Spurious Correlation From Tyler Vigen

Spurious Correlation or Spurious Correlation is the concept when strong association exist between unrelated variables. This makes a lot of sense to develop the caution that not all research findings can actually be theoretical viable so authors need a strong theoretical logic and practicality to define the association. Spurious regression has been very well document by Tyler Vigen (copy paste the link https://tylervigen.com/spurious-correlations if the link does not seems to work) and there are very interesting updates. We recommend their portal be visited on regular basis for evidence of spurious correlation on interesting cases.

Few examples of Spurious Correlation from Vigel's portal are presented in the following:

Margarine consumption linked to divorce is the first interesting example of spurious correlation between two very unrelated but shocking alarming indicators from real life. It is also what BBC has mention in their report mentioning some other examples from Tyler Vigel portal. Nicolas Cage is one top Hollywoord star. He has been mentioned on Tyler Viger series on spurious correlation with an example mentioning Nic's movies appearances and people drowning in ponds in US States. Similarly, we find the spurious correlation showing the association between consumption of cheese and people died through folding in their bed sheets.

Similarly, we Economists needs very strong care to define relationship between economic indicators. Theoretically and technically very unrelated time series might appear to be very strongly correlated to each other but that might be of the no use case. Hence, defining an econometric models merely from available data does not meaning anything of significance but one should all the three components of research significance profoundly available for witness. These three indicators should include:

  1. Whether the relationship has any significance for solving a real world problem.
  2. The relationship found should be able to predict too much of the theory.
  3. The data should be very strongly related in process of generation. If the data generating processes are not correlated to each, the chances of spurious correlation or spurious regression again increases.

Now enjoy the few cases of Spurious correlation found by Vigel here: https://tylervigen.com/spurious-correlations




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
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

Writing First Program In R

R provides an extensive environment to write all kinds of modelling and statistical programs at the cost of expertise only while nominally the price is less than 0 for you if you can get to work with it because in spite of paying for purchasing costly software, you can earn using R programming skills while spending nothing. In the following article, we will begin our first program using R.
Let us assume we want to test if a number is positive or negative. To test this using R, we need to define what a positive number is and what a negative number looks like in Roman style. It is well known that positive numbers are greater than 0 or 0 itself is a positive number. On the other hands, a negative number is always less than 0. So define the following:
1) A positive number >0 or =0
2) A negative number <0
Now plan what R will require to know to state a number of any value, an integer, a whole number of a rational number being positive or negative. We need to program it. Each R program starts with the definition of the program.
We define our program showing if the number is positive or negative using R. The program will begin typing:
givelesettter = function(thenumber){
The left side of the above definition gives the name of the program, the right side defines what the actual is, function() states R will use left side name as a function to evaluate any number being positive or negative. The syntax will start after the curly braces {.
After that, we will define the above statements for showing the number if positive and should be at least equal to 0 or greater than 0. So we write the first condition to apply in R for our program to use which should be:
if (thenumber >= 0){
followed the curly braces to define the logical value stating the nature of the number which we defined as positive number in the following line of the programe.
theletter = "Positive Number"
and end this logic with the curly bracket as below.
After defining the logic for a number to be positive, we can define any number less than 0 as negative using the following piece of code.
if (thenumber < 0){
theletter = "A Negative Number"
We end up our code/program by stating return() argument which will display the results of a right inputted argument in the program and put an end of program curly bracket.
After writing our first simple program, we can do a practice of what have intended to by defining a number whether positive or negative. Before doing this, we have to save the written program in a text file with extension .R in the working directory of R. To use the function or program we will set the working directory using:
setwd(“address of working directory folder”) like setwd(“E:/rwork”)
And load the program by typing in R workspace as:
See the example program as given:
## is used to state that here Begins the Comments
## The code will look like the following
giveletter = function(thenumber){
if (thenumber >= 0){
theletter = "Positive Number"
if (thenumber < 0){
theletter = "A Negative Number"
Type the following example to test the working of the code.
giveletter(30) ## will return the response as Positive Number
giveletter(-30) ## will return the response as Negative Number

Cointegration, Unit Root and ARDL

Assume we have three variables. X1, X2 and X3. In all of the following three cases, we can to test all of X variables for unit root by at least two to three different tests. I personally recommend using ADF and KPSS to test the opposite null hypotheses. ADF's null is unit root series and KPSS is stationary series. Case 1. If all variables are I(0), we can use VAR as Johansen-Juselieus Cointegration Pre-condition is not satisfied. Case 2. If Two variables are I(1) but only one is I(0), or Two are I(0) and one is I(1), then ARDL from Pesaran (2001) is a feasible approach. Case 3. If at least one variable is I(2) and others are either I(0) or I(1) or mixed, the Toda-Yamamoto Causality can be applied after estimated the VAR. Note also, Toda-Yamamoto is a causality test not a test of short run or long run relationship and I usually assume Granger type causality by Toda-Yamamoto or Granger Causality itself has no dependent on VECM or Cointegration.

So in nutshell, If you variables all I(0), you can use VAR. If your all variables are I(1) or I(2), use JJ and Granger Causality. If all variables are mixed I(1) and I(0) but none is I(2), use ARDL and you can also use Granger Causality after running a VAR. If you have mixed order I(0), I(1) and I(2), use Toda Yamamoto Causality Test.

Step By Step Instructions for running ARDL in Eviews.

The steps to conduct ARDL cointegration test in Eviews are:

  1. Open your time series in Eviews
  2. Dfuller and KPSS your variables to check no variable is I(2)
  3. Single click on Dependent Variable (DV)
  4. Press Ctrl Key on keyboard, and click one on all Independent variables (IV) one by one
  5. Once DV and IV are are selected, Righ click on them
  6. A small caption open, Click on Open As Equation
  7. Another selection window appear, select maximum lags for DV and IV
  8. Click on Ok go get the ARDL estimates.

The screenshot will explain the required steps in simple to understand instructions.

Cointegration, Unit Root and ARDL

Cointegration, Unit Root and ARDL

We will share the complete the silenced one minute video tutorial in next part of this tutorial.

The step by step instruction of run ARDL using Stata can be:

  1. Open your data in Stata
  2. Tsset your data with the time variable
  3. Dfuller and KPSS your variables
  4. There should be no I(2) in the variables.
  5. Findit ardl code
  6. or scc install ardl
  7. Once installed, run the code as: ardl dv ivs, lags(#) ec
  8. ## should be replaced with a number of lags.

Stata Homework Solution

As experienced academics and freelancers in Stata, Econometrics and Statistics, having seens homework questions from around the countries, we understand there a few differences among the requirements for many different types of Stata homework and assignments. In this tutorial, we are offering a step by step guide to best solve any Stata homework or other problems. In case, you have technical problems in Stata, we can help you get the Stata Homework Help for better understanding the solutions. Ask our econometrics and statistics for additional guides to ensure you learn all the details of the contents from the Stata Homework and related learning objectives for Statistics or Econometrics.

There are many agencies around the internet with uncertain system of providing solutions for many questions related to Stata homework. We, provide a complete solution each Stata Homework with an academic aim that helping to learn the difficult parts of programming for Stata homework helps a student get more benefits from the Stata homework compared to mere buying already completed solutions for Stata homework. Our academic experts in Statistics and Econometrics provide complete Stata homework help and you can ask for a complete tutorial on each question for your Stata homework to make sure you learn everything related to the Stata homework, Econometrics or Statistics.

There can be difference in opinion on how to get best grades by doing Stata homework, but our experience as academics, researchers, freelancers and stata homework solution provides to more than 400 graduate and bachelor students in Economics, Statistics and Business studies reveal that best grades can be ensured through the following key process. It can be completed independently to answer all questions of the Stata homework or you can request our top ranked freelancers in Stata and Statistics to help you solve the difficult part of answering all questions in Stata homework related to programming and writing do files in Stata. Follow the steps outlined below to get a best mark on each of your Stata homework:

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Stata vs SPSS : Which One Is Better?

As a regular lover of Stata for econometric analysis and quantitative analysis, I am happy to share my personal experience about few things that will help justify Stata vs SPSS. This does not mean we qualify or certify any of Stata vs SPSS is better than others. Yes, we personally believe, there are some advantages in Stata vs SPSS.
The Stata vs SPSS comparison can be After studying Stata for about half a year my department asked me to tell them some more about STATA. One of the things my coll qualified by answering a simple question that what makes Stata better than SPSS or what makes SPSS better than Stata? To answer this question about Stata vs SPSS and compare based on the econometric analysis or statistical techniques or even using Stata for Quantitative Analysis or Quantitative Analysis using SPSS, one can see the major differences between the Stata vs SPSS. So this personal evaluation of Stata vs SPSS can be easily qualified purely on an academic observation and personal views.

On Statalist, where I read about Stata frequently, Marion de Leeuw of Dept. of Methodology and Statistics at Maastricht University wrote that SPSS has two advantages vs many disadvantages. The advantages are user friendliness in complex graphics and charting and routine for logistic regression allowing for interactions. Marion also mention that SPSS's ANOVA commands are might also be considered as user friendly but he does not use it. Some statistical techniques like running Probit Models are nearly impossible to run with SPSS with stinking documentation for help.
Anees has mentioned on his social media pages on facebook (https://www.facebook.com/stata.help) in his satire style that Stata can do 95% of the econometrics and statistical analysis while SPSS can help you with 30% of them. This is though a silly view as he claims it, but if one looks into the Statistics menu in Stata and Analyse menu in SPSS, Stata vs SPSS can be answered by anyone with great confirmation.

An ideal option for commercial statistical software production is to help the user as much as possible. Stata vs SPSS comparison is thus in favour of Stata for its official or technical help compared to the help provision from IBM-SPSS. The documentation of Stata are mor rich with examples compared to documentation of SPSS on major statistical applications. Stata documentation is also to be considered more in academically viable with strong use of examples while SPSS documentation is little lacking in this regard. This further seeks our attention to favour Stata vs SPSS for personal reasons.

One can see easily that the most common techniques for econometric or statistical analysis is multivariate analysis in SPSS is limited to OLS, probit, and logit while Stata can be found to have more rich routines in multiple pooled cross sectional time series. One can easily see that Stata offers a lot more in Count Data Models based on Poisson, negative binomial and the zero inflated poisson routines and maximum likelihood based routines for Tobit, multinomial logit, ordinal logit or probit, and complementary log-log which are not found in SPSS. A regular academic user in many field with the application of these techniques is thus forced to favour Stata vs SPSS.

A well documented observation related to post estimation after many regression models is that SPSS does not offer much to see validity of the estimated models while Stata has a rich help sources from official routines and unofficial but regularly debated Statalist archives with personally contributed private routines. This makes Stata vs SPSS comparison and decision making even more easier. One can easily decide in favour of Stata based on is based on the estimation of complex surveys based models and covering the clustering options or weights. SPSS allows some but Stata offers much much more options to take clustering, aweights, iweights and pweignts better than SPSS. An example of post estimation results commonly required by researchers and analysts include AIC and BIC or Pseudo RSquared values. SPSS does not allow you to report these values easily until you write Visual Basic scripts for that. Stata on the other hands will ease this issue for you mostly through built in routines within a technique or small ado routines by contributors freely available.

Based on the above, one can develop a good sense to further detail down on Stata vs SPSS. Stay tune on this page as we will compare Stata vs SPSS for specific techniques in next few days.