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APPLIED ECONOMETRICS With Eviews Applications
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. 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. 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. 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. 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.
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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. 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. 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.
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. In Step 5, change the number in the Lags to include: Dummy Dependent Variable Techniques In this chapter: Estimating the linear probability model UE Estimating the binomial logit model UE Estimating the binomial probit model UE Interpreting the results of binary dependent variable regression 6. Estimating the multinomial logit model UE Exercises The workfile named women The data for this example are printed in UE, Table The name of the dummy variable is changed from D, in UE, Table To estimate the linear probability model printed in UE, Equation Open the EViews workfile named Women A series named JFOLSP is created that predicts whether a women is expected to be in the labor force based on the linear probability model.
The formula applies the decision rule: Double click the icon to reveal the percentage of correct predictions from the OLS model on the status line in the lower left of the screen 0. To estimate the weighted least squares model specified in UE, Equations Open the EViews workfile named women Select Name on the equation menu bar, enter EQ02a in the Name to identify object: Note that the coefficient on the Z variable is the constant i.
Select Name on the equation menu bar, enter EQ02b in the Name to identify object: A series named JFWLSP is created that predicts whether a women is expected to be in the labor force based on the linear probability model. To estimate the binomial logit model printed in UE, Equation From the Estimation Settings: The window will change to reflect your choice. There are two parts to the binary model specification. First, in the Equation Specification: Second, check logit as the Binary estimation method: Click OK to run the logit regression.
A series named JFLOGP is created that predicts whether a women is expected to be in the labor force based on the linear probability model. The linear probability model results and the binomial logit model results can be compared by opening both regression equation results in the work area i. To estimate the binomial probit model printed in UE, Equation From the Equation Specification: Second, check probit as the Binary estimation method: Click OK to run the probit regression.
Select Name on the equation menu bar, enterEQ04 in the Name to identify object: A series named JFPROP is created that predicts whether a women is expected to be in the labor force based on the linear probability model. Interpreting the results of binary dependent variable regression: The estimated coefficient on each independent variable is easy to interpret in an OLS model, but difficult to interpret in a model estimated using the probit or logit technique.
However, the relative size of each coefficient reflects the relative effect of the independent variables on the predicted probability for the dependent variable. Interpretation of the coefficient values is complicated by the fact that estimated coefficients from a binary dependent model cannot be interpreted as the marginal effect on the dependent variable.
The multinomial logit process will not be discussed in detail here because the EViews 3. EViews gives you the error message Near singular matrix when the logit is used to estimate the model with the Equation Specification: Follow the procedures outlined in: Estimating the linear probability model. Estimating the binomial logit model. Estimating the binomial probit model.
Open the EViews workfile named Mort See Estimating the linear probability model.
(PDF) APPLIED ECONOMETRICS With Eviews Applications
See Estimating the linear probability model and Estimating the binomial logit model. Simultaneous Equations In this chapter: Generating time series for taxes and net exports using structural equations UE, p. The identification problem and the order condition UE, The data for this model is found in the EViews workfile named macro Two variables that are included in the macroeconomic model must be generated from other data series see note at the bottom of UE, Table Follow these steps to generate time series values for T taxes and NX net exports using the structural equations in the model: A new series icon for T is created in the workfile window.
A new series icon for NX is created in the workfile window. To estimate the two-stage least squares model printed in UE, Equation Click OK to reveal the Estimation Output view printed below.
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The yellow highlighted portions of the regression output reflect the selections made in the dialog window shown above. Two-Stage Least Squares Date: This information is followed by the usual coefficient, t-statistics, and asymptotic p-values. EViews uses the structural residuals in calculating all of the summary statistics. These structural residuals should be distinguished from the second-stage residuals that you would obtain from the second-stage regression if you actually computed the two-stage least squares estimates in two separate stages.
To estimate the two-stage least squares equation printed in UE, Equation To generate the forecast values from this equation, select Forecast on the equation menu bar, enter YDF in the Forecast name: EViews will create a new variable in the workfile named YDF. Note that we have used the instrumental variable YDF instead of the actual variable YD for disposable income. The method, dependent variable, and variable names are highlighted in yellow in the OLS regression output shown below.
To compare the coefficients, std. Look at all three and compare the data printed in the red-boxed area for each regression. This supports the hypothesis that OLS estimates of coefficients have a positive bias in simultaneous equation models simultaneity bias. Contrarily, TSLS estimated coefficients tend to have a downward bias. In order to get accurate estimates of standard errors and t- scores, the estimation should be done on a complete two-stage least squares program like EViews TSLS.
When OLS is used to estimate the second stage, it ignores the fact that the first stage was run at all UE, footnote 11, p. The order condition for identification is easy to assess in EViews. Count, to make sure that the number of independent variables, not counting the constant, in the Equation Specification: Refer to Estimating CO with least squares. Double click the icon in the EViews Macro Click Estimate on the equation menu bar and click OK. If the icon is not in the workfile, you must go back and follow the steps outlined in Estimating two-stage least squares regression using EViews TSLS method.
Follow the procedures outlined in Chapter 9. Forecasting In this chapter: Forecasting confidence intervals UE Forecasting with simultaneous equation systems UE The chicken demand model developed in Chapter 6 was estimated using data from In order to forecast a variable beyond , the workfile range and sample must be expanded.
Follow these steps to forecast chicken consumption for - using ordinary least squares: To expand the sample, select Sample on the workfile menu bar, change the second number in the window from to , and click OK. Scroll to the bottom of the group spreadsheet and make sure that it looks like the table below. Forecasting chicken consumption using a generalized least squares model estimated with the AR 1 method UE, Complete the section entitled Forecasting chicken consumption using OLS before attempting this section.
Select Name on the equation window menu bar, enter EQ04 in the Name to identify object: YFAR1 Y Forecasting chicken consumption using a generalized least squares model estimated with the Cochrane-Orcutt method UE, Follow these steps to use the Cochrane-Orcutt method to estimate a GLS model for chicken consumption. If you have questions concerning the procedure, review the appropriate section of Chapter 9.
Open EQ01 by double clicking its icon in the workfile window. The coefficient on the E -1 term i. This evidence points to positive serial correlation. Calculate the new residual series by typing the following formula in the command window: Double click the icon in the workfile window and read the value, Select Sample on the workfile menu bar and change the End date from to Open EQ03 and select Forecast on the equation menu bar. Make sure that Y is checked in the Forecast of: Change the Forecast name: Check to make sure that the Sample range to forecast: This is due to the fact that the text uses rounded coefficient values for Equation 9.
Delete this group object when finished. Forecasting confidence intervals UE, Open the EViews workfile named Htwt1. Select Name on the equation menu bar and enter EQ01 in the Name to identify object: Select Forecast on the equation menu bar. Enter YF in the Forecast name: To view the forecast weight of the male student standing 6'1" tall, double click the YF series icon in the workfile window and scroll to the bottom. The forecast weight for the 21st observation is Select Sample on the workfile menu bar, change the Sample range pairs or sample object to copy: Enter the name E as the Name for residual series, and click OK.
Generate a new series for the residuals squared i. Generate a new series named XDEV2 for the residuals squared by selecting Genr on the workfile menu bar, entering the equation: To calculate the upper confidence interval for the 6'1" student, type the following formula all one equation in the command window, and press Enter on the keyboard: To calculate the lower confidence interval for the 6'1"student, type the following formula all one equation in the command window and press Enter on the keyboard: EViews has a Type of Object: EViews models do not contain unknown coefficients to be estimated.
Instead, the Model object allows you to solve for unknown values for the endogenous variables. This topic is beyond the scope of this guide but, if you were to do it, you would follow these steps to forecast with a simultaneous equation model: To solve the model, simply select the Solve button in the model toolbar. You should see the Model Solution dialog box offering various options for controlling the solution process.
EViews solves for the endogenous variables, given data for the exogenous variables. ARIMA autoregressive, integrated, moving average models use three tools for modeling the serial correlation in the disturbance: The first tool is the autoregressive or AR term. The AR 1 model introduced above uses only the first-order term but, in general, you may use additional, higher-order AR terms.
Each AR term corresponds to the use of a lagged value of the residual in the forecasting equation for the unconditional residual. An autoregressive model of order p is denoted as AR p. The second tool is the integration order term. Each integration order corresponds to differencing the series being forecast. A first-order integrated component means that the forecasting model is designed for the first difference of the original series. A second-order component corresponds to using second differences, and so on. The third tool is the MA, or moving average term.
A moving average forecasting model uses lagged values of the forecast error to improve the current forecast. A first-order moving average term uses the most recent forecast error, a second-order term uses the forecast error from the two most recent periods, and so on. In ARIMA forecasting, you assemble a complete forecasting model by using combinations of the three building blocks described above.
You can use the correlogram view of a series for this purpose see Testing for nonstationarity by calculating the auto correlation function ACF for a description of this process. D CO Step 3. Select Name on the Method: Least Squares equation menu bar and Date: Convergence achieved after iterations Step 4. Forecasting chicken C Select Forecast on the S. Enter COF Sum squared resid Check to make Durbin-Watson stat 2. Estimated MA process is noninvertible Step 6.
To view the forecast, double click the COF series and scroll to the bottom of the spreadsheet to view the following forecast values for - Refer to Running a simple regression for Woody's Restaurants example to estimate Equation 3. Then follow the steps in Forecasting chicken consumption using OLS to make an unconditional forecast of F for , given the numbers printed in the table in UE, Exercise 2c, p.
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