The basics of econometrics using Eviews

Econometrics aims to explore relationship between economic. Econometrics models can be classified as follows:.

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To examine the degree of relationship betw een export and. To investigate the impact of student-teacher ratio on s tudent. To examine the relations between inflati on and. Features of the good econometric model may be summar ized as. Structural equations are t he equations specific for the economic. There are different types of structural equations such as:. It in cludes reduced form of equations. Keynesian macro model is a.

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Reduced form equations indicate that the endogenous. In the reduced form of equations the endogenous variables are. These types of models are called the classical regression. In this model all of th e exp lanatory variables should be. Stochastic modeling is a technique of predicting outcomes and takes. Economic relationship is not a n exact relationship; a disturbance or. Deterministic model is a mathematical model in which outcomes are. In deterministic models, given input. In comparison, stochastic models use ranges of values for. Interrelationship among ISE, welfare and monetary p olicy.

How do financial linkages react to. To determine the segmented an d.

Regression Analysis Flow Chart. Data are the piece of information or knowlege which are used for. Qualitative data is extremely varied in nature. It includes virtually any. In -depth interviews include both individual in terviews one - on -one. The d ata can.

In-depth interviews dif fer fro m. The purpose of the interview is to probe the idea s of. Direct observation differs from interviewing in that the observer does. It includes field research to. The data can be obtained. Written documents refer to existin g and available documents. Generally, the qualitative methods are limited by the imagination of. There are a wide variety of methods th at are.

Some of them are mentioned. To analyze the ethical sensitiveness in the n ovels published. Participant observation is one of the most common and demanding. The researcher should be a. Direct observation is different fr om participant observation in the. Instead, direct observer tries to be as. The researcher is observing certain.

For instance, one might observe. It is associat ed with quantitative research and the main goal is to. The questions are selected based on research objectives. Unstructured interviewing requires direct interaction between the. It differs from traditional. A case study is an intensive study of a specific individual or specific. For in stance, Freud developed case studies of several. Quantitative methods focus only on numbers and frequencies rather.

Qualitative methods are ways of collecting data which are c oncerned. In modern research, most psychologist s tend to adopt a combination. Quantitative and qualitative method provide different outcomes, and. Quantitative an d qualitative d ata can be ob tained from. Primary d ata are those that the researcher coll ects himself. A secondary data research uses existing data. It can be obtained from.

Experimental data is obtained through experiments in order to. It is mu ch more expensive to. It als o has administration. In sciences, experimental data is data produced by a measu rement ,. It can be qualitative or quantitative, each being. Most of the data in the economic research is ob tained through. It includes su rveys telephone surveys, on st reet. In econ omics, the. Researchers who us e observational data can obtain data from lab. A time series is a collection of observations of variables ob tained. A times series allows the. It can also show the impact of cyclical, seasonal and irregular.

Time series can be. A stock series is a measure of certain attributes at a point in time. An origi nal time series shows the actual movements in the. A cyclical eff ect is any regular fluctuation or changes in daily, weekly,. For example, t he num ber of people using. An irregular effect is any movement that. A seasonally adj usted series involves estimating an d removing the. A trend series is a seasonally adjusted series that has been further.

For example, the trend. Number Year Inflation Export Interest rate. Panel data is consisted of multiple entities or variables observed at. I t is also known as longitudinal or cross-. These entities could be states, companies,. Panel data allows the researcher to control variables which are not.

It is suitable for multilevel or hierarchical modeling. Number University Year Bachelor Grad. Cross Sectional Data consists of multiple entities or variables which. Analysis of cross -sectional data. For example, the researcher wants to measure current education. A sample of 2,0 00 people randomly can be selected. This cross-sectional sample provides us with a. Qualitative explanatory variables are unobserved variables.

If dependent variable shows a. A determin istic linear trend variab le may be. If the data exhibits different regimes whether they should have been. RSS for the whole sample, restricted sum of squares. RSS for the sub- sample,. F-distribution is valid if the error terms are independently and. The null hypothesis of no structural. For a break at one date the test resembles the Chow forecast test,. More dates can be specified for the breakpoint test.

No breaks at specified breakpoi nts. The log transformation yields appealing interpretation of coefficients. The interpretation is good for small changes only. But also, lnY has a. However it implies a Th e d ifference. This is an approximation of t he.

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Sometimes it is unclear which form to choose, and thus we have to:. Select ing the functional form on. Stationarity is an issue for time series data and is a pre-condition to. Nonstationary time serie s data s hould. It is calculated by subtracting old one from new one and dividing the. It is used for the give emphasis return. A base year is used for comparison o f the two or more time series. The arbitrary level of is selected so that percentage.

Regression is an econometric technique for est imating the. It h elps to analyze how the typical. Linear regression estimates how much y cha nges. The main purpose of linear regression. It consists of omitted independent. Regression equation consists of two components:. OLS minimizes the squared d ifference b etween observed and. The residuals are observed,. OLS is trying to get a b est model in order to. Main objective is t o minimize. In order to determine the minimum f with respect to p redicted. ESS is a function of regression coefficients.

Regression equation in Eq. Using the data income. Multivariate regression model includes more than one regressor. In regression analysis, main objective is to observe how dependent. The causal relationship which is detected through regression analysis.

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How many independent variables should be included in the. It is assumed that the independent variables are not random. Their values are fixed in repeating samples. Error term, e i, includes all the independent variables which are not. We assume that e ffect of error term on. The multivariate regression equation above shows the impact of Lot. Size, Number of bedrooms, Number of B athrooms, and Number of. The Regression model should. The conditional expectation of the error term, given. As a result of this critical assumption, we can write the.

This can be interpreted as th e model for mean or. If there is only one independent variable,. If there is more than one independent variable, it. Assumpt ion is satisfied. If there is autocorrelation, an in crease in the error. This is the assumption of no. Alternatively, there is no specification bias or.

It is implicitly assumed that the number of. It is necessary for. The estimators are linear, that is, they are linear functions of the. Linear estimators are easy to understand and. Linear models can be expressed in a form that is linear in the. Reset-type test Ramsey, is the most common test for testing. This testing procedure involves the. In other words, estimation and testing results are. Nonlinearity is seen very clear in a plot of the observed versus. The points in the f irst plot.

The evidence of a "bowed" pattern. It indicates that the model makes. If the transformation seems to be appropriate, a nonlinear. The latter transformation is. In the class of linear estimators, OLS estimators have. As a result , the true parameter values can. The assumption of normality can be expressed as follows:. If the assumption of normality does not hold, then the OLS. However, without normality one cannot. The following null hypothesis should be specified before. The null is that the skewness. The above assumptions can be tested jointly using the Jarque-.

Bera test JB, which follows asymptotically a chi-squ are. Scatter plot shows whether the series are distributed normally or not. It skewed the left side and shows leptokurtic behavior. Minim um - Result of skewness and kurtosis indicates that income series are. If the residuals do not follow a normal pattern, omitted variables,.

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Normally, normality does not represent mu ch of a problem. Under the assumed above mentioned conditions, OLS estimators are. BLUE best linear unbiased estimators. This is the essence of the. With normality assumption, CLRM is known as the. For the linear regression model, a n estimate of the va riance of the. Sam ple Q1 Q4. Skew ness - 0. It measures the proportion of the variance in the dep endent variable.

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After es timation of a particular linear mo del, a q uestion comes up. A popular measure for the goodness of fit is called R 2 R. The closer to one the better is the fit,. Some source of variation in exploratory variable is much harder to. For example, models which explain. R 2 can be interpreted as a measure of quality of the model an d its.

It measures mainly linear approximation. A common way to deal with this problem is to correct the. This gives the valu e of. This new measure of goodness of fit punishes the inclusion of. As th e number of independent variable in the regression model. T he impact of. It is possible to test whether the increase in R 2 is statistically.

It is same as testing whether the coefficient of new added. The appropriate F-statistics can be written. J is the number of newly added regressors. Linearity is the situation in which the relationship between the. If linearity exists, the. In this sense, non-linearity. The regression function can be nonlinear in two different cases:. For example, the effect on test. The linearity of the regression can be examined:. The points should be symmet rically distributed around. Look carefully for evidence of a. The tutorials are split into self-contained sessions, although we recommend that new users of EViews work their way through the tutorials one by one.

Each tutorial is accompanied by data files so that you may follow the tutorials in your own copy of EViews. The data files are available in the Supporting Files side bar of each tutorial. You should note that the tutorials are written based on EViews 10, however the vast majority of material covered in them is applicable to earlier versions of EViews too. Workfiles An introduction to the Workfile, EViews' main data file format, including how to create new empty workfiles, and how to import data from other sources into your EViews workfile. Samples Samples are an important part of EViews, and allow you to easily work with different parts of your data.

You will learn how to use EViews' deep understanding of time frequencies to easily select different date ranges to work with, or, if you are using cross-sectional data, pick different categories or cross-sections. This tutorial explains how to create new series, bring data into series, use automatically updating series, and how to display different views of your series.

The Group object, which is simply a collection of Series objects, is also explained. Data Functions An introduction into the most common series creation and manipulation functions in EViews, including random-number generators, time-series functions and statistical functions. Date Functions A description of the EViews functions that deal with dates and dated data.

Dummy Variables How to create binary, or dummy variables, based upon an observation's date, or the values of other variables. Frequency Conversion Converting data from one frequency to another, including moving from high to low frequencies e. Basic Graphs This tutorial covers how to create graphs of your data in EViews, including an explanation of Graph Objects compared to Graph Views, a summary of some of the most common graphing options, as well as an introduction to working with graphs of panel data.

Statistical Analysis An introduction to performing statistical analysis in EViews. Although not every statistical procedure is discribed, this tutorial should provide enough understanding to get you started. Tables and Spools Tables are the basis of presentation output, whereas spools hold multiple collections of output objects tables, graphs, equations. Spools are useful for organizing results and for working with multiple objects.

Also covered are Dated Data Tables, which offer sophisticated tools to help you construct tables that combine original data along with transformations, frequency conversions, and summary statistics. Basic Estimation An introduction into estimation in EViews, focusing on linear regression. This tutorial includes information on specifying and creating new equation objects to perform estimation, as well as post-estimation analysis including working with residuals and hypothesis testing.