Many researchers consider that it is pretty hard to. Qualitative research is realtively more difficult in the. For instance, quantitative data such as absenteeism rates or. Alternatively, same issue may. In fact, the research scope, philosophical preferences of the. The qualitative research is ab out exploring issues, understanding. Th e subjective dat a which is. It p roduces comprehensive information,.
Findings are not conclusive and. It is necessary, t o develop an initial understanding and. This type of research aims at discovering the underlying motives and. Attitude or opinion research i. Through such research we can analyze the various factors which. It may be concluded that, to apply a. Quantitative research focuses on gathering numerical data and.
Its objective is to. Mostly, research samp les are consisted of. Confirmatory deduction scientific research method is used to test. Conclusion is made based on the statistical finding. Applied research is a st udy that has been conducted in order to apply. It is the application of.
The research project is likely to be short term often less than 6. For example, you might be investigating. The output from this type of research is likely. This type of study is concerned with. It focuses on finding an immediate solution to an existing. When the research problem is of a less specific nature and the. Basic research focus es on problem solving theoretically,. Theoretical framework is developed and t ested through empirical.
For this reason, the de ductive method is referred to as. Inductive resear ch represents type of study in which general. A particular research may be applied study, analytical stud y with. Conceptual research is related to some abstract idea s or theory. Empirical research relies on experience or observation alone, often. It is data-based research,. The researcher starts with a working hypothesis or guesses the. Then he collects th e data and sets up experimental. Evidence gathered through experiments or empirical studies is today.
Historical research utilizes historical sources such as documents,.
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The purpose is to make people aware o f what has happened. Economic modeling is one of the most important parts of a research. The researcher organizes and. The researcher follows certain steps in this model and deliver s. Visual models involve graph ical representation of different economic.
It consists of graphs with lines and curves which provides. The models help to present the complex. The mathematical models are systems of simultaneous equat ions. These models require a. For example, a basic microe conomics model includ es a supply. Empirical models are one type of mathematical models d esigned to. The b asic model is mathematical and the. To examine the demand elasticity for luxury cars in l ow. Simulation models are mainly used and created by using different.
The basic features of mathematics are required. The mathem atical complexity varies depending on the research. Most m odels which are used in econ omics are comparative statics. These models provide information about what happens over. The model estimate generally starts with predefined. In the end, the. Dynamic models directly incorporate time into the model. Sometimes dynamic models better represent the subtleties of. To investigate the changes in income when in vestment changes. The equations of the model are programmed in a software.
To investigate the impact of class size on the averag e test. While describing the economic activity of consumers in genera l or a. A rational expectations theory is the most. The behavior and actions of t raders. Portfolio investors, traders of financial asset s or.
Hence, the choice of the theory of. M odels incorp orating and. Econometrics is a science an d art of using economic theory and. Econometrics finds and explains the r elationship. It clarifies the interact ion and. To examine the role of Interest rate on Inflati on rate. Econometrics aims to explore relationship between economic. Econometrics models can be classified as follows:. 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. 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.
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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. 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. Choosing the Independent Variables In this chapter: EViews makes it easy to try alternative versions of an OLS model in order to determine whether omitting a variable is likely to result in specification bias or whether the variable is irrelevant.
The four important specification criteria UE, pp. However, when the theory is not absolutely clear about the relevancy of including a specific variable in a model, the other three criteria i. The only way to check these criteria is to run the regression with and without the variable and evaluate the results in terms of t-test, adjusted R2, and bias.
The following steps outline a procedure to determine whether the price of beef PB is a relevant variable in the demand for chicken model UE, Equation 6. Open the EViews workfile named Chick6. In this new equation window, select Estimate on the equation menu bar, delete PB from the Equation Specification: Compare and evaluate the two equations based on t-statistics, adjusted R2, and bias. The Freeze button on the objects toolbar creates a duplicate of the current view of the original object.
The primary feature of freezing an object is that the tables and graphs created by freeze may be edited for presentations or reports. Frozen views do not change when the workfile sample is changed or when the data change. The purpose for freezing the regression output table is to allow us to view it later by double clicking the objects icon in the workfile window. In order to do that, the frozen object must be named. EViews makes it easy to lag variables in an equation. However, the demand for chicken model UE, Equation 6. Note that EViews reports that it has adjusted the sample see the graphic to the right.
The range and sample in the workfile window show but the equation output reports Sample adjusted: You should be aware that if you include lagged variables in a regression, the degree of sample adjustment will differ depending on whether data for the pre-sample period are available or not. For example, suppose the workfile range is and the workfile sample is If you specify a regression with PC lagged one period, EViews will not adjust the sample because it can use the data for in the workfile. Select Forecast on the equation menu bar, enter YF in the Forecast name: Select Name on the equation menu bar, enter EQ03 in the Name to identify object: The F-statistic, highlighted in yellow, is the same as reported in UE, p.
This is in spite of the fact that none of their coefficients are individually significant. In this example, there are three restrictions, so the Chi-square test statistic is three times the size of the F-statistic, but the p-values of both statistics indicate that we can decisively reject the null hypothesis that the three coefficients are zero. Least Squares this case, the test Date: This is in spite of the fact that all of their individual t-statistics are insignificant. If you specify a large number of fitted terms, EViews may report a near singular matrix error message since the powers of the fitted values are likely to be highly collinear.
Open the EViews workfile named Drugs. Select Name on the equation window menu bar, enter EQ02 in the Name to identify object: Open EQ01 and EQ02 at the same time. Use information in these tables, UE 6. Choosing A Functional Form In this chapter: Table with EViews specification for functional forms 2.
Calculating "Quasi - R2" for a linear versus log-lin model using EViews 4. Linear models are frequently too restrictive to properly fit the functional form suggested by the underlying theory. The last column of Table 7. Note that a constant C should be included in all models even if theory suggests otherwise see UE, p. You must have a workfile open in order to specify and estimate a regression model.
Form Equation specification EViews specification 7. The dependent variable must be in the same form when using R2 and adjusted R2 to compare the overall goodness of fit between two equations. For example, it would not be appropriate to compare the R2 for a linear model with a double-log or a log-lin model.
If this method is used you must name the equation to save it. Select Name on the equation menu bar and enter the desired name in the Name to identify object: Likewise, it would be appropriate to compare R2 for double-log and log-lin functional form models.
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In order to demonstrate the process, the car acceleration data introduced in UE, Exercise 16, p. The steps below show how to compare the goodness of fit for models using S the number of seconds it takes a car to accelerate from 0 to 60 miles per hour as the dependent variable versus using the natural log of S as the dependent variable. In both models, the independent variables are the same as the original model printed at the top of UE, p. Calculating "Quasi - R2" for a linear versus a log-lin model using EViews: Open the EViews workfile named Cars7.
Select Name on the equation menu bar, write linear in the Name to identify object: Minimize the equation object named linear. Select Name on the equation menu bar, write loglin in the Name to identify object: Select Forecast on the equation menu bar, select S in the Forecast of: A new series named SF appears in the workfile window. To calculate the quasi-R2, type the following equation in the command window and press Enter: A new variable named quasir2 will appear in the workfile window.
Double click on it and the value for the quasi-R2 will be displayed in the lower left of the screen 0. The quasi-R2 calculated in Step 9 i. The F-test can be used to test a wide range of hypothesis concerning regression coefficients.
For example, suppose that the claim was made that when a car has a manual transmission it increases its acceleration speed i. Translating this into the language of UE, Equation 7. Just looking at the size of the estimated coefficients, it appears that you can easily reject the hypothesis because the absolute value of the coefficient on Ti is only about However, these coefficients are just estimates.
Follow these steps to carry out an F-test for the null hypothesis that the absolute value of the coefficient on Ti is times larger than the absolute value of the coefficient on Hi. Select Name on the equation menu bar, write EQ01 in the Name to identify object: The F- statistic compares the residual sum of squares computed with and without the restrictions imposed. If the restrictions are valid, there should be little difference in the two residual sum-of-squares and the F-value should be small. Based on the Wald Test: The calculated F- statistic of 2. Multiple coefficient restrictions must be separated by commas and the restrictions should be expressed as equations involving estimated coefficients and constants.
The coefficients should be referred to as C 1 , C 2 , and so on do not use series names. It supports this result in that rejecting the null hypothesis would be wrong less than The Chi-square statistic is equal to the F-statistic times the number of restrictions under test. Chow's Breakpoint Test divides the data into two sub-samples. A significant difference indicates a structural change in the relationship. Follow these steps to apply the Chow breakpoint test, as described in UE, pp. The F-statistic is based on the comparison of the restricted and unrestricted sum of squared residuals.
This may be a problem if, for example, you want to test for structural change between wartime and peacetime where there are only a few observations in the wartime sample. It supports this result in that rejecting the null hypothesis would be wrong less than 0. The log likelihood ratio statistic is based on the comparison of the restricted and unrestricted maximum of the log likelihood function.
The calculated value for LR test statistic of Multicollinearity In this chapter: Perfect multicollinearity UE 8. Detecting multicollinearity with simple correlation coefficients UE 8. Transforming multicollinear variables UE 8. Exercises Perfect multicollinearity UE 8. EViews is incapable of generating estimates of regression coefficients when the model specification contains two or more variables that are perfectly collinear.
High simple correlation coefficients between variables is a sign of multicollinearity. Follow these steps to compute the simple correlation coefficient between variables: Open the EViews workfile named Fish8. Create a group object for the variables found in UE, Equation 8. Select Freeze on the group object menu bar to create a table of the simple correlation coefficients. Select Name on the table object menu bar to name the table. R2 in the command window, and press Enter. Variable transformations can be achieved by creating a new variable or by simply writing the transformation in the Equation Specification: In many ways, the latter is preferred because the equation output labels depict the transformation.
Otherwise, it is easy to forget what transformations have been made. The table below lists the most common transformations used to rid a model of multicollinearity, along with the EViews specification EViews specification to make it happen. Follow the steps outlined in Detecting multicollinearity with simple correlation coefficients and Calculating Variance Inflation Factors to check for high correlations and high VIF's in the implied regression model.
Follow the steps outlined in Detecting multicollinearity with simple correlation coefficients and Calculating Variance Inflation Factors to check UE, Equation 8. Run the regressions for this problem using the Mine8. Serial Correlation In this chapter: Creating a residual series from a regression model 2.
Plotting the error term to detect serial correlation UE, pp. Estimating generalized least squares using the AR 1 method UE 9. Exercise Serial correlation analysis involves an examination of the error term. The demand for chicken model specified in UE, Equation 6. Creating a residual series from a regression model: Follow these steps to estimate the demand for chicken model UE, Equation 6. Enter E in the Name for residual series: Select Save on the workfile menu bar to save your changes.
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Plotting of the error term to detect serial correlation UE, pp. Complete the section entitled Creating a residual series from a regression model before attempting this section i. Follow these steps to view a residuals graph in EViews: Open EQ01, by double clicking the icon in the workfile window.
Note the residual series exhibits a pattern akin to the graphs displayed in UE, Figure 9. Thus, graphical analysis indicates positive serial correlation. Steps 3 and 4 below show how to generate a time series plot of the same residual series E. Open the residual series named E in a new window by double clicking the series icon in the workfile window to open the residual series from EQ01 in a new window. Follow the steps below to estimate the first order serial correlation coefficient and test for possible first order serial correlation: Select Name on the equation menu bar, enter EQ02 in the Name to identify object: To detect seasonal serial correlation in a quarterly model, regress the residuals against its value lagged four periods enter E C E -4 in the Equation Specification: Similarly, to detect seasonal serial correlation in a monthly model, regress the residuals against its value lagged twelve periods enter E C E in the Equation Specification: Follow these steps to view the Durbin-Watson d test for EQ Open EQ01 by double clicking the icon in the workfile window.
The Durbin-Watson statistic is highlighted in yellow and boxed in red. Use the Sample size printed after Included observations: More formally, the DW statistic measures the linear association between adjacent residuals from a regression model. If there is no serial correlation, the DW statistic will be around 2. The DW statistic will fall below 2 if there is positive serial correlation in the worst case, it will be near zero. If there is negative correlation, the statistic will lie somewhere between 2 and 4. Positive serial correlation is the most commonly observed form. As a rule of thumb, with 50 or more observations and only a few independent variables, a DW statistic below about 1.
Follow these steps to estimate the chicken demand model using the AR 1 method of GLS equation estimation. EViews automatically adjusts your sample to account for the lagged data used in estimation, estimates the model, and reports the adjusted sample along with the remainder of the estimation output. The estimated coefficients, coefficient standard errors, and t-statistics may be interpreted in the usual manner. The estimated coefficient on the AR 1 variable is the serial correlation coefficient of the unconditional residuals.
For AR models estimated with EViews, the residual-based regression statistics—such as the, the standard error of regression, and the Durbin-Watson statistic— reported by EViews are based on the one-period-ahead forecast errors. These are multi-step approaches designed so that estimation can be performed using standard linear regression. EViews estimates AR models using nonlinear regression techniques. This approach has the advantage of being easy to understand, generally applicable, and easily extended to nonlinear specifications and models that contain endogenous right-hand side variables.
The Cochrane-Orcutt method is a multi-step procedure that requires re-estimation until the value for the estimated first order serial correlation coefficient converges. Follow these steps to use the Cochrane-Orcutt method to estimate the CIA's "high" estimate of Soviet defense expenditures i. Open the EViews workfile named Defend9. To estimate the generalized differenced form of UE, Equation 9. The specification should appear as in the figure below. The variable names are truncated in the EViews regression output table because they don't fit in the variable name cell.
Nonetheless, the regression is correct. To calculate the new residual series, enter the following formula in the command window: The equation should read: The phrase "E successfully computed" should appear in the lower left of your screen. Convert the constant from the final version of EQ03 by typing the following formula in the command window: Double click the icon in the workfile window and read the value for the estimated constant in the lower left of the screen.
Note that this is the same equation reported in UE, Exercise 14, Equation 9. You can re-run the series e equation by clicking the cursor anywhere on the equation in the command window and hitting Enter on the keyboard. Heteroskedasticity In this chapter: Graphing to detect heteroskedasticity UE White's test UE Exercises The petroleum consumption example specified in UE Data for this problem is found in EViews workfile named Gas By graphing the residual from a regression against suspected variables, the researcher can often observe whether the variance of the error term changes systematically as a function of that variable.
Follow these steps to graph the residual from a regression against each of the independent variables in a model: Open the EViews workfile named Gas Select Name on the equation menu bar, enter EQ01 in the Name to identify object: Make a residual series named E and save the workfile. Complete Steps of the section entitled Graphing to detect heteroskedasticity before attempting this section. Follow these steps to complete the Park test for heteroskedasticity: Test the significance of the coefficient on log REG. Follow these steps to complete White's test for heteroskedasticity: EViews reports two test statistics from the test regression.
Since the nR2 value of It is printed above White's test statistic for comparison purposes. Follow these steps to estimate the weighted least squares using REG as the proportionality factor: Note the coefficients highlighted in yellow.
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Select OK to accept the options and select OK again to estimate the equation. Note that the weighted least squares coefficients found in Step 2 are the same as the coefficients found in Step 5 using the EViews weighted least squares option. The scaling of the weight series is a normalization that has no effect on the parameter results, but makes the weighted residuals more comparable to the un-weighted residuals. The normalization does imply, however, that EViews weighted least squares is not appropriate in situations where the scale of the weight series is relevant, as in frequency weighting.
Follow these steps to estimate heteroskedasticity corrected standard errors regression: Check the Heteroskedasticity Consistent Covariances White box see the yellow highlighted and red boxed areas in the graphic on the right. Note that the coefficients are the same but the uncorrected std. This means that the Heteroskedasticity Consistent Covariance correction has reduced the size of the t-statistics for the coefficients, a typical result.
Follow these steps to estimate UE, Equation Create an EViews workfile and enter the average income and average consumption data from the table printed in Exercise 5, p. Refer to Testing for heteroskedasticity: Refer to Remedies for heteroskedasticity: Open the EViews file named Books Open the EViews file named Bid Refer to Serial Correlation Chapter 9. A Regression User's Handbook In this chapter: Exercise How to observe checkpoint items displayed in UE, Table After that enter the following formula in the command window, and press Enter: The TSS can be viewed on the status line in the lower left of the screen.
Multicollinearity Serial correlation Chapter 9: Serial Correlation Heteroskedasticity Chapter Open the EViews workfile named House Check your results for each specification, following the outline printed in UE, p. Time Series Models In this chapter: Testing for serial correlation in Koyck distributed lag models UE The Lagrangian Multiplier LM test 3.
Performing Granger Causality tests UE Testing for nonstationarity with the Dickey-Fuller test Adjusting for nonstationarity Exercises The workfile named macro The examples examine the relationship between current purchases of goods and services CO and the level of disposable income YD. Estimating an ad hoc distributed lag model UE To estimate the ad hoc distributed lag model printed in UE, Equation Open the EViews workfile named Macro Estimating a Koyck distributed lag model UE To estimate the Koyck distributed lag model printed in UE, Equation Estimate the Koyck distributed lag model before attempting this section i.
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To determine whether the value in parenthesis, in the denominator under the square root sign in UE, Equation Press Enter to create a scalar object named denominator. Double click the scalar object icon named denominator in the EViews workfile and view its value in the left corner of the status bar bottom of the EViews window. To view this scalar, double click the scalar object icon named dhtest and view its value in the left corner of the status bar bottom of the EViews window. Open the Equation named EQ02 by double clicking its icon in the workfile window.
Change the number in the Lags to include: This LM statistic is computed as the number of observations times the R2 from the test regression. Since the calculated Breusch-Godfrey LM test statistic of 9. When you select the Granger Causality view, you will first see a dialog box asking for the number of lags to use in the test regressions. Pairwise Granger Causality Tests Date: Follow these steps to calculate the auto correlation function ACF: Open CO in one window by double clicking the series icon in the workfile window. Select level in the Correlogram of: You should pick a lag length that corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other.
In case you want to determine significance by comparing the calculated F statistic with the critical F value from the F Table, the numerator degrees of freedom are given by the number of coefficient restrictions in the null hypothesis i. Since the AC's are significantly positive and the AC k dies off geometrically with increasing lag k, it is a sign that the series obeys a low-order autoregressive AR process.
Testing for nonstationarity with the Dickey-Fuller DF test Follow these steps to conduct the Dickey-Fuller test of the hypothesis that the CO series is non-stationary: Note that EViews will probably display the correlogram view for CO since that was the last view selected in the previous section. Four things have to be specified in the Unit Root Test dialog box to carry out a unit root test. If AC k dies off more or less geometrically with increasing lag k, it is a sign that the series obeys a low-order autoregressive AR process. If AC k drops to zero after a small number of lags, it is a sign that the series obeys a low-order moving-average MA process.
The partial correlation at lag k measures the correlation of CO values that are k periods apart, after removing the correlation from the intervening lags. If the pattern of autocorrelation is one that can be captured by an autoregression of order less than k, then the partial autocorrelation at lag k will be close to zero. Select Trend and intercept for this example.
To see why, read footnote 18, UE, p. Fourth, specify the number of lagged first difference terms to add in the test regression 0 for the DF test. The theory behind each of these selections is beyond the scope of UE and this guide. Advanced econometrics courses deal with these issues. When finished with the selections click OK to reveal the following table: ADF Test Statistic The test fails to reject the null hypothesis of a unit root in the CO series at any of the reported significance levels, since the ADF Test Statistic9 is not less than i.
You will face two practical issues in performing the ADF test.
First, you will have to specify the number of lagged first difference terms to add to the test regression selecting zero yields the DF test; choosing numbers greater than zero generates ADF tests. The usual though not particularly useful advice is to include lags sufficient to remove any serial correlation in the residuals.
Second, EViews asks you whether to include other exogenous variables in the test regression. You have the choice of including a constant, a constant and a linear time trend, or neither in the test regression. If the test fails to reject the test in levels but rejects the test in first differences, then the series contains one unit root and is of integrated order one I 1.
If the test fails to reject the test in levels and first differences but rejects the test in second differences, then the series contains two unit roots and is of integrated order two I 2. In order to determine whether the first differenced series10 is stationary, follow the steps in the previous section and select 1st difference for the Test for unit root in: Open the EViews workfile named Mouse Follow the steps in estimating distributed lag models. Follow the steps in estimating Koyck lag models. Complete Exercise 5b and follow the steps found in Testing for serial correlation in Koyck lag models using the Lagrangian Multiplier LM test.
Complete Exercise 5b and follow the steps found in using the Lagrangian Multiplier LM test to detect serial correlation tests in Koyck lag models.