Barabas Role in the Jew of Malta Essay Example for Free

Barabas Role in the Jew of Malta Essay Christopher Marlow was born in 1564, as William Shakespeare. This play was probably written in 1589; however, it was not actually published until 1633, after Marlowes death in 1593 when he was just 29 years old. This play was performed for many years and had a great influence on Shakespeare’s The Venice Merchant. †¢1. Summary of the play The play is set on the island of Malta in the Mediterranean Sea. Calymath (the Turkish prince) arrives to exact Maltas tribute which has been accumulated to a considerable sum. Ferneze (Maltese governor) cannot pay the tribute immediately, but he promises to pay within a month. After the Turks leave, Ferneze decides to collect the needed money from the Jews of Malta: each Jew must give up half of his fortune. Barabas complains strongly, so his full fortune is confiscated. The Jew tries to keep part of his fortune which was hided in his mansion. Having confessed falsely, Abigail was admitted in the nunnery (formerly Barabas mansion) and recovered her fathers hidden fortune. Meanwhile, the Spanish Martin Del Bosco convinces Ferneze to break Maltas agreement with Turkey, promising to write the Spanish king for military help. Del Bosco also sells Ferneze his slaves, and Barabas ends up buying the Turkish slave Ithamore at the marketplace. At the marketplace, Barabas also runs into Mathias and Lodowick. Each young man desires to see Abigail, and Barabas promises his favours to each, but at the same time, Barabas is planning their death helped by Ithamore. Broken by his father’s selfishness and the death of her lover Mathias, Abigail on her own decides to enter the nunnery once again. Barabas, afraid that Abigail will betray him, poisons all the nuns included her own daughter Abigail who is the last to die. Before this, she manages to give friar Barnardino a written confession of her fathers crimes. Barnardino in companion with the friar Jacomo get to face Barabas and insinuate they know about the Jews crimes. In response, Barabas says that he would like to repent and become a Christian. Naturally, he will donate his huge fortune to whichever monastery he enters. The two friars, being from different monasteries, fight to win Barabas favour, each hoping to benefit from the Jew’s considerable fortune. Barabas once again has set a trap; he will kill both of the friars without arousing suspicion. Ithamore knows plenty of incriminating information. Once he is seduced by the courtesan Bellamira, Ithamore begins to blackmail Barabas with threats to confess if the Jew does not send him gold. In the last scene of the fourth act, Barabas arrives at Bellamiras house in the disguise of a French musician and poisons his blackmailers. Meanwhile, the Turkish Bashaws have arrived. In response to Fernezes refusal to pay, they declare war on Malta. In the final act, Ferneze prepares to defend Malta against the Turks. Ithamore, Bellamira, and her attendant Pilia Borza enter and all play their parts in revealing Barabas crimes, but the Jews poison takes effect and they all fall dead. Barabas meanwhile has been captured, but he pretends he is dead through the effect of a drug. He finds himself left outside the city walls. The Jew betrays Malta and leads the Turks into the city. He takes position as governor but he decides to return Malta to help Ferneze to massacre the Turkish forces. The Turkish troops also believed the Jews trick. But Ferneze turns the tables on Barabas at the last moment, and Barabas dies. Ferneze takes Calymath as a prisoner in order to ensure Maltas future safety. †¢2. About Barabas Barabas in the Jew of Malta is an extremely revengeful and ambitious character. He challenges the power with a great cunning. The accumulated tributes, Malta has to pay to the Turks, are more than this country can afford, that is why the governor of Malta is determined to ally to the Catholic Spain if this huge European power keep at bay to the Turks. Spain would take advantage of the sales of Turkish slaves in Malta and many other advantages in business. Malta wouldn’t have to pay the tribute to Turkey and could keep the money collected among its Jew population. This selfishness characterizes all the agreements between the Mediterranean governments. The word that designates these actions is â€Å"politics† and the Jew, Barabas, perceives this selfishness is the ruler’s main principle: â€Å"I, policie? That’s their profession, /and not simplicity as their suggest. † Besides, the rulers speak frankly about this, as we can see when Del Bosco is asked â€Å"what wind drives you in thus into Malta Rhode? And one of his Bashaws answered: â€Å"the wind that bloweth all the world besides, /desires of gold. † In this world in which each nation an d each man take care only of their own self-interest, the Jew of Malta appears at the beginning of the play as victim. Ferneze states Malta as the unique priority and states this:† to save the ruine of a multitude: /and better one want for a common good, then many perish for a private man†. But actually, their taxes on the Jews are hugely unfair. Moreover, Farneze, expect to keep the confiscated fortunes, once the alliance with Spain lets Malta to avoid the tributes that owes to the Turks. These unfair circumstances give Barabas the opportunity to create eloquent speeches against intolerance. He reproaches the Christians for using the scriptures to confirm the measures which go against the Jews: â€Å"What? Bring your scripture to confirm your wrongs? / Preach me not out of my possessions. /some Iewes are wicked, as all Christians are: / but say the tribe I descended of were all in general cast away for sinne, / shall I be tried by their transgression? / the man that dealeth righteously shall lieu: /and which of your can charge me otherwise? † The references to the bible in this extract emphasize how piteous he shows himself in this moment. Barabas is right when he calls â€Å"theft† and not â€Å"taxes† to the requisition of his wealth, and we cannot avoid feeling affected by his sad situation. The funny thing is that, as a Marlowe’s dramatic and moral strategy, in the prologue Barabas has been presented as the same Machiavelli and the Devil’s son, and Machiavelli in the prologue states this: †I count religion but a childish toy, /And hold there is no sinne but Ignorance†. At the very beginning, Barabas is shown as a unbelievable wealthy man and extremely shrewd and interested just in his own contentment. He is determined to let the Turks to invade Malta and slaughter everyone, he confesses in a soliloquy, if he would have the opportunity to get away with the situation. † I’le helpe to slay their children and their wiues, /to fire the churches, pull their houses downe. /take my goods too, and seize upon my lands. † He is completely decided to cheat on the others Jews; he also turns his back on his daughter when she abandons her loyalty to him. Later on we realize that his former speech about the sad situation of the Jews is just a theatrical trick created for the situation and refused in his soliloquies, he is a Jew because he was brought up as a Jew, but he is mainly a Maquiavelli and an immoral figure of vice. This vicious identity is clearer and clearer along the play, thus the Jew of Malta is developed more by disclosure of character than by change of personality. Barabas does not change but we progressively discover how he really is. Maybe the persecution ordered by Ferneze wakes in Barabas a desire of revenge, but he has always hated everyone and has always looked for his own benefit and survival using any means. His plan for kidnapping to her daughter and recovering his money hidden in his house, at that moment turned into a nunnery, results comprehensible and in fact Abigail shows herself decided to help him. However, when Barabas ignores Abigail happiness conspiring against her Christian lover Ludowick, just because he is the governor’s son and against Mathias, uses several strategies as the usury, extortion and persuasion which makes him an evil person even before the unfair tax of Farneze. Barabas boasts of his acts as we can read in the following line â€Å"Slew friend and enemy with my stratagems. † He considers Ithamore as one of his friends because: â€Å"why this is something: make account of me/ as of thy fellow; we are villainies both: Both circumcised, we hate Christian both† Here the dichotomy of motivation and unmotivated evil (a Samuel Tylor Coleridge’s expression) is evident in this combination of Judaism and pure evilness. Barabas’ vicious evilness is more and more present in his behaviour. Instead of sad laments, we can hear the satisfied laughter of Barabas who wants to solve skilfully all his plans. Abigail, who finds herself forgotten and rejected by her father; embraces Christian faith as she states â€Å"but I perceive there is no love on earth/ pitty in Iews, nor piety in Turkes. † As a punishment Barabas poisons every nun in the nunnery included her daughter. Barabas also cheats on the friar community taking advantage of their corruptness Barabas is a hypocrisy and disguise master, and he is surrounded by a group of thugs and courtesans that turn against him as the same time that he turns against them. His achievements in conspiracy and politics drives him to rule Malta, making agreements firstly with the Turks and then with Farneze. Brabas’ evilness is more persistent than even his own life as he lets us know: â€Å"Stand close, for here they come: why, is not this/ a kingly kinde of trade of purchase Townes/ by treachery, and sell ‘em by deceit? /Now tell me, worldlings, underneath the sunne, / If greater falsehood ever has bin doneâ€Å". Even in the moment of his death, when he is finally betrayed by Ferneze, he yearns for longing his wealth and domination and contemplating his Empire once more as we also saw in Faustus. †and had I but scap’d this stratagem, /I would have brought confusion on you all, / Damn Christians, dogges, and Turkish Infidels. † It is interesting how Marlowe gets Brabas’ huge ambition wakes in the readers a great admiration. There is no doubt that Barabas received a severe punishment when, at the end, he falls inside a caldron made by himself; he fell in his own trap and died shouting boastings and challenges. Anyway, this is an appropriate punishment for a life full of crimes. However, it is difficult to contemplate his end from an instructive and moral point of view because, Ferneze, his nemesis, is neither seen as virtuous character. Although he wants to look pious, (â€Å"No, Barabas, to staine our hands with blood / is farre from us and our profession†) he believes in his own policy, which has overcome Barabas evilness. He defeats Barabas by betraying him and then attributes his victory to God. This is an act typical of Maquiavelli’s disciple, who assigns the highest value to the State survival and uses religion as a mean for shaping the public opinion. If Farneze is an important figure in this play, is not because of his Christian virtue but because of his Maquiavellic virtue Maybe, Marlowe is inviting us to admire this shrewd governor whose policy ensures Malta’s survival and Barabas’ destruction. Marlowe destroys Barabas just for showing the strength of a really Maquiavellic strategist. Marlowe presents to his Elizabethan audiences a proposal which completely disagrees with any religious doctrine.

Japanese Pornographic Animation Essay Example for Free

Japanese Pornographic Animation Essay Susan J. Napier’s â€Å"The Frenzy of Metamorphosis: The Body in Japanese Pornographic Animation† describes how both female and male bodies are depicted in Japanese animated pornography. Napier explains how male dominance over women is portrayed in the different animated films she has cited, where gender-specific roles are usually restored in the end of each film or series. For example, the female lead character in the animated film Wicked City is portrayed as a better warrior than the male lead character, but in the end she assumes the traditional role of damsel-in-distress and mother of the child that would bring peace to their city and its parallel, the Black World. Napier further highlights the often problematic and complicated depiction of male-female relationships in these films. The fantasy behind every transformation a male or female character’s body undergoes in the animated film subliminally illustrates underlying cultural backgrounds, as well as frustrations, of the Japanese. In these films, the female characters are commonly fantastically proportioned with massive breasts and hips, and tiny waists, while the male characters are similarly fantastically sexually endowed and lustful. Such depictions appear to reflect heavy Western influences. The metamorphosis of female bodies is described by Napier as â€Å"controlled† in that they are rather reflective of traditional roles and perceptions towards females in Japanese culture. Meanwhile, the metamorphosis of male bodies is described as more of â€Å"demonic dominance and comic frustration. † While they are subversive to the patriarchal culture of Japan, male bodies are portrayed in the opposite of how female bodies are portrayed. Male bodies are portrayed as either grotesquely demonic-looking or ridiculously child-like. Napier maintains that males do not always come out as the dominant character in Japanese animated pornography. I find this rather arguable as most if not all of the examples highlighted in the reading suggest the presence of a male figure whose role is either to dominate the female character or to support it. Culturally, Japanese are of control-minded and patriarchal culture where every female is traditionally submissive to a male partner. The presence of a male character, no matter how comic or demonic as depicted in the film, can be attributed to an attempt to balance out the portrayal of powers where the protagonist is a female; hence, whether the dominant role belongs to the male or female remains problematic. The presence of the male support character suggests that female characters cannot achieve great power over her adversary without him. An example of this is La Blue Girl where the lead character, a female ninja, is supported by her male ninja sidekick who is portrayed as constantly lusting after her. Napier also cites that male orgasms are depicted far less than male frustrations in these films because orgasms are viewed as a loss of patriarchal control. Patriarchal control can also be seen in the way that female orgasms are almost always depicted in these films, regardless of whether these orgasms are the result of traditional lovemaking or of sexual torture. I find that this is because of socio-cultural factors given the changes in roles of men and women in modern Japanese society. The Japanese male ego seems to be affected by this change so much that their frustration is reflected even in their animated pornographic films. Hence, Japanese animated pornography tend to portray female characters as young and non-threatening (high school girls, commonly) while male characters are either comically lustful (young or old voyeurs) or powerfully demonic. Many of the strong male characters in Japanese animated pornography are depicted as demons, making them appear threatening and ultimately powerful over the females. Even if the female lead characters are able to overcome these demonic male antagonists at the end of the film, these females are still portrayed as traditionally vulnerable to sexual violence.

The History of Auditing :: GCSE Business Marketing Coursework

The History of Auditing Abstract The evolution of auditing is a complicated history that has always been changing through historical events. Auditing always changed to meet the needs of the business environment of that day. Auditing has been around since the beginning of human civilization, focusing mainly, at first, on finding efraud. As the United States grew, the business world grew, and auditing began to play more important roles. In the late 1800’s and early 1900’s, people began to invest money into large corporations. The Stock Market crash of 1929 and various scandals made auditors realize that their roles in society were very important. Scandals and stock market crashes made auditors aware of deficiencies in auditing, and the auditing community was always quick to fix those deficiencies. The auditors’ job became more difficult as the accounting principles changed, and became easier with the use of internal controls. These controls introduced the need for testing; not an in-depth detailed audit. Auditing jobs would have to change to meet the changing business world. The invention of computers impacted the auditors’ world by making their job at times easier and at times making their job more difficult. Finally, the auditors’ job of certifying and testing companies’ financial statements is the backbone of the business world. Introduction Auditing has been the backbone of the complicated business world and has always changed with the times. As the business world grew strong, auditors’ roles grew more important. The auditors’ job became more difficult as the accounting principles changed. It also became easier with the use of internal controls, which introduced the need for testing, not a complete audit. Scandals and stock market crashes made auditors aware of deficiencies in auditing, and the auditing community was always quick to fix those deficiencies. Computers played an important role of changing the way audits were performed and also brought along some difficulties. A Brief History of Early Auditing Auditing has existed since the beginning of human society. Auditing was used mostly for the detection of fraud and was done through extensive detailed examination from ancient times until the late nineteenth century (Lee, 1988). Fraud was a great concern during the early history of auditing, because internal controls were not used or not used effectively until the twentieth century. The late nineteenth century was a turning point in auditing history, when laws like the English Companies Act of 1862 were enacted.

Essay on Dover Beach: An Analysis -- Arnold Dover Beach Essays

An Analysis of Dover Beach Dover Beach intrigued me as soon as I read the title. I have a great love of beaches, so I feel a connection with the speaker as he or she stands on the cliffs of Dover, looking out at the sea and reflecting on life. Arnold successfully captures the mystical beauty of the ocean as it echoes human existence and the struggles of life. The moods of the speaker throughout the poem change dramatically as do the moods of the sea. The irregular, unordered rhyme is representative of these inharmonious moods and struggles. In this case, the speaker seems to be struggling with the relationship with his or her partner. In the beginning, there is a peaceful, blissful atmosphere to the poem. Imagery of light amidst the darkness of the night is created by the use of words such as "gleams," "glimmering" and "moon-blanch'd". The speaker seems excited by the sweet night-air and the lively waves that fling the pebbles on the shore as we see by the exclamation marks in the sixth and ninth lines. The waves "begin, and cease, and then again begin," much as life is an ongoing process of cessation and rebirth. The first stanza is quite happy until the last two lines when the "tremulous cadence slow, and bring/ the eternal note of sadness in." This phrase causes the poem's tone to change to a more somber one This shift in tone is continued into the second stanza where Arnold makes an allusion to Sophocles, a Greek dramatist whose plays dwell on tragic ironies and on the role of fate in human existence. The speaker feels connected to Sophocles in that he, too, heard the "eternal note of sadness" on the Aegean (a sea on the east side of Greece). It is suggested that Sophocles was inspired by the ... ...ere is a resolution in the rhyming. It becomes more ordered towards the end, because the speaker's love can counteract the chaos of the world. The various moods of "Dover Beach" reflect the many feelings and struggles that life holds for us all. This is one individual's experience, but it is still true to all of us, because each of us have felt disillusioned and betrayed by the world at one time or another. We have all known beauty and joy, but also misery and sadness. Arnold expresses these experiences by relating them to the nature of the ocean. The experience that surpasses all others is that of love, which is the only true thing in a deceptive world. Everything that the speaker is trying to express is tied together by the poem's form. The uneven rhyme is a perfect method of pronouncing the confusion that the speaker is feeling about the world.

The Relationship Between Policy, Statutes, and Regulations in Environmental Law

Environmental policy is the official stance or statement by a government or organization which provides a framework for its environmental objectives. (C2E2. org, 2011)The US Government Environmental policy is contained in the National Environmental Policy Act of 1969 (NEPA). Congress declared under section 4331(a) of NEPA that it is â€Å"the continuing policy of the federal government†¦ to create and maintain conditions under which man and nature can exist in productive harmony, and to fulfill the social, economic, and other requirements of present and future generations of Americans. (U. S. Congress, 1969) Environmental statutes are the written will or act of the legislature with regard to expressing the stated environmental policy. (Lectric Law Library, 2011) NEPA functions in this capacity by enabling the EPA to promulgate regulations in order to set forth guidelines by which other agencies must comply in order to satisfy the intent of NEPA. Environmental regulations act as the forcing mechanism with which to gain compliance with the statute as set forth under the policy. Continuing to use NEPA as an example, the US Government environmental policy is contained in NEPA (the statute) which prescribes regulations that aim at protecting the environment. Most noteably, the EPA issued regulations regarding Environmental Impact Statements (EIS) and Environmental Assessments (AE) assess the possible environmental impacts of proposed government projects and there alternatives – and are required from all federal agencies. (US EPA, 2011)

My Special Someone

In every story of life and love, there is always something new to discover and moments to treasure forever. Even is life is painful and full of suffering, there is always one thing that would give us the courage to stand and face all the consequences of living – love. Life is a never-ending journey. We tend to find someone that we thought that would last a lifetime but sometimes, we became too blind of loving to the extent of giving everything without anything in return that leave a result of being a martyr. However, in spite of the hardships that we have felt, we still fall in love again and hope that the next time we fall, our heart our fly along with our love and not fall in the ocean leaving us wounded alone.Personally, I can say that falling in love is the greatest feeling on earth. Despite of all the problems that occur in our path, it will always be fine because we knew that there is light through the eyes of our special someone. I believe this perspective because I pre sently feel the same way. After all, I knew that I have found the one for me and I am truly blessed to have this woman by my side and prayed that she will stay with me for the rest of my life. Because of the real feelings that I have for her, I want to share my special someone named Shabnam.Shabnam is a very fine, loving and caring person. She is 25% Spanish, 25% Filipino, and 50% Indian. Shabnam has a different life-story, which we only often see in the movie or television. Shabnam’s father is Indian while her mother is Spanish. Her father knew her mother in Spain where they fall in love. Their love with one another produced a child, which is Shabnam. While her mother was pregnant, her father and her mother went to India. When her father and mother got there, Shabnam’s mother found out that her father was already married to another woman who is also pregnant during those times.Aside from this, the family of Shabnam’s father does not want Shabnam’s mother because she is Spanish. Eventhough this was the case, Shabnam’s mother accepts the situation and still pursues the birth of Shabnam but her mother and father separated. Shabnam’s stepmother born her stepbrother named Vishal. Her father brought Vishal to San Jose, California, USA. Ew years later, Shabnam’s mother died due to cancer. After her mother died, she had to live with her father but despite her father exist, she still seeks for a mother figure that would guide and take care of her. She really wanted to have a mother that is why she went to India to ask her stepmother to go to the USA to bring back the family together.Through this story, I realized that she is not selfish because even if she wanted to have a mother, she did not search for anything else but her stepmother whom she asked to bring the family back, which is the original family. Through this personality of Shabnam, it really brought me to the fact of loving and taking care of her. She deserves to be loved because she does not have any anger and selfishness even if she was lack of love and concern coming from a mother. I was amazed and I admired her eagerness and concern of bringing back the family again after a long time.   Ã‚  Ã‚   Shabnam’s story can be a realization to other children who build hatred towards their parents because of their parent’s illegitimate relationship. However, despite of this situation of Shabnam, I love and will always love her as best as I could.I started admiring Shabnam because of her smile and expressive eyes. I did not even think that she had a special family situation because she seemed to be happy always. Having a special relationship with Shabnam is amazing because she is very caring and humble. I guess despite of her family background, she remains humble and generous for it is the only way of showing her love to other people that should be for her mother.She is a Libra and I am a Gemini. Based on the astrology, Libra an d Gemini are compatible with one another. Maybe, we were meant for each other because even the astrology says that we have both found one another – I to her and her to me. We have also so many things in common; it is because we both have an Indian blood and though she has different blood aside from Indian blood, she was raised as an Indian because she lives in his father.My present relationship with Shabnam is great. We are having moments that I treasure inside my heart. I am always happy when we were together. She make me laugh, she make my heartbeats fast, she teach me to be understanding because she understand the deeper perspective of life even if it is very hard and problematic.Having an extraordinary feeling of great love and happiness, I used to think if getting married. I want to marry Shabnam because she is the one I am looking for. I know that not everything may seem to be perfect but despite of it all, I want to be with her for the rest of my life. Maybe people wil l think that I am too frustrated and too much rushing of getting married but logically, a person do not want to miss the opportunity of having someone he/she is looking for a long time. At may age, I know what I want and I know what I feel. I am sure with this next phase of my life in case she will accept my love. Now, I do not want to lose Shabnam for she is very special to me that I never felt before.When I am alone, I used to think of my future with my Shabnam. I think of our future life, which I know will be wonderful and unbeatable. I think of our future children that Shabnam and I will love and nurture. I think of our never-ending happiness even if we are already old. I also think of Shabnam while being with me – I will make her happy and contented in life. I will not leave Shabnam as long as I breathe and live.Writing this paper makes me express my love, joy and admiration to Shabnam. She opened my eyes to different perceptions of living in this kind of world. I used t o live as it is before having my own world and ignoring many things but when I met Shabnam, things have change and made me become a better person that is why I love Shabnam everyday because she was not just a beautiful person outside but also inside. Today, we are having a great time together, keeping the fire burning in our hearts with love, faithfulness, and loyalty with one another. She may not tell that she do not want to be like her mother but I know she does so I will took care of her and love her as my one and only in this whole wide world.While writing this paper, I also created a simple poem for Shabnam that shows my love and admiration to her. I want to give her this poem as a sign of my love to her that will not fade until the day I die because I love her so much.I have searched for you,A long, long time ago.I thought I would never ever met,Someone who is like you.Now you’re here with me,And hope will always be.The one I admire before,Today, tomorrow and forevermor e.You make my heart sing,You make my life ring.You bring sunlight in the sky,You bring colors in the dark.I want to be with you,I want to love you.I want to marry you;So please answer, I do.Work CitedFutral, Ann. Gemini with Libra. (2006) Retrieved August 27, 2007 from

Econometrics Chapter Summaries Essay

2) Basic Ideas of Linear Regression: The Two-Variable Model In this chapter we introduced some fundamental ideas of regression analysis. Starting with the key concept of the population regression function (PRF), we developed the concept of linear PRF. This book is primarily concerned with linear PRFs, that is, regressions that are linear in the parameters regardless of whether or not they are linear in the variables. We then introduced the idea of the stochastic PRF and discussed in detail the nature and role of the stochastic error term u. PRF is, of course, a theoretical or idealized construct because, in practice, all we have is a sample(s) from some population. This necessitated the discussion of the sample regression function (SRF). We then considered the question of how we actually go about obtaining the SRF. Here we discussed the popular method of ordinary least squares (OLS) and presented the appropriate formulas to estimate the parameters of the PRF. We illustrated the OLS method with a fully worked-out numerical example as well as with several practical examples. Our next task is to find out how good the SRF obtained by OLS is as an estimator of the true PRF. We undertake this important task in Chapter 3. 3) The Two-Variable Model: Hypothesis Testing In Chapter 2 we showed how to estimate the parameters of the two-variable linear regression model. In this chapter we showed how the estimated model can be used for the purpose of drawing inferences about the true population regression model. Although the two-variable model is the simplest possible linear regression model, the ideas introduced in these two chapters are the foundation of the more involved multiple regression models that we will discuss in ensuing chapters. As we will see, in many ways the multiple regression model is a straightforward extension of the two-variable model. 4) Multiple Regression: Estimation and Hypothesis Testing In this chapter we considered the simplest of the multiple regression models, namely, the three-variable linear regression model—one dependent variable and two explanatory variables. Although in many ways a straightforward extension of the two-variable linear regression model, the three-variable model introduced several new concepts, such as partial regression coefficients, adjusted and unadjusted multiple coefficient of determination,  and multicollinearity. Insofar as estimation of the parameters of the multiple regression coefficients is concerned, we still worked within the framework of the classical linear regression model and used the method of ordinary least squares (OLS). The OLS estimators of multiple regression, like the two-variable model, possess several desirable statistical properties summed up in the Gauss-Markov property of best linear unbiased estimators (BLUE). With the assumption that the disturbance term follows the normal distribution with zero mean and constant variance ÏÆ'2, we saw that, as in the two-variable case, each estimated coefficient in the multiple regression follows the normal distribution with a mean equal to the true population value and the variances given by the formulas developed in the text. Unfortunately, in practice, ÏÆ'2 is not known and has to be estimated. The OLS estimator of this unknown variance is . But if we replace ÏÆ'2 by , then, as in the two-variable case, each estimated coefficient of the multiple regression follows the t distribution, not the normal distribution. The knowledge that each multiple regression coefficient follows the t distribution with d.f. equal to (n – k), where k is the number of parameters estimated (including the intercept), means we can use the t distribution to test statistical hypotheses about each multiple regression coefficient individually. This can be done on the basis of either the t test of significance or the confidence interval based on the t distribution. In this respect, the multiple regression model does not differ much from the two-variable model, except that proper allowance must be made for the d.f., which now depend on the number of parameters estimated. However, when testing the hypothesis that all partial slope coefficients are simultaneously equal to zero, the individual t testing referred to earlier is of no help. Here we should use the analysis of variance (ANOVA) technique and the attendant F test. Incidentally, testing that all partial slope coefficients are simultaneously equal to zero is the same as testing that the multiple coefficient of determination R2 is equal to zero. Therefore, the F test can also be used to test this latter but equivalent hypothesis. We also discussed the question of when to add a variable or a group of variables to a model, using either the t test or the F test. In this context we also discussed the method of restricted least squares. 5) Functional Forms of Regression Models In this chapter we considered models that are linear in parameters, or that can be rendered as such with suitable transformation, but that are not necessarily linear in variables. There are a variety of such models, each having special applications. We considered five major types of nonlinear-in-variable but linear-in-parameter models, namely: 1.The log-linear model, in which both the dependent variable and the explanatory variable are in logarithmic form. 2.The log-lin or growth model, in which the dependent variable is logarithmic but the independent variable is linear. 3.The lin-log model, in which the dependent variable is linear but the independent variable is logarithmic. 4.The reciprocal model, in which the dependent variable is linear but the independent variable is not. 5.The polynominal model, in which the independent variable enters with various powers. Of course, there is nothing that prevents us from combining the features of one or more of these models. Thus, we can have a multiple regression model in which the dependent variable is in log form and some of the X variables are also in log form, but some are in linear form. We studied the properties of these various models in terms of their relevance in applied research, their slope coefficients, and their elasticity coefficients. We also showed with several examples the situations in which the various models could be used. Needless to say, we will come across several more examples in the remainder of the text. In this chapter we also considered the regression-through-the-origin model and discussed some of its features. It cannot be overemphasized that in choosing among the competing models, the overriding objective should be the economic relevance of the various models and not merely the summary statistics, such as R2. Model building requires a proper balance of theory, availability of the appropriate data, a good understanding of the statistical properties of the various models, and the elusive quality that is called practical judgment. Since the theory underlying a topic of interest is never perfect, there is no such thing as a perfect model. What we hope for is a reasonably good model that will balance all these criteria. Whatever model is chosen in practice, we have to pay careful attention to the units in which the dependent and independent variables are expressed, for the interpretation of regression coefficients may hinge upon units of  measurement. 6) Dummy Variable Regression Models In this chapter we showed how qualitative, or dummy, variables taking values of 1 and 0 can be introduced into regression models alongside quantitative variables. As the various examples in the chapter showed, the dummy variables are essentially a data-classifying device in that they divide a sample into various subgroups based on qualities or attributes (sex, marital status, race, religion, etc.) and implicitly run individual regressions for each subgroup. Now if there are differences in the responses of the dependent variable to the variation in the quantitative variables in the various subgroups, they will be reflected in the differences in the intercepts or slope coefficients of the various subgroups, or both. Although it is a versatile tool, the dummy variable technique has to be handled carefully. First, if the regression model contains a constant term (as most models usually do), the number of dummy variables must be one less than the number of classifications of each qualitat ive variable. Second, the coefficient attached to the dummy variables must always be interpreted in relation to the control, or benchmark, group—the group that gets the value of zero. Finally, if a model has several qualitative variables with several classes, introduction of dummy variables can consume a large number of degrees of freedom (d.f.). Therefore, we should weigh the number of dummy variables to be introduced into the model against the total number of observations in the sample. In this chapter we also discussed the possibility of committing a specification error, that is, of fitting the wrong model to the data. If intercepts as well as slopes are expected to differ among groups, we should build a model that incorporates both the differential intercept and slope dummies. In this case a model that introduces only the differential intercepts is likely to lead to a specification error. Of course, it is not always easy a priori to find out which is the true model. Thus, some amount of experimentation is required in a concrete study, especially in situations where theory does not provide much guidance. The topic of specification error is discussed further in Chapter 7. In this chapter we also briefly discussed the linear probability model (LPM) in which the dependent variable is itself binary. Although LPM  can be estimated by ordinary least square (OLS), there are several problems with a routine application of OLS. Some of the problems can be resolved easily and some cannot. Therefore, alternative estimating procedures are needed. We mentioned two such alternatives, the logit and probit models, but we did not discuss them in view of the somewhat advanced nature of these models (but see Chapter 12). 7) Model Selection: Criteria and Tests The major points discussed in this chapter can be summarized as follows: 1.The classical linear regression model assumes that the model used in empirical analysis is â€Å"correctly specified.† 2.The term correct specification of a model can mean several things, including: a.No theoretically relevant variable has been excluded from the model. b.No unnecessary or irrelevant variables are included in the model. c.The functional form of the model is correct. d.There are no errors of measurement. 3.If a theoretically relevant variable(s) has been excluded from the model, the coefficients of the variables retained in the model are generally biased as well as inconsistent, and the error variance and the standard errors of the OLS estimators are biased. As a result, the conventional t and F tests remain of questionable value. 4.Similar consequences ensue if we use the wrong functional form. 5.The consequences of including irrelevant variables(s) in the model are less serious in that estimated coefficients still remain unbiased and consistent, the error variance and standard errors of the estimators are correctly estimated, and the conventional hypothesis-testing procedure is still valid. The major penalty we pay is that estimated standard errors tend to be relatively large, which means parameters of the model are estimated rather imprecisely. As a result, confidence intervals tend to be somewhat wider. 6.In view of the potential seriousness of specification errors, in this chapter we considered several diagnostic tools to help us find out if we have the specification error problem in any concrete situation. These tools include a graphical examination of the residuals and more formal tests, such as MWD and RESET. Since the search for a theoretically correct model can be exasperating, in  this chapter we considered several practical criteria that we should keep in mind in this search, such as (1) parsimony, (2) identifiability, (3) goodness of fit, (4) theoretical consistency, and (5) predictive power. As Granger notes, â€Å"In the ultimate analysis, model building is probably both an art and a science. A sound knowledge of theoretical econometrics and the availability of an efficient computer program are not enough to ensure success.† 8) Multicollinearity: What Happens If Explanatory Variables are Correlated? An important assumption of the classical linear regression model is that there is no exact linear relationship(s), or multicollinearity, among explanatory variables. Although cases of exact multicollinearity are rare in practice, situations of near exact or high multicollinearity occur frequently. In practice, therefore, the term multicollinearity refers to situations where two or more variables can be highly linearly related. The consequences of multicollinearity are as follows. In cases of perfect multicollinearity we cannot estimate the individual regression coefficients or their standard errors. In cases of high multicollinearity individual regression coefficients can be estimated and the OLS estimators retain their BLUE property. But the standard errors of one or more coefficients tend to be large in relation to their coefficient values, thereby reducing t values. As a result, based on estimated t values, we can say that the coefficient with the low t value is not statistically different from zero. In other words, we cannot assess the marginal or individual contribution of the variable whose t value is low. Recall that in a multiple regression the slope coefficient of an X variable is the partial regression coefficient, which measures the (marginal or individual) effect of that variable on the dependent variable, holding all other Xvariables constant. However, if the objective of study is to estimate a group of coefficients fairly accurately, this can be done so long as collinearity is not perfect. In this chapter we considered several methods of detecting multicollinearity, pointing out their pros and cons. We also discussed the various remedies that have been proposed to solve the problem of multicollinearity and noted their strengths and weaknesses. Since multicollinearity is a feature of a given sample, we cannot foretell which method of detecting multicollinearity or which  remedial measure will work in any given concrete situation. 9) Heteroscedasticity: What Happens If the Error Variance Is Nonconstant? A critical assumption of the classical linear regression model is that the disturbances ui all have the same (i.e., homoscedastic) variance. If this assumption is not satisfied, we have heteroscedasticity. Heteroscedasticity does not destroy the unbiasedness property of OLS estimators, but these estimators are no longer efficient. In other words, OLS estimators are no longer BLUE. If heteroscedastic variances ÏÆ'i2 are known, then the method of weighted least squares (WLS) provides BLUE estimators. Despite heteroscedasticity, if we continue to use the usual OLS method not only to estimate the parameters (which remain unbiased) but also to establish confidence intervals and test hypotheses, we are likely to draw misleading conclusions, as in the NYSE Example 9.8. This is because estimated standard errors are likely to be biased and therefore the resulting t ratios are likely to be biased, too. Thus, it is important to find out whether we are faced with the heteroscedasticity problem in a specific application. There are several diagnostic tests of heteroscedasticity, such as plotting the estimated residuals against one or more of the explanatory variables, the Park test, the Glejser test, or the rank correlation test (See Problem 9.13). If one or more diagnostic tests reveal that we have the heteroscedasticity problem, remedial measures are called for. If the true error variance ÏÆ'i2 is known, we can use the method of WLS to obtain BLUE estimators. Unfortunately, knowledge about the true error variance is rarely available in practice. As a result, we are forced to make some plausible assumptions about the nature of heteroscedasticity and to transform our data so that in the transformed model the error term is homoscedastic. We then apply OLS to the transformed data, which amounts to using WLS. Of course, some skill and experience are required to obtain the appropriate transformations. But without such a transformation, the problem of heteroscedasticity is insoluble in practice. However, if the sample size is reasonably large, we can use White’s procedure to obtain heteroscedasticity-corrected standard errors. 10) Autocorrelation: What Happens If Error Terms Are Correlated? The major  points of this chapter are as follows: 1.In the presence of autocorrelation OLS estimators, although unbiased, are not efficient. In short, they are not BLUE. 2.Assuming the Markov first-order autoregressive, the AR(1), scheme, we pointed out that the conventionally computed variances and standard errors of OLS estimators can be seriously biased. 3.As a result, standard t and F tests of significance can be seriously misleading. 4.Therefore, it is important to know whether there is autocorrelation in any given case. We considered three methods of detecting autocorrelation: a.graphical plotting of the residuals b.the runs test c.the Durbin-Watson d test 5.If autocorrelation is found, we suggest that it be corrected by appropriately transforming the model so that in the transformed model there is no autocorrelation. We illustrated the actual mechanics with several examples. 11) Simultaneous Equation Models In contrast to the single equation models discussed in the preceding chapters, in simultaneous equation regression models what is a dependent (endogenous) variable in one equation appears as an explanatory variable in another equation. Thus, there is a feedback relationship between the variables. This feedback creates the simultaneity problem,rendering OLS inappropriate to estimate the parameters of each equation individually. This is because the endogenous variable that appears as an explanatory variable in another equation may be correlated with the stochastic error term of that equation. This violates one of the critical assumptions of OLS that the explanatory variable be either fixed, or nonrandom, or if random, that it be uncorrelated with the error term. Because of this, if we use OLS, the estimates we obtain will be biased as well as inconsistent. Besides the simultaneity problem, a simultaneous equation model may have an identification problem. An identification problem means we cannot uniquely estimate the values of the parameters of an equation. Therefore, before we estimate a simultaneous equation model, we must find out if an equation in  such a model is identified. One cumbersome method of finding out whether an equation is identified is to obtain the reduced form equations of the model. A reduced form equation expresses a dependent (or endogenous) variable solely as a function of exogenous, or predetermined, variables, that is, variables whose values are determined outside the model. If there is a one-to-one correspondence between the reduced form coefficients and the coefficients of the original equation, then the original equation is identified. A shortcut to determining identification is via the order condition of identification. The order condition counts the number of equations in the model and the number of variables in the model (both endogenous and exogenous). Then, based on whether some variables are excluded from an equation but included in other equations of the model, the order condition decides whether an equation in the model is underidentified, exactly identified, or overidentified. An equation in a model is underidentified if we cannot estimate the values of the parameters of that equation. If we can obtain unique values of parameters of an equation, that equation is said to be exactly identified. If, on the other hand, the estimates of one or more parameters of an equation are not unique in the sense that there is more than one value of some parameters, that equation is said to be overidentified. If an equation is underidentified, it is a dead-end case. There is not much we can do, short of changing the specification of the model (i.e., developing another model). If an equation is exactly identified, we can estimate it by the method of indirect least squares (ILS). ILS is a two-step procedure. In step 1, we apply OLS to the reduced form equations of the model, and then we retrieve the original structural coefficients from the reduced form coefficients. ILS estimators are consistent; that is, as the sample size increases indefinitely, the estimators converge to their true values. The parameters of the overidentified equation can be estimated by the method of two-stage least squares (2SLS). The basic idea behind 2SLS is to replace the explanatory variable that is correlated with the error term of the equation in which that variable appears by a variable that is not so correlated. Such a variable is called a proxy, or instrumental, variable.2SLS estimators, like the ILS estimators, are consistent estimators. 12) Selected Topics in Single Equation Regression Models In this chapter we discussed several topics of considerable practical importance. The first topic we discussed was dynamic modeling, in which time or lag explicitly enters into the analysis. In such models the current value of the dependent variable depends upon one or more lagged values of the explanatory variable(s). This dependence can be due to psychological, technological, or institutional reasons. These models are generally known as distributed lag models. Although the inclusion of one or more lagged terms of an explanatory variable does not violate any of the standard CLRM assumptions, the estimation of such models by the usual OLS method is generally not recommended because of the problem of multicollinearity and the fact that every additional coefficient estimated means a loss of degrees of freedom. Therefore, such models are usually estimated by imposing some restrictions on the parameters of the models (e.g., the values of the various lagged coefficients decline from the f irst coefficient onward). This is the approach adopted by the Koyck, the adaptive expectations, and the partial, or stock, adjustment models. A unique feature of all these models is that they replace all lagged values of the explanatory variable by a single lagged value of the dependent variable. Because of the presence of the lagged value of the dependent variable among explanatory variables, the resulting model is called an autoregressive model. Although autoregressive models achieve economy in the estimation of distributed lag coefficients, they are not free from statistical problems. In particular, we have to guard against the possibility of autocorrelation in the error term because in the presence of autocorrelation and the lagged dependent variable as an explanatory variable, the OLS estimators are biased as well as inconsistent. In discussing the dynamic models, we pointed out how they help us to assess the short- and long-run impact of an explanatory variable on the dependent variable. The next topic we discussed related to the phenomenon of spurious, or nonsense, regression. Spurious regression arises when we regress a nonstationary random variable on one or more nonstationary random variables. A time series is said to be (weakly) stationary, if its mean, variance, and covariances at various lags are not time dependent. To find out whether a time series is stationary, we can use the unit root test. If the unit root test (or other tests) shows that the time series of interest is stationary,  then the regression based on such time series may not be spurious. We also introduced the concept of cointegration. Two or more time series are said to be cointegrated if there is a stable, long-term relationship between the two even though individually each may be nonstationary. If this is the case, regression involving such time series may not be spurious. Next we introduced the random walk model, with or without drift. Several financial time series are found to follow a random walk; that is, they are nonstationary either in their mean value or their variance or both. Variables with these characteristics are said to follow stochastic trends. Stock prices are a prime example of a random walk. It is hard to tell what the price of a stock will be tomorrow just by knowing its price today. The best guess about tomorrow’s price is today’s price plus or minus a random error term (or shock, as it is called). If we could predict tomorrow’s price fairly accurately, we would all be millionaires! The next topic we discussed in this chapter was the dummy dependent variable, where the dependent variable can take values of either 1 or 0. Although such models can be estimated by OLS, in which case they are called linear probability models (LPM), this is not the recommended procedure since probabilities estimated from such models can sometimes be negative or greater than 1. Therefore, such models are usually estimated by the logit or probit procedures. In this chapter we illustrated the logit model with concrete examples. Thanks to excellent computer packages, estimation of logit and probit models is no longer a mysterious or forbidding task.