for which they are employed.

Practical volatility and correlation modeling for financial market risk management

What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds

Practical Volatility and Correlation Modeling for Financial Market Risk Management

What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds.