Eviews Homework Solution

Now you can request Eviews Homework Solution completed by top freelancer in Econometrics and Eviews. Our freelancers at AnEc Center for Econometrics Research work under the mentorship of Professor Muhammad Anees, leading econometrician and Eviews expert. Eviews Homework Solution include complete workfile (Eviews file format), detailed written answers and complete training through GoToMeeting.com to ensure maximum learning outcomes to guarantee highest grades in the coursework.

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Step 1: Discuss your requirements

Before we begin working on your project, we would like to discuss all your requirements to be clear in all respect. It needs us to see your data, hypothesis, objectives or related PDFs about lecture notes and also related textbooks. It will help us propose if you can do the work yourself with out small feedback for free. We will process to next step once you confirm that the solution be produced by our top freelancers in Econometrics.

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Step 3: Agree to project milestones

Now is the time to confirm payment once you agree to the project milestones. The milestones are project phases for each step of the homework solution. Mostly, the project milestones includes phases like: initial results in Eviews, final results in Eviews, first draft of answers in Word file and final edited answers with Eviews results and complete answers. Payment can be split into two phases. One installment in advance, then second installment after the results in Eviews are reviewed.

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How To Interpret ARDL Results?

How To Interpret ARDL Results? This is a very frequently asked question on our social media groups for Econometrics, Statistics and Research. Some of the students ask about How To Interpret ARDL Results using Stata? and many others ask about How To Interpret ARDL Results using Eviews. We will use Eviews to estimate Eviews for this tutorial but the interpretation does not depend on softwares but the statistic/calculated estimates we have. So, the results from Stata can equally be interpreted relevantly.

How To Write ARDL Equations?

We can help you learn how to interpret ARDL Results in the following few steps. But first we should understand what is ARDL in few lines and how we can estimate ARDL in Eviews. The ARDL is a modified regression model to test and estimate the cointegrated relationships between time series variables. Using bounds test instead of Johenson Jusuleus Cointegration Test, the presence of long run relation between the time series variables can be predicted and that too using the F statistic. The ARDL equation is given in the following:

yt = β0 + β1yt-1 + .......+ βkyt-p + α0xt + α1xt-1 + α2xt-2 + ......... + αqxt-q + ε,      (1)

The ECM equation from ARDL setup is:

Δyt = β0 + Σ βiΔyt-i + ΣγjΔx1t-j + ΣδkΔx2t-k + φzt-1 + et    ;        (2)

The conditional ECM (Pesaran et al. 2001) is written like:

Δyt = β0 + Σ βiΔyt-i + ΣγjΔx1t-j + ΣδkΔx2t-k + θ0yt-1 + θ1x1t-1 + θ2 x2t-1 + et   ;    (3)

The Cointegrated Equation can be written as:

yt = α0 + α1x1t + α2x2t + vt       ;       (4)

and the bounds test can be conducted on the coefficients (H0:  θ= θ1 = θ2 = 0) from the equation 3. The critical values of this F Statistic should be looked upon from the Tables in (Pesaran et al. 2001) or Narayan (2005):

Now, we will interpret the estimated results from an actual Eviews output. To estimate ARDL using Eviews (Learn Time Series Analysis Theory Here), one needs to open the WORKFILE or data in Eviews. Then click on Quick, then click on Estimate Equation and a new small window will appear. We select ARDL from the Method section of this small window which is near the bottom and below the list of variables textbox. Once ARDL is selected the options on this Window changes. We enter our list of variables in order of DV and IVs and intercept. The rest of the options can be selected from same Window like whether to include intercept, trend or both, lags for both the dependent variables and regressors and

Eviews allows us to specify the equation in form of regression models with general list of coefficients and estimated values in form the regression equation like this:

DEBT = C(1)*DEBT(-1) + C(2)*DEBT(-2) + C(3)*DEBT(-3) + C(4)*GDP + C(5)*GDP(-1) + C(6)*GFC + C(7)*GFC(-1) + C(8)*GFC(-2) + C(9)*GFC(-3) + C(10)*TRADE + C(11)

DEBT = 0.9172*DEBT(-1) - 0.4375*DEBT(-2) + 0.3484*DEBT(-3) - 0.0614*GDP - 0.0955*GDP(-1) + 0.3101*GFC + 0.1751*GFC(-1) + 0.7765*GFC(-2) + 0.3646*GFC(-3) - 2864*TRADE + 1375446667.23

The Cointegrated equation from above model becomes where we truncted the coefficients at 4 decimal points:

D(DEBT) = 1375446667.2237 -0.1718*DEBT(-1) -0.1569*GDP(-1) + 1.6265*GFC(-1) -28649248.9413*TRADE** + 0.0890*D(DEBT(-1)) -0.3484*D(DEBT(-2)) -0.0614*D(GDP) + 0.3101*D(GFC) -1.1411*(DEBT - (-0.9135*GDP(-1) + 9.4649*GFC(-1) -166714193.3055*TRADE(-1) + 8003926456.4992 ) -0.3646*D(GFC(-2)) )

Then we can get the first estimation table which looks like this:

Interpret ARDL Results Main Table

In this results, the first part is summary of the information, Eviews has worked on. We can see that Dependent variable and the method has been reported in the first two lines respectively. Then the time and information about the sample time period and number of observations is given. Also, we can see that Eviews estimated a few regressions models to come up to the selected model based on information criteria. AIC selected the ARDL with Model with 3 lags for the dependent (DEBT) and the independent variables (GDP GFC and DEBT)were included with their level and selected lags of 1, 3 and 0 respectively. 0 lags of an independent variable means it will be added to list of regressor in level only. The second part of the main equation gives values of coefficients, standard errors and t statistics with p-values. We can interpret this an AR and DL equations like we do in ADL models. We can either interpret the coefficients as simple regression coefficients keeping mind the nature of X and Y variables like in log or percentage etc or we can conduct an F test to jointly determine if the X with its lags has any effect on the Y which works like causality test (not what Granger Causality is). We can generalize this step to conduct Granger Causality test on given set of coefficients as well upon confirmation that the hypothesis test matches the one which Granger causality is based upon (hint for those who wish to conduct Granger Causality after ARDL).

The main results of the ARDL Regression model is given in the central table in Eviews results output window. We can see this portion contain few columns each on list of variables in the model with their lags, coefficients values, standard errors, t or Z statistics and corresponding p-values. We can interpret these as conventional regression models coefficients are interpreted like if the variables are in logs or simple measurement. One has to keep in mind the sign and size of coefficients for interpretation if the objective is inferential study of predictive study respectively. A positive coefficient on the level variable means that the current change in X affects current level of Y positively and negative sign of the level variable means the current change in X affects current level of Y negatively. The coefficient of a lag 1 of X means the effect of changes in past years values of X results in a change in current values of Y or the current changes in X affects values of Y in next time periods. This can also be positive or negative. The coefficient of second lag means the change in X today will cause changes in Y two years from today or a change in X two years ago will affect Y today. It can be positive and negative as well as we can see it in level changes or first lag changes.

The Standard Error of the coefficients are given the sampling distribution of the coefficients. We can see these value the are the standard deviation of the coefficients from different samples of the data if the same coefficients are estimated from each sample and taken as a sample itself. This gives us a margin of error or limits within which the value of coefficients can vary within a limit on average. We will need this determine the t-Statistic and Z statistic to define the hypothesis testing for the given coefficients.

The next column is t Statistics. We use this column to test the null hypothesis that

 

Interpret ARDL Results: Some Basic Results:

 

Interpret ARDL Results for Short Run Relationship

Interpret ARDL Results for Long Run Relationship

References:

Pesaran, M. H. and Y. Shin, 1999. An autoregressive distributed lag modelling approach to cointegration analysis. Chapter 11 in S. Strom (ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge University Press, Cambridge. (Discussion Paper version.)
Pesaran, M. H., Shin, Y. and Smith, R. J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.

Pesaran, M. H. and R. P. Smith, 1998. Structural analysis of cointegrating VARs. Journal of Economic Surveys, 12, 471-505.

Toda, H. Y and T. Yamamoto (1995). Statistical inferences in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66, 225-250.

ARDL Lag Selection

There are commonly asked question that about ARDL Lag Selection or how to select lag structure for ARDL models. In this simple tutorial using Eviews, we demonstrate and explain how to select the ARDL model based on various criteria. Note, that Eviews produces a the ARDL model automatically so we have to explain the model selection criteria in a little more details so we are clear as to what Lag selection is.

Information Criteria

Eviews reports Log Likelihood, Akaike Information Criteria, Bayesian Information Criteria, Hannan-Quin Information Criteria and Adj. R-sq (not specifically an information criteria). We should note that many models are proposed in different settings so selection of an information criteria for lag selection in a time series models should be carefully dealt with.

One can read more about AIC vs BIC in a reply to Adrift by Methodology Center (Read more about Latent Class Analysis here):

Dear Adrift,

As you know, AIC and BIC are both penalized-likelihood criteria. They are sometimes used for choosing best predictor subsets in regression and often used for comparing nonnested models, which ordinary statistical tests cannot do. The AIC or BIC for a model is usually written in the form [-2logL + kp], where L is the likelihood function, p is the number of parameters in the model, and k is 2 for AIC and log(n) for BIC.

AIC is an estimate of a constant plus the relative distance between the unknown true likelihood function of the data and the fitted likelihood function of the model, so that a lower AIC means a model is considered to be closer to the truth. BIC is an estimate of a function of the posterior probability of a model being true, under a certain Bayesian setup, so that a lower BIC means that a model is considered to be more likely to be the true model. Both criteria are based on various assumptions and asymptotic approximations. Each, despite its heuristic usefulness, has therefore been criticized as having questionable validity for real world data. But despite various subtle theoretical differences, their only difference in practice is the size of the penalty; BIC penalizes model complexity more heavily. The only way they should disagree is when AIC chooses a larger model than BIC.

AIC and BIC are both approximately correct according to a different goal and a different set of asymptotic assumptions. Both sets of assumptions have been criticized as unrealistic. Understanding the difference in their practical behavior is easiest if we consider the simple case of comparing two nested models. In such a case, several authors have pointed out that IC’s become equivalent to likelihood ratio tests with different alpha levels. Checking a chi-squared table, we see that AIC becomes like a significance test at alpha=.16, and BIC becomes like a significance test with alpha depending on sample size, e.g., .13 for n = 10, .032 for n = 100, .0086 for n = 1000, .0024 for n = 10000. Remember that power for any given alpha is increasing in n. Thus, AIC always has a chance of choosing too big a model, regardless of n. BIC has very little chance of choosing too big a model if n is sufficient, but it has a larger chance than AIC, for any given n, of choosing too small a model.

So what’s the bottom line? In general, it might be best to use AIC and BIC together in model selection. For example, in selecting the number of latent classes in a model, if BIC points to a three-class model and AIC points to a five-class model, it makes sense to select from models with 3, 4 and 5 latent classes. AIC is better in situations when a false negative finding would be considered more misleading than a false positive, and BIC is better in situations where a false positive is as misleading as, or more misleading than, a false negative.

So we can conclude that between AIC and BIA for ARDL Lag Selection, one has to think not mere on some references from literature but also a reason to why the model has been proposed.

ARDL Lag Selection using Eviews

So in Eviews, we can proceed in the following to ARDL lag selection for proposed an ARDL model.

  1. Open the time series data in Eviews workfile
  2. Test all variables to be included in the model for unit root to make sure none is I(2) i.e. unit root in first difference or stationary in second difference.
  3. Then estimate the ARDL equation selecting your Dependent variable (DV) and all the independent variables (IVs). Here, we would like to comment down that the stationarity of DV (or DV should be I(0) before estimating the ARDL model is not a must to have condition as has been asked many times. In literature, we can see most of the papers has the same I(1) variables but when we estimate, the model specification itself defines the DV is in difference. So it becomes stationary by default when we estimate an ARDL model using an I(1) variable. One example model has been produced here: Image result for ARDL model (see the full paper here)
  4. Once the ARDL model has been estimated, click on the View menu of the results window.
  5. Click on Model Selection Summary.
  6. Now, we have two options, either to see the ARDL Lag Selection in Table with all the proposed ARDL models with corresponding lag selection criteria like LogL, AIC and BIC etc. Or we can see the Graph of selected models in ranking based on the criteria we chose.
  7. Either way, click on any of the two option, we can find the ARDL Lag Selection criteria and we will be able to determine the best model based on Lower AIC or Lower BIC as we proposed above for when to use AIC or when to use BIC.

I am sure, this simple intro to ARDL Lag Selection will help us in the future to determine a feasible ARDL Model. For more details, enroll to one of our Econometrics Courses here to develop similar critical skills in Applied Econometrics Research.

If you need Assistance in Data Analysis for writing your PhD Thesis or MS Dissertation, hire Top Econometrics Freelancer here.

Nonlinear ARDL using Stata and Eviews

Nonlinear ARDL using Stata and Eviews is a part of our video series in Econometrics Workshops. The video workshops are conducted at AnEc Center for Econometrics Research. The current workshop on Nonlinear ARDL using Stata and Eviews was held on September 10, 2017 and presented by Professor Anees. Professor Anees has been working as a senior econometricians and has been a top freelancer in Econometrics.

The free econometrics workshops are conducted on monthly basis. You can register your email to be alerted for next free workshops to be held through our state of the art and high quality video conference tool. The workshops in Econometrics offers free certificates as well that be attacked to your LinkedIn profiles and are fully verifiable.

Nonlinear ARDL using Stata and Eviews workshop included discussion of the theoretical prospects of using the nonlinear ardl for testing the hypothesis of causality. The key element of motivation for using nardl is that the effect of a positive change in x on a positive change in y is not similar to the effect of negative change in x on the negative change in y. To capture this, the authors of NARDL introduced the concept of asymmetric causality which they initially captured through Nonlinear ARDL.

In this video tutorial, we demonstrate how to use Nonlinear ARDL using Stata and Eviews. Our key motivation for this free workshop in Econometrics is based on the requests from our PhD students in Economics and Finance who are bound to apply mostly the ARDL or NARDL for their time series data analysis to write their thesis or publication quality research papers. We are always happy to help our students learn the latest econometric methods and tools to conduct data analysis for writing high impact research reports.

Using Nonlinear ARDL helps our students to conduct data analysis for writing thesis as well as research papers with higher ratio of acceptability.

Watch this video of the complete Econometrics workshop on Nonlinear ARDL using Stata and Eviews. Follow our Youtube channel for more videos on Stata and Eviews. You can also request private and instructor led online course in Advanced Econometric Modeling here.

Stata vs Eviews! Which one is better?

Stata vs Eviews

Do you need to know which one is better? Stata vs Eviews? Then read our following guide based on some observations and some econometric reasons to decide whether you need Stata or Eviews for data analysis.

This guide is only comparing Stata vs Eviews for helping you decide for selection any one of the two econometric softwares and should not be considered an official Stata or Eviews promotion. We are independent users of Stata and Eviews and we use both equally valued.

Read more about Stata vs Eviews here

Stata vs Eviews

We have compiled this perceptions based comparative index based on our freelancers and academic community who are using both Stata and Eviews on regular basis to help our clients, students, colleagues and frineds.

Read the criteria as a preference for Stata (Red) and Eviews (Sky Blue). If The benchmark is close to hundred, our community regard Stata is preferred compared to if the (1-100%) for Eviews which means Red being close to 1%.

Statistical Methods Covered

98%

Econometrics Methods Covered

95%

Progammers Choice For Replication

90%

Addins and Extensions Availability from usersbase

90%

Forums and Blogs on Eviews To Help Its Users

85%
SOME COMPARATIVE FEEDBACK ON STATA VS EVIEWS

How to compare Stata vs Eviews?

To select which one is better? Stata vs Eviews, we can point out some of the following features from both of the econometric softwares

Stata

Stata menu allows very extensively how to deal with Data Management, graphics and analysis.
Stata offers to begin analysis of data through Statistics menu which offers around 5-10 times more methods.
Stata allows built in menu for variables transformation and recoding in addition to codes and do files.
Stata has been used to create MarkDowns to create HTML files recently allowing more options now.

Eviews

Eviews menus are little complex to figure out where to begin for data management and analysis.
Stata helps to begin Quick analysis through its Quick menu and offers selected methods for analysis of data.
Eviews has limited features to recode variables through Menu driven to transform and convert variables.
Eviews is more like the same since its last previous couple of versions and it is more an academic software everytime.

Stata vs Eviews: Online Courses

If one has to search on google for online courses with use of Stata or Eviews, the number of courses available for online learning is more from Stata users than Eviews. AnEc Center for Econometrics Research also offers some courses with Stata and Eviews.
Panel Data Analysis using Stata

Panel Data Analysis using Stata

To understand better the differences between Stata and Eviews, we recommend to enroll for our courses in Applied Econometrics Research and Panel Data Analysis using Stata.

Enroll for Panel Data Analysis using Stata

Financial Econometrics using Eviews

Financial Econometrics using Eviews

Eviews is more famous among academic researchers in Economics and Finance who use Time Series Econometrics for data analysis. Enroll for Financial Econometrics Using Eviews to learn more.

Enroll for Financial Econometrics using Eviews

Stata vs Eviews: How the comparison work?

This comparative analysis of Stata vs Eviews is based on our observation and has no direct relevance for marketing by the Stata or Eviews providers. We are independent researchers and users of Stata and Eviews and we love both the softwares equally. The differences are only to help our students who usually ask for such comparison between common softwares like Stata, Eviews and SPSS.

In this simple discussion on Stata vs Eviews, we have only focused on the following simple criteria of covering econometric and statistical techniques and convenience. Our findings reveal that menu driven differences in Stata vs Eviews are more clear to support Stata while users who are more conversant in using Eviews, are finding Eviews as more convenient in dealing with Time Series Analysis mostly because it does the job of analytics automatically while Stata allows its users to select many things before getting the final output. If a user of Stata is proficient and has been using Stata for a sufficient time, he will definitely prefer to work on Stata compared to Eviews for the same reason of more analytical outcomes can be created compared to Eviews.

So the ultimate conclusion of Stata vs Eviews comparison is not to be used for technical comparison at all. We will share our technical comparison on a separate thread with our users in a few days time of this earlier comparison.

Enroll for Stata + Eviews Course HereRequest Data Analysis For Thesis Writing

Nonlinear ARDL using Eviews

Nonlinear ARDL using Eviews or NARDL using Eviews

This simple video tutorial on Nonlinear ARDL using Eviews or NARDL using Eviews is dedicated to Hassan Hanif who originally wrote an article on NARDL using Eviews on his blog. The key steps in estimating an Nonlinear ARDL using Eviews or NARDL using Eviews is given below video and can also be found in the following sections of this page...
Watch Video

Nonlinear ARDL using Eviews

This simple video tutorial on Nonlinear ARDL using Eviews or NARDL using Eviews is dedicated to Hassan Hanif who originally wrote an article on NARDL using Eviews on his blog. The key steps in estimating an Nonlinear ARDL using Eviews or NARDL using Eviews is given below:
This simple video tutorial on Nonlinear ARDL using Eviews or NARDL using Eviews is dedicated to Hassan Hanif who originally wrote an article on NARDL using Eviews on his blog. The key steps in estimating an Nonlinear ARDL using Eviews or NARDL using Eviews is given below:

1. Test each of the variable is not unit root in second difference.
2. Make the CUSUM of your explanatory variables.
3. Make the differences of these CUSUM and dependent variables.
4. Estimate a Stepwise Least Square Regression model.
5. Test for cointegration using the Wald restrictions and testing it with Pesaran et al. (2001) critical values.
6. Determine the asymmetric causality from the NARDL

We hope this video tutorial will further simplify and save your time to run Nonlinear ARDL using Eviews or NARDL using Eviews.

If you need a complete training in Applied Econometrics Research or Advanced Econometric Modeling, click on www.aneconomist.com or copy paste it to your internet browser and enroll for a private and instructor led online course with verifiable certificate.

The codes you might need to create differences and CUSUMs in Eviews and STEPLS, are here:
genr ddebt=debt-debt(-1)
genr dgfc = gfc-gfc(-1)
genr pos = dgfc >=0
genr dgfc_p = pos*dgfc
genr dgfc_n = (1-pos)*dgfc
genr gfc_p = @cumsum(dgfc_p)
genr gfc_n = @cumsum(dgfc_n)

d(debt) c debt(-1) gfc_p(-1) gfc_n(-1)
ddebt(-1 to -4) dgfc_p(-0 to -4) dgfc_n(-0 to -4)

Enjoy Nonlinear ARDL using Eviews or NARDL using Eviews.

Video Tutorials in Econometrics

Subscribe to our YouTube Channel and watch out other short videos in Econometrics to learn more. Or request private and instructor led courses in Applied Econometrics or Advanced Econometric Modeling from AnEc Center for Econometrics Research

Partial Least Squares Regression using SPSS

Watch a small video on how to install PLS plugin in SPSS, Anaconda Python and its libraries to run PLS in SPSS.

Enroll for online courses here.

Logistic Regression Models using Stata

A simple video tutorial from our online course in Applied Econometrics Research and Writing A Thesis in Quick Time.

Request Data Analysis here.

Phillips-Ouliaris Cointegration Test

In this short tutorial in Eviews, we would explain the basic steps of conduting Phillips-Ouliaris Cointegration Test. To run Phillips-Ouliaris Cointegration Test in Eviews, we will need to ender the data as dated frequency and panel structure. Theoretically, the Phillips-Ouliaris Cointegration Test can found well explained in P. C. B. Phillips and S. Ouliaris (1990): Asymptotic Properties of Residual Based Tests for Cointegration. Econometrica 58, 165–193 which can be downloaded here.

Phillips-Ouliaris Cointegration Test

To run Phillips-Ouliaris Cointegration Test in Eviews, open Eviews and follow the steps below:

The following video tutorial explains the above steps to help replication be more convenient. If you would like to learn more about Applied Econometrics Research using Eviews, check our private and instructor led courses here.

Econometric Models Selection

In this simple outline on the issue of commonly raised douts on how to select an economic model, I would like to sum the issue in this 300 words article. Econometric Models Selection is one key problem for all. The model selection is based on the type of data and type of variables. Here, we mention an order following which helps an econometric model strategy development for us. We assume to use Panel data to develop econometric model selection strategy. Initially, to determine the right set of models, the nature of data structure for the panel data follows like this: A. Check data structure. 1. if N>T, then follows B series of steps. 2. If T>N, then follow the steps in C series.

If we assume the panel data has N sufficiently greater than T, then simple panel data or instrumental/GMM models for panel data can be achieved using the following order. This helps in Econometric Models selection looking into the key steps of the econometric analysis of the given panel data.

For Case 1, follow these steps.
B1. The first stepOLS
B2. RE/FE
B3. Hausman
B4. Assumptions of RE or FE from B3.
B5. Endogeneity Tests
B6. Instrumental Regression
B7. GMM if seens dynamics/autocorrelation in B4.

If the data at hands is such that T is sufficiently larger than N, the panel time series structure will be evidenced and hence our econometric models selection strategy will follow the following procedures. The unit roots and cointegration is for panel time series data which are also sometimes considered as longer time panel data or longer panel data. In such cases, the time series properties in estimators are heavily observed and hence the steps to select an econometric models selection strategy will include these.
For Case 2, follow these steps
C1. Unit Root
C2. Cointegration
C3. VECM


ARDL and Unit Root Testing using Eviews

ARDL Cointegration using Eviews 9

To estimate ARDL using Eviews 9 on Time Series Data, first open the data file/workfile, Click on your DV, press control key on keyboard, now left click to select all your IVs one by one, once selected then right click on any selected variables and open these as Equations. Once you get the Methods window in Eviews, go the methodology selection from Estimation Setting near to bottom, select ARDL from the list and click Okay. Now you cans elect the lags of DV and IV and any other options for the the methods. You can click on the OK button to get your estimates.

Augmented Dickey Fuller Unit Root Test using Eviews

Augmented Dickey Fuller Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Augmented Dickey Fuller Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Augmented Dickey Fuller Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Augmented Dickey Fuller as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Augmented Dickey Fuller Approach, we can click on OK to get the test results.

Phillips Perron Unit Root Test using Eviews

Phillips Perron (PP) Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Phillips Perron (PP) Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Phillips Perron (PP) Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Phillips Perron (PP) as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Phillips Perron (PP) Approach, we can click on OK to get the test results.

KPSS Unit Root Test using Eviews

KPSS Unit Root Test using Eviews We can test a time series variable for Unit Root Test following Kwiatkowski-Phillips-Schmidt-Shin Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on KPSS Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the KPSS as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using KPSS Approach, we can click on OK to get the test results.

Ng-Perron Unit Root Test using Eviews

We can test a time series variable for Unit Root Test following Ng-Perron Approach in Eviews following the steps outlined below. First of all open the Eviews workfile or the Excel data in Eviews, then right click on any of the variables we would like to test for unit root based on Ng-Perron Approach and click on Open. The series opens in spreadsheet in Eviews. We can click on View in the left upper corner of the new spreadsheet window in Eviews. Then we can click on Unit Root Test in this list that pops down by clicking on View tab. This opens the dialogue box as shown in the inserted screenshot from Eviews itself, we can see that it has mainly four sections. Main section is related to selecting the test type. The Eviews produces unit root test results following 6 methods. We will select the Ng-Perron as test type. The we will select either the Level, Difference or Second Difference. Next we can select either to include intercept or both of trend and intercept or none. On the right side of the same window, we can either ask Eviews to use lags automatically or we can insert manually the maximum lags into the model to base our unit root test on. Once we select everything as per our assumed approach to test a series for unit root using Ng-Perron Approach, we can click on OK to get the test results.

List of Best Econometrics Books

Most of the times, we need to select a book to understand econometrics more easily. In the following, I have shared my personally recommended set of best books for econometrics which I have been reading frequently. These books on econometrics have helped me develop a strong understanding of some core issues in developing econometric skills for research and data analytics, both for academic and professional research for freelance projects.

Mostly Harmless Econometrics: An Empiricist's Companion
Mostly Harmless Econometrics: An Empiricist's Companion (Paperback)
by Joshua D. Angrist (shelved 11 times as econometrics)
Basic Econometrics 4th Economy Edition
Basic Econometrics 4th Economy Edition
by Damodar N. Gujarati (shelved 9 times as econometrics)
Introductory Econometrics: A Modern Approach
Introductory Econometrics: A Modern Approach (Hardcover)
by Jeffrey M. Wooldridge (shelved 8 times as econometrics)
Econometric Analysis
Econometric Analysis (Hardcover)
by William H. Greene (shelved 8 times as econometrics)
Econometrics
Econometrics (Hardcover)
by Fumio Hayashi (shelved 7 times as econometrics)
A Guide To Econometrics
A Guide To Econometrics (Paperback)
by Peter E. Kennedy (shelved 5 times as econometrics)
A Guide to Modern Econometrics
A Guide to Modern Econometrics (Paperback)
by Marno Verbeek (shelved 4 times as econometrics)
The Econometrics of Financial Markets
The Econometrics of Financial Markets (Hardcover)
by John Y. Campbell (shelved 4 times as econometrics)
Time Series Analysis
Time Series Analysis (Hardcover)
by James D. Hamilton
Econometric Analysis of Cross Section and Panel Data
Econometric Analysis of Cross Section and Panel Data (Hardcover)
by Jeffrey M. Wooldridge (shelved 4 times as econometrics)
Applied Time Series Econometrics
Applied Time Series Econometrics (Paperback)
by Helmut Luetkepohl (Editor) (shelved 3 times as econometrics)
Bayesian Econometrics
Bayesian Econometrics (Paperback)
by Gary L. Koop (shelved 3 times as econometrics)
Advanced Econometrics
Advanced Econometrics (Hardcover)
by Takeshi Amemiya (shelved 3 times as econometrics)
Schaum's Outline of Statistics and Econometrics
Schaum's Outline of Statistics and Econometrics (Paperback)
by Dominick Salvatore (shelved 3 times as econometrics)
A Course in Econometrics
A Course in Econometrics (Hardcover)
by Arthur S. Goldberger (shelved 3 times as econometrics)
Econometric Theory and Methods
Econometric Theory and Methods (Hardcover)
by James G. MacKinnon (shelved 3 times as econometrics)
Introduction to Econometrics
Introduction to Econometrics (Paperback)
by Christopher Dougherty (shelved 3 times as econometrics)
Microeconometrics Using Stata
Microeconometrics Using Stata (Paperback)
by A. Colin Cameron (shelved 2 times as econometrics)
Computational Methods in Statistics and Econometrics
Computational Methods in Statistics and Econometrics (Hardcover)
by Hisashi Tanizaki (shelved 2 times as econometrics)
Nonparametric Econometrics
Nonparametric Econometrics (Paperback)
by Adrian Pagan (shelved 2 times as econometrics)
Econometric Methods
Econometric Methods (Hardcover)
by Jack Johnston (shelved 2 times as econometrics)
Introduction to Modern Bayesian Econometrics
Introduction to Modern Bayesian Econometrics (Hardcover)
by Tony Lancaster (shelved 2 times as econometrics)
Econometrics
Econometrics (Paperback)
by Badi H. Baltagi (shelved 2 times as econometrics)
Using Eviews for Principles of Econometrics
Using Eviews for Principles of Econometrics (Paperback)
by R. Carter Hill (shelved 2 times as econometrics)
Limited-Dependent and Qualitative Variables in Econometrics
Limited-Dependent and Qualitative Variables in Econometrics (Paperback)
by G.S. Maddala (shelved 2 times as econometrics)
The Practice of Econometrics: Classic and Contemporary
The Practice of Econometrics: Classic and Contemporary (Hardcover)
by Ernst R. Berndt (shelved 2 times as econometrics)
Financial Econometrics: Problems, Models, and Methods
Financial Econometrics: Problems, Models, and Methods (Hardcover)
by Christian Gourieroux (shelved 2 times as econometrics)
Estimation and Inference in Econometrics
Estimation and Inference in Econometrics (Hardcover)
by Russell Davidson (shelved 2 times as econometrics)
Analysis of Panel Data
Analysis of Panel Data (Paperback)
by Cheng Hsiao (shelved 2 times as econometrics)
Microeconometrics: Methods and Applications
Microeconometrics: Methods and Applications (Hardcover)
by A. Colin Cameron (shelved 2 times as econometrics)
Introduction to Econometrics (Addison-Wesley Series in Economics)
Introduction to Econometrics (Addison-Wesley Series in Economics)
by James H. Stock (shelved 2 times as econometrics)
Principles of Econometrics
Principles of Econometrics (Hardcover)
by R. Carter Hill (shelved 2 times as econometrics)
Learning and Practicing Econometrics
Learning and Practicing Econometrics (Hardcover)
by William E. Griffiths (shelved 2 times as econometrics)
Econometric Models And Economic Forecasts
Econometric Models And Economic Forecasts
by Robert S. Pindyck (shelved 2 times as econometrics)
Analysis of Financial Time Series
Analysis of Financial Time Series (Hardcover)
by Ruey S. Tsay (shelved 2 times as econometrics)
Poverty Dynamics: Interdisciplinary Perspectives
Poverty Dynamics: Interdisciplinary Perspectives (Hardcover)
by Tony Addison (Editor) (shelved 1 time as econometrics)
Student Solutions Manual To Introductory Econometrics
Student Solutions Manual To Introductory Econometrics
by Jeffrey M. Wooldridge (shelved 1 time as econometrics)
Econometric Methods
Econometric Methods (Hardcover)
by Manoranjan Dutta (shelved 1 time as econometrics)
Evolutionary Dynamics And Extensive Form Games
Evolutionary Dynamics And Extensive Form Games (Hardcover)
by Ross Cressman (shelved 1 time as econometrics)
Theory of econometrics
Theory of econometrics
by A. Koutsoyiannis (shelved 1 time as econometrics)
Mastering 'Metrics: The Path from Cause to Effect
Mastering 'Metrics: The Path from Cause to Effect (Hardcover)
by Joshua Angrist (shelved 1 time as econometrics)
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
by Andrew Gelman (shelved 1 time as econometrics)
Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models
Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Hardcover)
by Julian James Faraway (shelved 1 time as econometrics)
Probability and Statistics
Probability and Statistics (Paperback)
by Morris H. DeGroot (shelved 1 time as econometrics)
Introduction to Bayesian Econometrics
Introduction to Bayesian Econometrics (Hardcover)
by Edward Greenberg (shelved 1 time as econometrics)
Introductory Econometrics with Applications
Introductory Econometrics with Applications (Hardcover)
by Ramu Ramanathan (shelved 1 time as econometrics)
Mastering 'Metrics: The Path from Cause to Effect
Mastering 'Metrics: The Path from Cause to Effect (Kindle Edition)
by Joshua D. Angrist (shelved 1 time as econometrics)
Mathematical Statistics and Data Analysis
Mathematical Statistics and Data Analysis (Hardcover)
by John A. Rice (shelved 1 time as econometrics)
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS (Hardcover)
by John Kruschke (shelved 1 time as econometrics)
Bayesian Data Analysis
Bayesian Data Analysis (Hardcover)
by Andrew Gelman (shelved 1 time as econometrics)