**اسافر بلد عربى ولا اجنبي**

لو عايز تغير البلد اللى انتا عايش فيه وتسافر عشان تعيش فى مكان افضل و تعليم وصحة افضل فالبلاد العربية هتلاقى فيها ثقافات متشابهه ومفيهاش تغيير نفسى كبير زي اللى هتقابلة فى البلاد الاجنبية. فلو عندك فرصة حلوه فى البلاد العربية سافر لانك مش بتختار مكان لنفسك بس او فرصة شغل افضل وخلاص, انا بتختار مكان لاسرتك كلها, افضل طريق انك تدرس فى دول اوروبية وبعدها تسافر بلاد عربية

**النقد**

طالما فيه نقد يعنى فيه نجاح, واوعى تخلى رأي الناس فيك هو مقياس نجاحك من عدمه لا فى ناس كتير نجاحك بيستفزهم وبيوريهم قد ايه هما قليلين وبتهز صورتهم قدام نفسهم وكل ما تبقى افضل كل ما يكرهوك وينتقدوك اكثر. هيكون رد فعلهم الطبيعى انهم مش بس ينتقدوك لا دول يهاجموك عشان يحاولوا يشوفوا قوتهم فى ضعفك. فالاحوال دى لازم تكون عارف من جواك انك مصدر ازعاج لنفسيات مريضة كتير فترك الكلاب تنبح و سر قدما للامام

**حل المشاكل**

اى مشكلة هتقابلها فى الدنيا او ممكن تظهر فكر فيها تفكير منطقى و هتلاقى حل تقدر تعملة وتوكل على الله بعدها متفكرش تانى بعدها واشغل نفسك بحاجة حلوه لانه طالما علمت اللى عليك فعلا بما يرض الله ومظلمتش حد ولا عايز تظلم وفكرت فى حل يرضى ربنا ويريح قلبك يبقى اى حاجة بعد كدا هتكون افكار من الشيطان بيحاول يشوش بيها على قلبك

**مين اللى بينتقدك عشان بيحبك**

اللى تعرف انه بيحبك لو عايز ينتقدك بييجى يقولك انت بس الموقف الفلانى لو كنت عملت كذا وكذا او اتصرفت بالطريقة الفلانية, او كان تصرفك هيبقى احسن كتير وده اسمه نقد بناء. ده شخص عايز مصلحتك وبيكلمك كلام موضوعى وبيقدملك الحل وفعلا بيحب نجاحك او بيحبك شخصيا. ولكن اللى بيكلمك بصوره عامة من غير ما يقول مواقف وبينتقد شخصك وخلاص ده تنفصل عنه تماما وتضحك وتبتسم وتقول شكرا كتير لذوقك وشياكتك والاحسن انك تبعد عنه خالص احسنلك

**اراده البشر**

لو كل الناس اللى فى الدنيا عازولك حاجة وربنا رايدلك حاجة تانية حلوه حتى لو الناس اللى يتتأمر عليك وعايزنلك حاجة وحشة شكلهم المتحكمين ظاهريا فى الموضوع و لاحد يقدر يعملك حاجة نهائى لان ربنا مردش. مافيش حد من البشر حتى نهاية الخليقة يقدر يقف قدام مراد الله ومشيئته فسبحانه فوق كل ملك وكل متحكم يوريهم اللى فاكرين انهم اقويا اد ايه هما ضعفاء ويوري الضعفاء اد ايه هو قوي لما يكون متولى عباده ومعينهم. اطمئن اذا كنت مع الملك

**قدراتك**

عمرك ما هتعرف قدراتك ايه غير لما تجربو لو العالم كله معرفش يعمل حاجة معينه متقولش طيب ماهى كل الناس معرفتش, . عادي جدا انتا تقدر والتجربة فعلا هي خير دليل, المثل المبتذل اللى كلنا حافظينه لكن عمره مابييجى على بالنا ولا بنفذه جرب وشوف طالما الحاجة دى مفيدة متغضبش ربنا وعاقبتها حلوه عليك وعلى الناس و ممكن على البشر كلهم يبقى ممكن تنجح . لو كل واحد اخترع حاجة فى الدنيا قال اه ما هو لو كان حد قبلى عملها كنت عملتها, ساعتها مكنش فى حد اخترع حاجة جديده

**رضا ربنا**

لو فى اى حاجة هتعملها هتبعدك عن رضا ربنا يبقى احسن متعملهاش حتى لو انت فاكر ان فيها سعادة, لان فعليا مافيش سعادة او نعمة فى ترك حب اللى بيحبك حقيقى وبتنده عليه يوم ما تضيق بيك وتتزلله قلبك وتقولوا مالى رب سواك

**الدنيا ومشاغلها**

الدنيا ومشاغلها مبتخلصش ومافيش حاجة بتلهى وتبعد الانسان عن سلام قلبه وراحته زى مخالطة الناس كل يوم والمواضيع الكتير والتلصص وعشان كدا لازم تاخد وقت تفصل عن الناس تماما عشان قلبك وراحة بالك والحنيه والود مع ربنا مينتهيش والا هتكون انتا فى طريق وقلبك وعقلك فى طريق تانى

]]>Sincere and heartful new year greetings to the M&S Research Hub team and trainees. Without the contribution, effort, and time of every member of the team, we could not endure and succeed. Without their belief in our vision of “bridging knowledge” and our department mission of “providing top notch live – human interactive- support and training for applied statistics” nothing of the realized success would be possible. We thank every trainee, student, and researcher who trusted our brand and had the confidence that our team will offer the best guidance, support, and training that significantly matters for his/her research and career plans. In 2020 we have

*trained over 1300 researchers at our different training, events, workshops, and webinars (40% more enrollment than the previous year)

*developed and currently contributing to 3 research projects.

* wrote more than 15 useful econometric-related posts on the MSR economics perspective (27% more contribution than the previous year).

* offered 4 fee-waiver scholarships for different training programs.

* launched our institute’s MSR working paper series and the study support service (Our educational counselors currently work with 6 students who plan to start their degree in Germany this year)

**Here we are entering 2021 and wish this year to carry for every one success, blessing, happiness, and joy.**

I would suggest to first do the following steps:

- Scatter your independent variable (in the x-axis) against your dependent variable (in the y-axis)
- Observe what kind of linear and non-linear relationships may exists in the graph.
- Place the mean values of the variables to have some sort of idea of what kind of data concentrations we might have.
- Make your inferences accordingly, and do a matrix with correlations with everything.

To do an example of this, let’s make an example with a Data Generating Process of the form:

And to generate the random sample we will use:

clear all set obs 100 gen n=_n set seed 1234 gen x=rnormal() gen x_sq=x*xgen z=rnormal()gen y= 1 + (0.5*x)+ (- 0.2*x_sq) + (1.5*z)

Now let’s see a summary of our variables.

sum

Which will have as a result

Skipping n, which is just the individual identificatory variable, we can see the mean values of these variables. Now let’s start to play with some scatter plots.

scatter y x

scatter y z

And we will have two graphs that look like this:

First graph, which is the scatter of y and x doesn’t show any clear relationship, in fact, we might state that there’s no relationship by such dispersion, On the second hand, we find out that there’s a possible linear relationship with y and z.

Let’s go and place the means of each variable in the scatter graph, remember that x mean is 0.0078 and y mean is 0.7479, with these values we will have something like this:

scatter y x, xline(.0078032) yline(.747933)

scatter y z, xline(-.0452837) yline(.747933)

According to this, the data appears to be normal distributed (as it should be since we use a random sampling with normal distribution), in other cases, we might find that the mean is allocated in extreme values in either of the axis, which might imply some sort of kurtosis or non-normal distributions.

Now let’s use some linear and non-linear predictions using the not so common lfitci and qfitci. To do this, we type:

twoway (lfitci y x)

twoway (lfitci y z)

And the respective output will be:

If we want to use lines instead of shaded area, we might type

twoway (lfitci y x, ciplot(rline) )

twoway (lfitci y z, ciplot(rline) )

And it will display the same graph, but without shaded areas.

We can extend the same idea with non-linear relationships with a quadratic form using qfitci:

twoway (qfitci y x)

twoway (qfitci y z)

And the output of the graph will be:

Notice that the quadratic relationship is now more visible using the quadratic adjustment for x and y. Therefore, it is a good practice to perform the quadratic adjustment even when the relationship is totally linear like in the case of y and z.

One last type of graphical analysis is using the fractional polynomial, where the syntax is given by:

twoway (fpfitci y x)

twoway (fpfitci y z)

Finally, and to complete the steps we mentioned in this post, let’s do the matrix of correlations. Which is just simply the scatter plots together.

graph matrix y x z

The useful thing to consider with the matrix of correlations is that we can observe not only the scatter plots to a certain variable, but instead we got the scatter plots associated to all the variables we place in the command. Therefore, in regression analysis, this is quite useful to inspect to multicollinearity issues among the independent variables and not only the correlation between the dependent variable.

We can say that similar to x and z, there’s no strong linear correlation since it looks like more like a cloud of dots instead of a linear relationship like it has y and z.

Notice, however, that unless we use a quadratic adjustment, we don’t have it easy to detect the quadratic relationship between y and x, therefore, it is recommended to use the qfitci command to investigate such non-linear relationship.

**Bibliography.**

StataCorp (2020) Graph twoway fpfitci, Recuperated from: https://www.stata.com/manuals13/g-2graphtwowayfpfitci.pdf#g-2graphtwowayfpfitci

]]>The Covid19 pandemic invaded the world like a silent dark shadow. Originating as pneumonia alike disease in the city of Wuhan in China on December 31, 2019, the pandemic has completely paralyzed the world[1]. As of August 15, 2020, the pandemic infected almost 21 million people and caused close to 750,000 deaths worldwide[2]. It is argued that the viral spread has not only affected people’s health, but also the global economy with a rapid economic recession along with severe rising humanitarian crises and social changes[3]. Authors like John Gray, suggest that under this pandemic, globalization will come to end, resulting in more fragmented world[4]. The pandemic also “exposed fatal weaknesses in the economic system that was patched up after the 2008 financial crisis” says John gray. Likewise, the measures adopted or enforced to stop the spread the viral infection has changed the way people live and work, such as working remotely, online learning, pandating (sex, love and dating)[5], tele-health, and home entertainment[6], creating self-centralization, protectionism and strong nationalism, with close borders to contain the viral spread[7]. These abrupt changes imply that globalization is over, and the world is moving towards isolation instead of global cohesion. However, after conducting a secondary research on various aspects of de-globalization and the impact of covid19, it can be concluded that these predictions or changes never implies that globalization is going to die, or shift to small-scale localism, rather, it will reshape its features, scale and magnitude of globalization and societal changes in post-covid world.

We know that Globalization is a growing interdependence, and the recent pandemic shows how this interdependence could be driven relating to economies, culture and populations influenced by flows of people, information, money, media, trade and technology. While the economies globally are affected by the pandemic, with billions of dollars are lost in several industries, unemployment, closing borders and trading, and has an unrest in the society. However, several countries developed the coping mechanism and adopted this change.

Despite being severe in both scale and depth, one fortunate thing is that the pandemic occurred in today’s digital age[8] where we used advanced technologies as artificial intelligence (AI), machine learning, smart sensors, Internet of Things (IoT), mobile and location technologies, virtual and augmented reality (VR & AR), cloud computing, and autonomous systems[9], to mitigate its effects both on economy, society and human lives. While its true that pandemic like the Black Death or 1918 influenza had huge ramifications for the world afterwards, but which of these changes will have a lasting impact on society and ultimately globalization, and whether those previous pandemic ended the notion of “*globalization*”, we never see again, need a closer look.

The first argument lies in dissolving the traditional sources of social cohesion and political legitimacy and replacing them with the promise of rising economics and living standards through globalization and inter-dependencies. Socio-economic analysts like John Gray claiming “*De-globalization*” believe that this experiment has now run its course, as suppressing the virus necessitates an economic shutdown and immobility. However, such prepositions are supported by any ground realities or empirical evidence. Their main argument lies on the single notion of *globally integrated capitalist system marked by relationships of domination between centre and periphery*. But, it is far more complex and not merely an economic and political process, but also a social, cultural, and even environmental phenomenon.We know that, these shutdown can only be temporary, and even many world economies like China, South Korea, Pakistan performed well through “smart lockdown”, with little impact on global supply chain, an essence of globalization. Similarly, many countries begin to open their trading and will start visa services business across Asia and EU.

Second argument suggests that the pandemic will induce inequality, racial discrimination or xenophobia, consequently, collapsing the pluralistic world. As the early counts of deaths in the US showed that coronavirus killed far more black and Latino people in comparison to the percentage of population that they represent – as in New York City, the rate was twice as much for those populations than for white people[10]. Similarly, the rise in xenophobic attacks against Asian immigrants in Europe, Australia, and US also created unrest among nationals and government of these countries. . However, the *“chronological account of the world shows that during 1853, **the yellow-fever epidemic in the United States and European immigrants, who were perceived to be more vulnerable to the disease, were also primary targets of stigmatization”*.* During the SARS outbreak, which originated in China, East Asian bore the brunt. When the Ebola outbreak emerged in 2014, Africans were targeted*”[11]. Hence, it’s not the first time that people from the origin of a disease country are targeted and whether this means people from these countries or the government ended their trade with the US, Europe or Australia?

Even though no one can deny the fact that the COVID-19 thrive the negative effects on the health of minorities (Asian-American, latino and black) and other vulnerable groups, there are reasons for optimism as well. These include the emergence of mechanisms for reporting and tracking incidents of racial bias, increased awareness of racism’s insidious harms and subsequent civic and political engagement by the Asian American community, and building resilience-promoting factors that can reduce the negative health effects of racism. Likewise, in terms of the inequality, all those seem to be examples of immobility rather than changes during pandemic[12]. Indeed, humanity’s past experience with pandemics is telling when it comes to radical change. The record shows that* “inequality can be disrupted with pandemics”*, but only in those cases where it involves “*massive death”*. Such was the case with the fourteenth-century Black Death, which killed as much “*a third of the population of Europe, whereby labour shortage resulting from the decimation of populations ended up doubling or tripling wages for ordinary farmers and craftsmen*”[13]. On other hand, the coronavirus pandemic seems to have prompted many national governments, INGOs and Financial institutions to timidly decide to dedicate larger resources to programs like employment retention, social safety net, care and social protection, making the mental and political shift towards valuing care, protection of indigenous, immigrants and marginalized communities. This is evident from several developed and developing countries such as Pakistan[14], China, South Korea[15], Spain[16] who spend even at much larger than ever in history.

Another argument is based on our recent experiences of the imposition of lockdown as a shock to the world free system- as *people’s freedom or mobility is restricted*, making us feel *lonely *or *listless *or *anxious*. But while “physically distanced”, the internet and social media have allowed us to reach into each other’s homes even more than ever. The estimates from We are Social (2020), showed an *“increase of 7% in the use of the internet with respect to the previous year in 2019 in global terms, meaning 298 million new users, the growth in active users of social networks experienced a growth of 9.2% as compared to the 2019, which implies 321 million more users interacting in the networks*.”[17] In Spain alone, it was calculated that Social media consumption increased by 55% where people were using these media to mingle with Spanish and other nationals[18]. Hence, the Social relationships for many seem not to have suffered. This new normal and social media have also allowed us to explore hobbies and interests we might never have had before – *like the people turning to social media to solve real-life mysteries from their homes***[19]**. However, it is also important to maintain a balance between leveraging the science and technology, social media and protecting people’s privacy as it has been more challenging to trace the Covid-19 cases in the USA than in countries like China or South Korea due to varying nature of different privacy laws.[20]

Similarly, despite a great disruptive and painful, covid19 also invariably nurtures the emergence of great ** common purpose**,

Likewise, the perpetual rise in the incidences of divorce i.e. in China, provoke the notion that it happened only due to the pandemic. However, it wouldn’t be fair to completely blame coronavirus for an increase in divorce rates and relationship issues, as there are several other factors (age, incompatibility during summer breaks, holidays) as Neuropsychologist, Dr. Hafeez revealed that “*many of her clients already knew they had issues in their marriage before COVID-19, and their problems only worsened during lockdown” [22]*. Similarly, University of Washington research considered divorce rates to be

The pandemic perhaps also unlocked an inner creativity and resourcefulness of many communities, as many of us have more time on our hands these days, to start home gardening, Kitchening, allowing people of different nations, sharing foods recipes, books recommendation, and culture, reconnecting us with something that is increasingly lost in hectic modern life – from rebuilding personal relationship with intimate partners and families, to link with other people. In that sense, a vital priority ahead is an upscaling one: *“to extend care from individual bodies to what allows them to persist: relationships, ecosystems, and the biosphere, the whole planet [24]” *that consequently will support the idea of global citizenship and assert changing the features and scale of globalization after pandemic.

The pandemic will also positively shift in expectations and workplace culture, where employees are valued on how well they meet their deliverable targets on time, not how many hours they sit behind their desk in the office. Hence, workers no longer need to remain within commuting distance of the office, but can live wherever most convenient or desirable. And the knock-on effect of this would be “ *residential property values dropping in major cities, and more people moving out into the suburbs or rural areas: a reversal of the trend seen since the beginning of the Industrial Revolution*”[25].

In conclusion, history demonstrates the dynamism and resilience of global connections despite the very catastrophes that globalization provoked. In the light of past experiences, it would be risky to predict deep renunciation of interconnection and interdependence. Since, even in the past, consequences of of such catastrophes disprove the assertions of those who prophesy globalization’s end, and major conflicts had provoked unprecedented global interactions. Furthermore, the COVID-19 pandemic does not mean the end of globalization; it doesn’t even mean the beginning of de-globalization, but neo-globalization, reshaping its nature, at social, political, environmental fronts, countries will continue to adopt, learn from other each: a more global interdependence will continue to be a defining feature of our time.

__End Notes__

^{[1]} Blackburn et al., Digital strategy in a time of crisis. McKinsey Digital, April 22, 2020

^{[2]} Johns Hopkins University & Medicine. *Coronavirus Resource Center*. Retrieved from https://coronavirus.jhu.edu/

^{[3]} Stoll, J.D. Crisis has jump-started America’s innovation engine: What took so long*? Wall Street Journal*, April 10. 2020

^{[4]} John Gray, Why the crises is turning point in history? Published in New States Man, April 1, 2020 https://www.newstatesman.com/international/2020/04/why-crisis-turning-point-history

^{[5]}^{ }The Economist. Pandating: coronavirus and the language of love. Published on July 15 2020 Available on https://www.economist.com/1843/2020/07/15/pandating-coronavirus-and-the-language-of-love

^{[6]}^{ }Lee, S., & Trimi, S. Convergence innovation in the digital age and in the COVID-19 pandemic crisis, *Journal of Business Research*, forthcoming in 2020.

^{[7]} Ibidi

^{[8]} Guy, 2019; Trimi, 2020

^{[9]}^{ }Tonby & Woezel. Could the next normal emerge from Asia? McKinsey & Company, April 8. 2020

^{[10]}^{ }New York Times. Virus Is Twice as Deadly for Black and Latino People Than Whites in N.Y.C. Published on April 8, 2020

Retrieved online from https://www.nytimes.com/2020/04/08/nyregion/coronavirus-race-deaths.html

^{[12]} Christos Zografos. Covid recovery and radical social change. Published online 7^{th} July, 2020. The Ecologist. Available online at https://theecologist.org/2020/jul/07/covid-recovery-and-radical-social-change

^{[13]}^{ }Evans, Richard J. “Epidemics and Revolutions: Cholera in Nineteenth-Century Europe.” Past & Present, no. 120 (1988): 123-46. Accessed November 9, 2020. http://www.jstor.org/stable/650924.

^{[14]}^{ }The News International. Pakistan ranked top in Asia for social protection amid Covid-19. Oct 6, 2020 Retrieved from https://www.thenews.com.pk/print/725452-pakistan-ranked-top-in-asia-for-social-protection-amid-covid-19

^{[16]}^{ }Maria Dalli. The minimum vital income, a question of rights. 2020 retrieved from http://blogs.infolibre.es/alrevesyalderecho/?p=5781

^{[17]}^{ }Hootsuite We are social. Digital 2020. [(accessed on 18 July 2020)];Abril Glob. Statshot Rep. 2020 Retrieved from: https://wearesocial.com/blog/2020/04/digital-around-the-world-in-april-2020.

^{[18]}^{ }Pérez-Escoda, A., Jiménez-Narros, C., Perlado-Lamo-de-Espinosa, M., & Pedrero-Esteban, L. M. (2020). Social Networks’ Engagement During the COVID-19 Pandemic in Spain: Health Media vs. Healthcare Professionals. International journal of environmental research and public health, 17(14), 5261. https://doi.org/10.3390/ijerph17145261

^{[19]}^{ }Frank Swain (2020). BBC Future. The people solving mysteries during lockdown. Available online https://www.bbc.com/future/article/20200612-how-to-help-the-world-during-lockdown

^{[20]}^{ }Chen, Z. COVID-19: A revelation – A reply to Ian Mitroff. Technological Forecasting & Social Change, 156, 1-2. 2020

^{[21]}^{ }COVID-19: New awareness of social cohesion growing in the crisis. Available online https://www.lutheranworld.org/news/covid-19-new-awareness-social-cohesion-growing-crisis

^{[22]}^{ }Spectrum News New York. Has the Coronavirus Pandemic Created a Spike in Divorces? Available online at https://www.ny1.com/nyc/all-boroughs/news/2020/06/27/has-the-coronavirus-pandemic-created-a-spike-divorces-

^{[23]}^{ }University of Washington. Is Divorce Seasonal? UW Research Shows Biannual Spike in Divorce Filings. Published online 16 August, 2016. Available online https://www.newswise.com/articles/is-divorce-seasonal-uw-research-shows-biannual-spike-in-divorce-filings

^{[24]}^{ }Nonunadimeno. La Vita Oltre La Pandemia. 2020 available on https://nonunadimeno.wordpress.com/2020/04/28/la-vita-oltre-la-pandemia/

^{[25]}^{ }Ibidi

**Bibliography**

Blackburn et al., Digital strategy in a time of crisis. McKinsey Digital, April 22, 2020

Chen, Z. COVID-19: A revelation – A reply to Ian Mitroff. Technological Forecasting & Social Change, 156, 1-2. 2020

Christos Zografos. Covid recovery and radical social change. Published online 7^{th} July, 2020. The Ecologist. Available online at https://theecologist.org/2020/jul/07/covid-recovery-and-radical-social-change

COVID-19: New awareness of social cohesion growing in the crisis. Available online https://www.lutheranworld.org/news/covid-19-new-awareness-social-cohesion-growing-crisis

Evans, Richard J. “Epidemics and Revolutions: Cholera in Nineteenth-Century Europe.” Past & Present, no. 120 (1988): 123-46. Accessed November 9, 2020. http://www.jstor.org/stable/650924.

Frank Swain (2020). BBC Future. The people solving mysteries during lockdown. Available online https://www.bbc.com/future/article/20200612-how-to-help-the-world-during-lockdown

Guy, J. S. Digital technology, digital culture and the metric/nonmetric distinction. Technology Forecasting & Social Change, 145, 55-61. 2019.

Hootsuite We are social. Digital 2020. [(accessed on 18 July 2020)];Abril Glob. Statshot Rep. 2020 Retrieved from: https://wearesocial.com/blog/2020/04/digital-around-the-world-in-april-2020.

John Gray, Why is the crisis a turning point in history? Published in New States Man, April 1, 2020 https://www.newstatesman.com/international/2020/04/why-crisis-turning-point-history

Johns Hopkins University & Medicine. *Coronavirus Resource Center*. Retrieved from https://coronavirus.jhu.edu/

Lee, S., & Trimi, S. Convergence innovation in the digital age and in the COVID-19 pandemic crisis, *Journal of Business Research*, forthcoming in 2020.

Maria Dalli. The minimum vital income, a question of rights. 2020 retrieved from http://blogs.infolibre.es/alrevesyalderecho/?p=5781

Mauro Testaverde. Social protection for migrants during the COVID-19 crisis: The right and smart choice. APRIL 28, 2020. Available online https://blogs.worldbank.org/voices/social-protection-migrants-during-covid-19-crisis-right-and-smart-choice

New York Times. Virus Is Twice as Deadly for Black and Latino People Than Whites in N.Y.C. Published on April 8, 2020. Retrieved online from https://www.nytimes.com/2020/04/08/nyregion/coronavirus-race-deaths.html

Nonunadimeno. La Vita Oltre La Pandemia. 2020 available on https://nonunadimeno.wordpress.com/2020/04/28/la-vita-oltre-la-pandemia/

Pérez-Escoda, A., Jiménez-Narros, C., Perlado-Lamo-de-Espinosa, M., & Pedrero-Esteban, L. M. Social Networks’ Engagement During the COVID-19 Pandemic in Spain: Health Media vs. Healthcare Professionals. International journal of environmental research and public health, 17(14), 5261. 2020 https://doi.org/10.3390/ijerph17145261

Spectrum News New York. Has the Coronavirus Pandemic Created a Spike in Divorces? Available online at https://www.ny1.com/nyc/all-boroughs/news/2020/06/27/has-the-coronavirus-pandemic-created-a-spike-divorces-

Stoll, J.D. Crisis has jump-started America’s innovation engine: What took so long*? Wall Street Journal*, April 10. 2020

The Economist. Pandating: coronavirus and the language of love. Published on July 15 2020 Available on https://www.economist.com/1843/2020/07/15/pandating-coronavirus-and-the-language-of-love

The News International. Pakistan ranked top in Asia for social protection amid Covid-19. Oct 6, 2020 Retrieved from https://www.thenews.com.pk/print/725452-pakistan-ranked-top-in-asia-for-social-protection-amid-covid-19

Tonby & Woezel. Could the next normal emerge from Asia? McKinsey & Company, April 8. 2020

Trimi, S. Technology, innovation, and the COVID-19 pandemic, Decision Line, 51(3), 32-37. 2020

University of Washington. Is Divorce Seasonal? UW Research Shows Biannual Spike in Divorce Filings. Published online 16 August, 2016. Available online https://www.newswise.com/articles/is-divorce-seasonal-uw-research-shows-biannual-spike-in-divorce-filings

]]>The last equation presents the dependent variable Y as a function of X however, we can see that the polynomial in the model is of second-order degree. A few mentions can be done from here: 1) the model still linear in the parameters β. 2) No multicollinearity can be argued to exists between the regressors in X and the square of X (the model itself in terms of X will be highly correlated) therefore we’re modeling a structure where both of them will move together. 3) The parameters will no longer have a static/basic marginal effect, to find out this marginal effect we need to calculate the derivate of the model, given by:

Which represents that when X increase in one unit, the change in y is the above expression.

Considering the derivate, a turning point is given in the effect of X to Y, and can be found when we equal this derivate to 0 (to find the numerical spot where the slope is equal to 0). And that is done by solving the equation for the value of X:

We clear X and we have:

Let’s see this in practice, first let’s formulate a Data Generating Process -DGP- as follows without any noise or error:

Where X~N(0,1), with Stata let’s generate some random observations and the square variable.

clear all ** Setting observations set obs 50 gen n=_n set seed 1234 gen x=rnormal() gen x_sq=x*xgen y= 1 + (0.5*x)+ (- 0.2*x_sq)

After that, let’s scatter y, over x. and using scatter y x we have the next graph:

If we regress this functional form with the next command:

regress y x x_sq

We have the regression totally adjusted to the DGP. But with missing values on lots of statistics (since there is no residual at all!).

Notice also that the linear adjustment for r-squared is 1, meaning it is matching the data perfectly.

Now confirming that coefficients are 0.5, -0.2 and 1 for the constant. Let’s confirm that the turning point of the model is in:

Solving and changing the parameter’s we have that:

The slope of the curve where it turns to be 0 it should be allocated in X=1.25, with an image in Y=1+0.5(1.25)-0.2(1.25^2)= 1.3125 after that, there’s a decreasing effect in Y given changes in X.

Let’s redo the graph but marking those points.

scatter y x, yline(1.3125) xline(1.25)

We allocated the exact point where the input of x variable is enough to create a decreasing effect on the dependent variable (specifically at x=1.25, y=1.3525) and moving to x>1.25 we have decreasing effects on y, where areas before this point it was positive.

Within this context, let’s introduce to curvefit command.

This package created by Liu wei (2010) and it is good to investigate this kind of nonlinearities, let’s look it in action.

curvefit y x, function(1)

By placing the variables of interest (y as dependent and x as an independent), we need to specify the behavior of the polynomial, as the examples show, function(1) equals a first-order polynomial (a single straight line equation). With the following output.

As you can see, it gives estimates of the coefficients (b0 as the constant with b1 as the slope) and the basic statistic of the number of observations (N) and the adjusted r-squared. The graph displayed is:

Which is a linear model. A simple regression with first-order power in X. let’s try another function (the quadratic function). We type:

curvefit y x, function(4)

Which gives the following output:

Where b0 is the constant parameter, b1 would equal to the X without any power, and finally, b2 is the parameter associated with X^2. Giving an R^2 adjusted of 1, represents the goodness fit of the model of 100%. With the associated graph:

As you can see, the curve provides estimates pretty decent of the structure of the data given different types of mathematical models.

Here’s the complete list of what kind of functions it can be modeled.

function(string) The following are alternative Models correspond with the values of the sting: . string = 1 Linear: Y = b0 + (b1 * X) . string = 2 Logarithmic: Y = b0 + (b1 * ln(X)) . string = 3 Inverse: Y = b0 + (b1 / X) . string = 4 Quadratic: Y = b0 + (b1 * X) + (b2 * X^2) . string = 5 Cubic: Y = b0 + (b1 * X) + (b2 * X^2) + (b3 * X^3) . string = 6 Power: Y = b0 * (X^b1) OR ln(Y) = ln(b0) + (b1 * ln(X)) . string = 7 Compound: Y = b0 * (b1^X) OR ln(Y) = ln(b0) + (ln(b1) * X) . string = 8 S-curve: Y = e^(b0 + (b1/X)) OR ln(Y) = b0 + (b1/X) . string = 9 Logistic: Y = b0 / (1 + b1 * e^(-b2 * X)) . string = 0 Growth: Y = e^(b0 + (b1 * X)) OR ln(Y) = b0 + (b1 * X) . string = a Exponential: Y = b0 * (e^(b1 * X)) OR ln(Y) = ln(b0) + (b1 * X) . string = b Vapor Pressure: Y = e^(b0 + b1/X + b2 * ln(X)) . string = c Reciprocal Logarithmic: Y = 1 / (b0 + (b1 * ln(X))) . string = d Modified Power: Y = b0 * b1^(X) . string = e Shifted Power: Y = b0 * (X - b1)^b2 . string = f Geometric: Y = b0 * X^(b1 * X) . string = g Modified Geometric: Y = b0 * X^(b1/X) . string = h nth order Polynomial: Y = b0 + b1X + b2X^2 + b3X^3 + b4X^4 + b5*X^5 … . string = i Hoerl: Y = b0 * (b1^X) * (X^b2) . string = j Modified Hoerl: Y = b0 * b1^(1/X) * (X^b2) . string = k Reciprocal: Y = 1 / (b0 + b1 * X) . string = l Reciprocal Quadratic: Y = 1 / (b0 + b1 * X + b2 * X^2) . string = m Bleasdale: Y = (b0 + b1 * X)^(-1 / b2) . string = n Harris: Y = 1 / (b0 + b1 * X^b2) . string = o Exponential Association: Y = b0 * (1 - e^(-b1 * X)) . string = p Three-Parameter Exponential Association: Y = b0 * (b1 - e^(-b2 * X)) . string = q Saturation-Growth Rate: Y = b0 * X/(b1 + X) . string = r Gompertz Relation: Y = b0 * e^(-e^(b1 - b2 * X)) . string = s Richards: Y = b0 / (1 + e^(b1 - b2 * X))^(1/b3) . string = t MMF: Y = (b0 * b1+b2 * X^b3)/(b1 + X^b3) . string = u Weibull: Y = b0 - b1*e^(-b2 * X^b3) . string = v Sinusoidal: Y = b0+b1 * b2 * cos(b2 * X + b3) . string = w Gaussian: Y = b0 * e^((-(b1 - X)^2)/(2 * b2^2)) . string = x Heat Capacity: Y = b0 + b1 * X + b2/X^2 . string = y Rational: Y = (b0 + b1 * X)/(1 + b2 * X + b3 * X^2) . string = ALL refers to a total of above models (Attention: it's uppercase!) nograph Curve Estimation without curve fit graph.

This package can be installed using:

ssc install curvefit, replace.

**Bibliography.**

Liu Wei (2010) “**CURVEFIT: Stata module to produces curve estimation regression statistics and related plots between two variables for alternative curve estimation regression models**,” Statistical Software Components S457136, Boston College Department of Economics, revised 28 Jul 2013.

التعليم فى المانيا ببلاش لكل مراحل التعليم حتى الدكتوراه, و منح كتير جدا عشان متصرفش مليم من جيبك و فرص شغل وخبره اذا قررت تستقر هناك او رجعت بلدك….

الموضوع شكله سهل, بس هوا مش سهل, بس احنا ان شاء الله هنخليه سهل…

احنا شركة بحثية واكاديمية فى المانيا و من شهر اعلنا عن خدمة دعم طلابى, لكل المراحل الجامعية, بكالريوس, ماجستير و دكتوراه فى كل المجالات عشان نساعدهم يقدموا على الجامعات و المنح وياخدوا فرصة حقيقية لتغيير و تعليم افضل. الخدمة مش مجانية بس مصاريفها مخفضة بشكل كبير خاصة للطلبة من الدول النامية.

فى اخر سنة فى ثانوي, دة الوقت المناسب عشان تجهز ورقك للبكالريوس …لو اخر سنة كلية, ده الوقت المناسب عشان تقدم على الدراسات العليا

…بتفكر تسافر امريكا او بريطانيا…ليه تدفع الالاف فى الدراسة لما ممكن تتعلم ببلاش فى المانيا و فى افضل جامعات العالم

الموضوع محتاج وقت ومجهود

املا الاستمارة على موقنا وهنتواصل معاك نوضحلك الخطوات اللى جاية

https://ms-researchhub.com/home/study_support.html

ولو فعلا مش قادر تدفع مصاريف الخدمة ونفسك تبذل وقت ومجهود عشان تحقق هدفك, املا الاستمارة و ممكن نخفض ليك المصاريف اكتر او نشيلها خالص

وفى علمكم فرصة الدراسة فى المانيا مجانا بقت محدوده, لان بدأت جامعات تفرض رسوم دراسية عالية على الطلبة خارج الاتحاد الاوروبي و بدا النظام ده فى اكبر 8 جامعات فى المانيا و هيستمر حتى يتطبق على كل الجامعات

والمقال عن الموضوع ده

https://www.studying-in-germany.org/germany-will-reintroduce-tuition-fees-non-eu-students/

متضيعش الفرصة وابدا النهارده قبل بكره

]]>Big brands have changed their logos to rainbow flags in support of gay and minority rights for a whole month. With the public projections of Hebdo caricatures of the Islam prophet Mohamed on a local government building, while heavily armed police officers stood guard. We – the international and multireligious team of M&S Research Hub – stand against anything that can hurt the convictions of someone else, in particular religious convictions. Accordingly and for a whole month, our logo will change and include the name of the prophet Mohamed in Arabic letters.

we actually took this stand in support for freedom of speech by any mean and any message, yet it is not a license to abuse or humiliates other believes rather it is a responsibility message. We would actually take this stand if the projected drawings were mocking of Jesus or Budha.

When I mock you in front of people in a humiliating way, you would not think of it as my freedom of speech, rather it is an issue of my manners and attitude, instead, it would be better to say what I think, in the right time and the right way, otherwise, the point of my message would be completely twisted because of the wrong attitude. Eventually and briefly, hate speech is never and has never been a freedom of speech, when a speech or message cost people lives, create tensions and societal isolations and divisions, then it is simply wrong.

]]>Hence, we have to use approximations to the non-linear models. We have to make concessions in this, as some features of the models are lost, but the models become more manageable.

In the simplest terms, we first take the natural logs of the non-linear equations and then we linearise the logged difference equations about the steady state. Finally, we simplify the equations until we have linear equations where the variables are percentage deviations from the steady state. We use the steady state as that is the point where the economy ends up in the absence of future shocks.

Usually in the literature, the main part of estimation consisted of linearised models, but after the global financial crisis, more and more non-linear models are being used. Many discrete time dynamic economic problems require the use of log-linearisation.

There are several ways to do log-linearisation. Some examples of which, have been provided in the bibliography below.

One of the main methods is the application of Taylor Series expansion. Taylor’s theorem tells us that the first-order approximation of any arbitrary function is as below.

We can use this to log-linearise equations around the steady state. Since we would be log-linearising around the steady state, x* would be the steady state.

For example, let us consider a Cobb-Douglas production function and then take a log of the function.

The next step would be to apply Taylor Series Expansion and take the first order approximation.

Since we know that

Those parts of the function will cancel out. We are left with –

For notational ease, we define these terms as percentage deviation of x about x* where x* signifies the steady state.

Thus, we get

At last, we have log-linearised the Cobb-Douglas production function around the steady state.**Bibliography:**

Sims, Eric (2011). Graduate Macro Theory II: Notes on Log-Linearization – 2011. Retrieved from https://www3.nd.edu/~esims1/log_linearization_sp12.pdf

Zietz, Joachim (2006). Log-Linearizing Around the Steady State: A Guide with Examples. SSRN Electronic Journal. 10.2139/ssrn.951753.

McCandless, George (2008). The ABCs of RBCs: An Introduction to Dynamic Macroeconomic Models, Harvard University Press

Uhlig, Harald (1999). A Toolkit for Analyzing Nonlinear Dynamic Stochastic Models Easily, Computational Methods for the Study of Dynamic

Economies, Oxford University Press

The approach is used to test first-order serial correlation, the general form of the test is given the statistic as:

Where the statistic of Box- Pierce Q is defined as the product between the number of observations and the sum of the square autocorrelation ρ in the sample at lag h. The test is closely related to the Ljung & Box (1978) autocorrelation test, and it used to determine the existence of serial correlation in the time series analysis. The test works with chi-square distribution by the way.

The null hypothesis of this test can be defined as H0: Data is distributed independently, against the alternative hypothesis of H1: Data is not distributed independently. Therefore, the null hypothesis is that data is not suffering from an autocorrelation structure against the alternative which proposes that the data has an autocorrelation structure.

The test was implemented in Stata with the panel data structure by Emad Abd Elmessih Shehata & Sahra Khaleel A. Mickaiel (2004), the test works in the context of ordinary least squares panel data regression (the pooled OLS model). And we will develop an example here.

First we install the package using the command ssc install as follows:

ssc install lmabpxt, replace

Then we will type help options.

help lmabpxt

From that we got the next result displayed.

We can notice that the sintax of the general form is:

lmabpxt depvar indepvars [if] [in] [weight] , id(var) it(var) [noconstant coll ]

In this case id(var) and it(var) represents the identificatory of individuals (id) and identificatory of the time structure (it), so we need to place them in the model.

Consider the next example

clear all

use http://www.stata-press.com/data/r9/airacc.dta

xtset airline time,y

reg pmiles inprog

lmabpxt pmiles inprog, id(airline) it(time)

Notice that the Box-Pierce test implemented by Emad Abd Elmessih Shehata & Sahra Khaleel A. Mickaiel (2004) will re-estimate the pooled regression. And the general output would display this:

In this case, we can see a p-value associated to the Lagrange multiplier test of the box-pierce test, and such p-value is around 0.96, therefore, with a 5% level of significance, we cannot reject the null hypothesis, which is the No AR(1) panel autocorrelation in the residuals.

Consider now, that you might be using fixed effects approach. A numerical approach would be to include dummy variables (in the context of least squares dummy variables) of the individuals (airlines in this case) and then compare the results.

To do that we can use:

tab airlines, gen(a)

and then include from a2 to a20 in the regression structure, with the following code:

lmabpxt pmiles inprog a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 , id(airline) it(time)

This would be different from the error component structure, and it would be just a fixed effects approach using least squares dummy variable regression. Notice the output.

Using the fixed effects approach with dummy variables, the p-value has decreased significantly, in this case, we reject the null hypothesis at a 5% level of significance, meaning that we might have a problem of first-order serial correlation in the panel data.

With this example, we have done the Box-Price test for panel data (and additionally, we established that it’s sensitive to the fixed effects in the regression structure).

N*otes:*

*The lmabpxt appears to be somewhat sensitive if the number of observations is too large (bigger than 5000 units).*

*There are an incredible compilation and contributions made by Shehata, Emad Abd Elmessih & Sahra Khaleel A. Mickaiel which can be found in the next link:*

http://www.haghish.com/statistics/stata-blog/stata-programming/ssc_stata_package_list.php

I su*ggest you to check it out if you need anything related to Stata.*

B**ibliography**

Box, G. E. P. and Pierce, D. A. (1970) “Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models”, Journal of the American Statistical Association, 65: 1509–1526. JSTOR 2284333

G. M. Ljung; G. E. P. Box (1978). “On a Measure of a Lack of Fit in Time Series Models”. Biometrika 65 (2): 297-303. doi:10.1093/biomet/65.2.297.

Shehata, Emad Abd Elmessih & Sahra Khaleel A. Mickaiel (2014) LMABPXT: “Stata Module to Compute Panel Data Autocorrelation Box-Pierce Test”

]]>Assume a basic fitted model given by:

Where **y** is the vector of containing the dependent variable with *nx1* observations, **X** is the matrix that contains the explanatory variables which is *nxk* (n are the total observations and k are the number of independent variables). The vector **b **represents the estimated coefficient vector.

Ramsey test fits a regression model of the type

Where **z** represents the powers of the fitted values of **y**, the Ramsey test performs a standard F test of **t=**0 and the default setting is considering the powers as:

In Stata this is easily done with the command

estat ovtest

after the regression command reg.

To illustrate this, consider the following code:

use https://www.stata-press.com/data/r16/auto regress mpg weight foreign estat ovtest

The null hypothesis is that **t=0** so it means that the powers of the fitted values have no relationship which serves to explain the dependent variable **y**, meaning that the model has no omitted variables. The alternative hypothesis is that the model is suffering from an omitted variable problem.

In the panel data structure where we have multiple time series data points and multiple observations for each time point, in this case we fit a model like:

With i=1, 2, 3, …, n observations, and for each i, we have t=1, 2, …, T time periods of time. And ** v** represents the heterogenous effect which can be estimated as parameter (in fixed effects: which can be correlated to the explanatory variables) and as variable (in random effects which is not correlated with the explanatory variables).

To implement the Ramsey test manually in this regression structure in Stata, we will follow Santos Silva (2016) recommendation, and we will start predicting the fitted values of the regression (with the heterogenous effects too!). Then we will generate the powers of the fitted values and include them in the regression in (4) with clustered standard errors. Finally, we will perform a significant test jointly for the coefficients of the powers.

use https://www.stata-press.com/data/r16/nlswork xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south, fe cluster(idcode) predict y_hat,xbu gen y_h_2=y_haty_hat gen y_h_3=y_h_2y_hat gen y_h_4=y_h_3*y_hat xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south y_h_2 y_h_3 y_h_4, fe cluster (idcode) test y_h_2 y_h_3 y_h_4

Alternative you can skip the generation of the powers and apply them directly using c. and # operators in the command as it follows this other code:

use https://www.stata-press.com/data/r16/nlswork xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south, fe cluster(idcode) predict y_hat,xbu xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenure#c.tenure 2.race not_smsa south c.y_hat#c.y_hat c.y_hat#c.y_hat# c.y_hat c.y_hat#c.y_hat# c.y_hat# c.y_hat , fe cluster (idcode) test c.y_hat#c.y_hat c.y_hat#c.y_hat# c.y_hat c.y_hat#c.y_hat# c.y_hat# c.y_hat

At the end of the procedure you will have this result.

Where the null hypothesis is that the model is correctly specified and has no omitted variables, however in this case, we reject the null hypothesis with a 5% level of significance, meaning that our model has omitted variables.

As an alternative but somewhat more restricted, also with more features, you can use the user-written package “resetxt” developed by Emad Abd & Sahra Khaleel (2015) which can be used after installing it with:

ssc install resetxt, replace

This package however doesn’t work with factor-variables or time series operators, so we cannot include c. or i. and d. or L. operators for example.

clear all use https://www.stata-press.com/data/r16/nlswork gen age_sq=ageage gen ttl_sq= ttl_expttl_exp gen tenure_sq= tenure* tenure xtreg ln_w grade age age_sq ttl_exp ttl_sq tenure tenure_sq race not_smsa south, fe cluster(idcode) resetxt ln_w grade age age_sq ttl_exp ttl_sq tenure tenure_sq race not_smsa south, model(xtfe) id(idcode) it(year)

however, the above code might be complicated to calculate in Stata, depending on how much memory do you have to do the procedure. That’s why in this post it was implemented the manual procedure of the Ramsey test in the panel data structure.

**Bibliography**

Emad Abd, S. E., & Sahra Khaleel, A. M. (2015). *RESETXT: Stata Module to Compute Panel Data REgression Specification Error Tests (RESET).* Obtained from: Statistical Software Components S458101: https://ideas.repec.org/c/boc/bocode/s458101.html

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. *Journal of the Royal Statistical Society Series B 31*, 350–371.

Santos Silva, J. (2016). *Reset test after xtreg & xi:reg .* Obtained from: The Stata Forum: https://www.statalist.org/forums/forum/general-stata-discussion/general/1327362-reset-test-after-xtreg-xi-reg?fbclid=IwAR1vdUDn592W6rhsVdyqN2vqFKQgaYvGvJb0L2idZlG8wOYsr-eb8JFRsiA