Criminal Justice Organizations And Challenges Faced

Criminal Justice Organizations And Challenges Faced In the world of criminal justice organizations, leaders are facing challenges that have become a part of the daily routine. Criminal justice organizations consist of corrections, courts, and the police. These organizations each have an important role in the criminal justice system that results in the leadership of each organization. This paper will explain some of these challenges of the leaders and steps that can be taken to affect change for the future. Leadership The ability to effectively lead a group of followers making the organization and the followers successful is leadership. An individual must have dedication, commitment and not be afraid of taking in leadership. In leadership, learning and communication skills are important in the accomplishing these tasks while still being able to maintain valid ideas and principles. To ensure their success, leader must know how to treat the followers. In addition, leaders should know how to motivate others and stay consistent their values, morals and ethical standards (Schermerhorn, Hunt, Osborn, Uhl-Blen, 2010). Challenges There are many challenges that leaders face in criminal justice organizations. Some of these challenges include increased criminal activity, staffing, budgeting, legal and political, terrorism, conflict and power, communication, and ethical and moral issues. Most of these challenges relate to other challenges. Resolving one challenge may help resolve another challenge. Criminal activity increases as the population increases. Police officers, the courts and other criminal justice organizations are not able to protect and serve the public and ensure safety because of the shortage of professionals. Hiring additional staff or introducing more community programs is necessary to help reduce criminal activity. The question of additional staff brings another challenge of budgeting. Leadership in criminal organizations is battling shortfalls in the budget which brings about staffing and equipment shortages. Politically, state judges and prosecutors are in position from election votes. When applicable the election can bring in new appointments and result in a number of changes within criminal justice organizations. In terms of legal challenges, the changing of laws, policies and procedures can be become a challenge in enforcement and being aware of political alliances (Duelin, 2010). Conflict and power are two other challenges that leaders face in the criminal justice agencies. The conflict occurs among the variety of agencies inside the criminal justice system. An example is between the courts and the police. The police have a job to uphold protection and safety, so they may make arrests charging individuals with crimes with the hope that these individuals are prosecuted and punished for these crimes. On the other hand, the courts decide what the punishment should be and how it is carried out. These two agencies do not have the same goals and this becomes a conflict. The police and courts have a substantial amount of power with these responsibilities. Leadership for these criminal justice agencies must balance this power with fairness (Duelin, 2010). Another leadership challenge is communication. The barriers of communication are individual and organizational. Individual barriers relate to how an individual interprets a conversation or message. Organizational barriers relate to the culture of an organization. Leadership includes being an effective communicator. An effective communicator will have the ability to handle these barriers. Within the criminal justice system, organizations have different jargon which is a part of their culture. This can make it very difficult to communicate with other organizations. Another communication barrier is between the professionals in the criminal justice organizations and the public. With the increasing population, our society includes many races, nationalities, cultures and languages. The ability to communicate with the public is essential. Other important leadership challenges are ethical and moral standards. Ethics allows us a way to make moral choices at times when we are uncertain of what to do in a situation involving moral issues. In the criminal justice system, ethics is important in management and policy decisions that relate to punishment and the rationality in making decisions. These decisions regarding punishment are to rehabilitate, deter or impose imprisonment. A criminal justice organization consists of professionals that carry power and authority over others and in some case have authority to use force and physical coercion against others. Ethical rules and responsibilities are given to these professionals as the law or accepted standards of behavior that require them to be aware of these ethical standards while performing their duties. To avoid any attempt to abuse power, ethics are crucial in decision making that involves discretion, force, and due process. Domestic terrorism is the greatest challenge for criminal justice organization. Since 9/11, safety and security is a top priority. Every role in the criminal justice system is affected by terrorism. Although a number of new policies and procedure have been put into place to help ensure the safety of all, terrorism is an ongoing challenge. Employees from many criminal and correctional agencies are away from their regular jobs and are serving active duty for the military regarding the war on terrorism. This means these departments are suffering and have a lack of security. Other employees such as the local police and persecutors have to take more prominent roles in the investigation and prosecution of crimes that would normally be the responsibility of federal law enforcement (Daniels, 2002). Affecting Change for the Future Criminal justice professionals can affect changes in the future by reducing the opportunity for crime, changing peopleà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s fundamental values, nurturing these values into the youth, and motivating the people responsible for crime will reduce the likelihood of future criminal behavior. Continuing to use the advancement of technology such as DNA analysis, forensic testing, surveillance, stoplight cameras, biometrics, and radio frequency identification microchips will continue to enhance crime solving and prevention. Also increasing the intelligence of databases for use by the general public in addition to the use by analysts and police officers will help educate and make the public aware of criminal activity. A current example of this is the sex offender registries and access to inmate information (Ritter, 2006). In terms of improving communication and information-sharing, the Office of Justice Programs is working together with the FBI to widen access to the Regional Information Sharing System and the FBIà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s Law Enforcement Online system to combine the operations of the two systems. This will allow the RISS program to network a variety of different federal and state law enforcement information-sharing to create a secure network that can share information at levels of the government in law enforcement (Daniels, 2002). In order to facilitate change, hiring additional criminal justice professionals and providing excellent training skills will be a necessity. The training should consist of ethical and cultural standards, policies and procedures, weapons, equipment, and communications skills. The Police Service program has volunteers and the program works to increase the citizen volunteers in law enforcement agencies. This allows the law enforcement professionals to be available to perform their front-line duties. In additions, it allows the law enforcement agencies to enhance existing programs and start new programs while expanding ways to use citizen volunteers (Daniels, 2002). In terms of terrorism, criminal justice organizations must stay alert in linking terrorism to other crimes. These crimes include cybercrime, drug trafficking and identity theft. In the 9/11 situation, drug trafficking was the source of finance for the terrorists, the hijackers stole the identity of innocent victims to start and build credit and bank accounts to cover terrorist activities. Law enforcementà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s determination of cybercrime was found. The terrorists were using computers to attach banking networks, and defense system computers. The internet is the method chosen by terrorist organizations to communicate with members by e-mail and to raise funds. A balance between neighborhood security and national security must be made. In creating this balance maximizing our resources will allow the continuation of making advances on both sides. This means that criminal justice organizations at every level will have to work together to resolve the terrorism challenge. Conclusion Leadership is an important aspect in criminal justice organizations. Challenges arise on a daily basis. Addressing organizational change including more cooperation between the community and law enforcers, and the institution and advancement of technology in law enforcement, and more training will help alleviate some of the current challenges and help with future challenges.

What Are the Problems Faced by Indian Educational System

What Is The Biggest Problem Facing Our Educational System Today? Education is a vast and broad subject, and has been a topic open to discussion for many centuries. In the broadest sense of the term, education is any experience or exposure to an act that will have a formative and learned effect on a person's character and mind. Modern education tends to mean the process by which a society will teach and pass on its broad knowledge, skill and values, from one generation to another.Education itself can fall into many categories, ranging from those that many will be aware of such as schools, colleges and universities, through to adult education and indigenous education. Educational systems are always likely to be affected by a number of economic factors. Monetary wise, funding and budgeting for education has always been a factor that faces governments worldwide.Education is essential for economic growth, theorist have found that a higher rate of teaching and education in society has a po sitive affect on the growth of a nation. Other problems that may face an educational system include the differentiation between students who attend public and private schools. Those who can afford private school are often seen to be given the best education possible, while children from a less privileged background are forced to attend public schools, which by default may not have as good a quality of teaching.Though in many examples this is not the case, the long standing stereotype still rings true in modern society, as those from a wealthier family often seem to progress further and quicker up the job ladder. Alternatively, other factors facing educational systems include a decrease in funding, larger class sizes, reduction in teaching staff and safety in schools

Process Of Determining A Regression Finance Essay - Free Essay Example

Sample details Pages: 10 Words: 3039 Downloads: 8 Date added: 2017/06/26 Category Finance Essay Type Essay any type Did you like this example? The process of determining a regression or prediction equation to predict Y from X , with all the method of least squares. In the resulting regression line, the sum of the squared discrepancies between the actual dependent values and the corresponding values predicted by the line are as small as possible, hence the name least squares' (Hassard, 1991). The estimated regression equation is: Y = ß0 + ß1X1 + ß2X2 + ß3D + à ª Where the ßs are the OLS estimates of the Bs. OLS minimizes the sum of the squared residuals OLS minimizes SUM à ª2 The residual, à ª, is the difference between the actual Y and the predicted Y and has a zero mean. In other words, OLS calculates the slope coefficients so that the difference between the predicted Y and the actual Y is minimized. The residuals are squared so as to compare negative errors to positive errors more easily. The properties are: 1. The regression line defined by 1 and 2 passes through the means of the observed values 2. The mean of the predicted Ys for the sample will equal the mean of the observed Ys for the sample. 3. The sample mean of the residuals will be 0. 4. The correlation between the residuals and the predicted values of Y will be 0. 5. The correlation between the residuals and the observed values of X will be 0. Don’t waste time! Our writers will create an original "Process Of Determining A Regression Finance Essay" essay for you Create order Stationarity Stationarity can be defined as a time series yt is covariance (or weakly) stationary if, in support of if, its mean and variance are both finite and outside of time, and the auto-covariance doesnt overgrow time, for those t and t-s, 1. Finite mean E (yt) = E (yt-s) =  µ 2. Finite variance Var (yt) = E [(yt- µ) 2] = E [(yt-s  µ) 2] = 3. Finite auto-covariance Cov (yt, yt-s) = E [(yt- µ) (yt-s  µ)] = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ³s Non-Stationarity The variance is time dependent and visits infinity as time strategies to infinity. A time series which is not stationary depending on mean can be done stationary by differencing. Differencing is a popular and effective method of removing a stochastic trend from a series. Nonstationarity in a time series occurs individuals no constant mean, no constant variance or those two properties. It could possibly originate from various sources nevertheless the most crucial one is the unit root. Unit root Any sequence that contains one or more characteristic roots which can be comparable to is known as a unit root process. The most convenient model which will contain a unit root may be the AR (1) model. Look at the autoregressive process of order one, AR (1), below Yt = ÃÆ'†°Ãƒâ€šÃ‚ ¸Yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt Where ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt denotes a serially uncorrected white-noise error term which has a mean of zero and also a constant variance If ÃÆ'†°Ãƒâ€šÃ‚ ¸ = 1, becomes a random walk without drift model, that is certainly, a nonstationary process. 2, we face precisely what is called the unit root problem. This means that were facing a scenario of nonstationarity in the series. If, however, ÃÆ'†°Ãƒâ€šÃ‚ ¸ 1, then this series Yt is stationary. The stationarity on the series is essential because correlation could persist in nonstationary time series whether or not the sample is quite large and might end in what is called spurious (or nonsense) regression (Yule, 1989). The unit root problem can be solved, or stationarity can be performed, by differencing the info set (Wei, 2006). Testing of Stationarity If the time series features a unit root, the series is considered to be non-stationary. Tests which may be helpful to confirm the stationarity are: 1. Partial autocorrelation function and Ljung and Box statistics. 2. Unit root tests. To check the stationarity and when there may be presence of unit root inside the series, one of the most famous with the unit root tests are the ones derived by Dickey and Fuller and described in Fuller (1976), also Augmented Dickey-Fuller (ADF) or said-Dickey test has become mostly used. Dickey-Fuller (DF) test: Dickey and Fuller (DF) considered the estimation of the parameter ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± from the models: 1. A simple AR (1) model is: yt à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ½Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¡Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  yt-1 à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¥ 2. Yt =  µ + ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt 3. Yt =  µ + ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²t + ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt It si assumed that y0 = 0 and ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt ~ independent identically distributed, i.i.d (0, ÃÆ' Ãƒâ€ Ã¢â‚¬â„¢2) The hypotheses are: H0: ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± = 1 H1: |ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±| 1 The ADF test may be tested on at the least three possible models: (i) A pure random walk without a drift. This is defined by while using constraint ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±= 0, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² = 0 and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ³ = 0. This may lead to the equation ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt = ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt The Equation above is a nonstationary series because its variance grows with time (Pfaff, 2006). (ii) A random walk with a drift. This is obtained by imposing the constraint ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² = 0 and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ³ = 0 which yields to the equation ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt (iii) A deterministic trend with a drift. For ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚   0, becomes the following deterministic trend with a drift model ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± + ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²t + ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  yt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt The sign of the drift parameter (ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±) causes the series to wander upward if positive and downward if negative, whereas the length of the value aspects the steepness of the series (Pfaff, 2006). Augmented Dickey-Fuller (ADF): Augmented Dickey-Fuller test can be an augmented version on the Dickey-Fuller test to accommodate some varieties of serial correlation and useful for an increased and much more complicated list of time series models. If you find higher order correlation then ADF test is used but DF is utilized for AR (1) process. The testing strategy of the ADF test matches for that Dickey-Fuller test but we look at the AR (p) equation: yt à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ½Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¡Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  t à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  y à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  iyt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt Assume that there is for the most part one unit root, thus the operation is unit root non-stationary. After reparameterize this equation, we get equation for AR (p):  Ãƒ ¢Ã¢â€š ¬Ã… ¾Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  yt à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ½Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ­Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  t à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¡Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  yt-1 à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  i Ãƒ ¢Ã¢â€š ¬Ã… ¾Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  yt-i à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¥t Each version from the test have their critical value which will depend on how big the sample. In each case, the null hypothesis is we have a unit root, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ³ = 0. Within tests, critical values are calculated by Dickey and Fuller and is also dependent upon whether it has an intercept and, or deterministic trend, be it a DF or ADF test. Test has problems. Its got low statistical power to reject a unit root, and power is reduced by having the lagged differences. The ADF test is also affected by size distortions that occur every time a large first-order moving average component exists inside the time series. Diebold and Rudebusch (1991) show the test has low power against the alternative of fractionally integrated series. Perron (1989, 1993) show that whenever a period of time series is generated by way of a procedure that is stationary in regards to broken trend, standard DF tests of an I(1) null might have very lower power. Alternatively, Leybourne, Mills and Newbold ( 1998) show that after a moment series is generated by way of a process that is I(1), however intense break, routine putting on the DF test may result in a severe problem of spurious rejection on the null when the break is at the outset of the sample period. Granger Causality test Granger (1980) Granger causality measures whether one thing happens before another thing and helps predict it and nothing else. Grangers definition1 for probabilistic causality assumes three basic axioms: (1) The cause must precede the effect in time, (2) The cause contains some unique information concerning the effects future value, (3) While the strength of causal relations may vary over time, their existence and direction are time-invariant (Granger, 1980; 1988a, b). The general definition for probabilistic causality: If F (Yt+jÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ Ãƒ ¢Ã¢â€š ¬Ã… ¡Ut) ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚   F (YT+jÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ Ãƒ ¢Ã¢â€š ¬Ã… ¡Ut Xt), Then Xt causes Yt+j; states that if the j-step-ahead (where j represents the time delay between the perceived cause and effect) conditional probability distribution (P) of random variable Yt+j in period t + j is changed by removal of X from the universal information set (U) existing in period 1, then X, causes U, would contain all possible information in existence up to and including period t. Xt, would contain all past and present values of variable X. The change would be due to some unique information Xt, has concerning Ys future distribution. If X occurs, and X and Y arc causally related, Ys probability of future occurrence changes. Note that Ut, includes Y, so that Xt, contains some information about the value of future Y not found in past or present Y (Granger, 1980; 1988a, b). The general definition implies that if a variable X causes variable Y, then if one is trying to forecast a distribution of future Y, one will frequently he better off using the information contained in past and present valu es of X (Granger, 1980; 1988a, b). GRANGER (1980), noting the absence of a universally accepted definition for causality, offered a probabilistic definition which he suggested might be useful in econometric research. Granger (1980) proposed two operational definitions which he derived from his general one. The first he referred to as causality-in-mean. The second he referred to as full causality or causality-in-distribution. Full causality is preferred to mean causality when decision-making populations are characterized by non-linear utility functions (Ressler and Kling, 1990). Ashley et al. (1980) proposed and applied a method of testing for a mean causal relationship between two variables. Given a prior belief that X caused Y, mean causality was inferred if the mean squared error of a one-step-ahead point forecast of Y from a bivariate model (an information set of past and present Y and X) was significantly less than that from a univariate model (past and present Y) over the sa me out-of-sample period. 1 Source TESTING FOR GRANGERS FULL CAUSALITY by Ted Covey and David A.Bessler 2Granger causality tests are mostly used in situations where we are willing to consider 2-dimensional systems. If the data are well described by a 2-dimensional system (no zt variables) the Granger causality concept is likely to be straightforward to think about and to test, noting that there are special problems with testing for Granger causality in co-integrated relations (see Toda and Phillips (1991). Engle and Granger A non-stationary time series of which exhibit a good-term equilibrium relationship tends to be said to become cointegrated. The potential of non-fixed time series to possibly be cointegrated was considered inwards 1970S by Engle and also Granger. Many people define cointegrated specifics in their own paper coming from 1987 in the following approach. Consider two non-stationary time series, yt and xt where each of the time series become stationary after differencing once, i.e. they are both are structured associated with, I(1). These non-stationary time series are then said to be cointegrated of order one-one, CI(1,1) if there exists a cointegrating vector ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± that in a linear combination of the two variables yields a stationary term ÃÆ'Ã… ½Ãƒâ€šÃ‚ ¼t ~ I(0), in the regression ÃÆ'Ã… ½Ãƒâ€šÃ‚ ¼t = yt ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±xt. Cointegration signifies that these kind of nonstationary specifics contribution an extended operate human relationship, and so the brand new time series from pairing the actual connected non-standing time serial is actually fixed, i.e. the this deviations have limited alternative and also a regular necessarily mean. On the whole, two series are cointegrated when they are both integrated of order d, I(d) along with a linear blend of them includes a lower order of integration, (d-b), where b0. Time series need to be non-stationary to allow them to be able to be cointegrated. Thus, one stationary variable and one non-stationary variable cannot have a long-term co-movement, because the first youve gotten a constant mean and finite variance, whereas your second one does not, hence the gap between your two will not be stationary. But, if there are more than two time series within a system, it is also possible to help them to have different order of integration. Consider three time series, yt ~ I (2), xt ~ I (2), qt ~ I(1). If yt and xt are cointegrated, to ensure that their linear combination brings about a disturbance term ÃƒÆ 'Ã… ½Ãƒâ€šÃ‚ ¼t = yt ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±xt that is integrated of order 1, I(1), then it is potentially feasible that ut and qt are cointegrated with resulting stationary disturbance term st = qt ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²ut., where ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±,ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² are cointegrating vectors. Generally, with n integrated variables there can potentially exist nearly to n-1 cointegrating vectors. This does not necessarily mean that each one integrated variables are cointegrated. It is possible to find one example is a couple of 1(d) variables that is not cointegrated. If variables are integrated of different orders, they can be cointegrated. However, youll be able to have cointegration with variables of various orders. Pagan and Wickens (1989: 1002) illustrate this point clearly that its possible to uncover cointegration among variables of orders (when there are many than two variables). Enders (2004: 323) agrees with Pagan and Wickens (1989) it is possible to discover cointegration among sets of variables that are integrated of orders. This takes place when there are other than two variables. This is backed up by Harris (1995: 21). Vector Auto-regression (VAR) Vector autoregressions (VARs) were introduced into empirical economics by Sims (1980), who demonstrated that VARs offer a flexible and tractable framework for analyzing economic time series. Vector Auto-regression (VAR) can be an econometric model has been utilized primarily in macroeconomics to capture the connection and independencies between important economic variables. As outlined by Brooks and Tsolacos (2010) one benefit of VAR modeling is the fact that all of the variables are endogenous. Consequently organic meat is capable of capture more features of the results so we are able to use OLS separately on each equation. Brooks and Tsolacos (2010) also talk about Sims (1972) and Mcnees (1986) that VAR models often perform a lot better than traditional structural models. Additionally they indicate some disadvantages, one of these being that VAR models can be a-theoretical by nature. Lag-length determination is a concern critical to finding the most beneficial VAR specification. They cannot rely heavily on economic theory except for selecting variables to be within the VARs. The VAR can be viewed as a method of conducting causality tests, or even more specifically Granger causality tests. VAR can often test the Causality as; Granger-Causality makes it necessary that lagged values of variable X matched to subsequent values in variable Y, keeping constant the lagged values of variable Y and some other explanatory variables. In association with Granger causality, VAR model gives a natural framework to try the Granger causality in between each pair of variables. VAR model estimates and describe the relationships and dynamics of a set of endogenous variables. For a set of n time series variables yt = (y1t, y2t, ymt), a VAR model of order p (VAR (p)) can be written as: yt à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ½Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  A0 à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  A1 yt-1 à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  A2 yt-2 à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ®Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ®Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ®Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ap yt-p à ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ «Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚  Ãƒâ€šÃ‚ Ãƒâ€šÃ‚ ¥t For just a set of n time series variables yt = (y1t, y2t, ymt), a VAR type of order p (VAR (p)) can be written as: yt = A0 + A1 yt-1 + A2 yt-2 + + Ap yt-p + et Where, p = the quantity of lags to get considered from the system. n = the amount of variables to become considered in the system. yt is definitely an (n.1) vector containing each of the n variables in the VAR. A0 is surely an (n.1) vector of intercept terms. Ai is usually an (n.n) matrix of coefficients. ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt is usually an (n.1) vector of error terms. A critical take into account the specification of VAR models will be the resolution of the lag length of the VAR. Various lag length selection criteria are defined by different authors like, Akaikes (1969) final pred iction error (FPE), Akaike Information Criterion (AIC) suggested by Akaike (1974), Schwarz Criterion (SC) (1978) and Hannan-Quinn Information Criterion (HQ) (1979). Impulse response functions An impulse response function (IRF) traces the consequences of the one-time shock one on the innovations on current and future values with the endogenous variables. If your innovations ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt is contemporaneously uncorrelated, the interpretation on the impulse fact is straightforward. The ith innovation ÃÆ'Ã… ½Ãƒâ€šÃ‚ µi, t is only a shock for the ith endogenous variable yi,t. In accordance with Runkle (1987), reporting impulse response functions without standard error bars matches reporting regression coefficients without t-statistics. In numerous empirical studies impulse response functions are already utilized to distinguish temporal from permanent shocks (Bayoumi and Eichengreen, 1994), in your case theyll be helpful to determine the extent to which every endogenous variable reacts for an innovation of each one variable. Traditionally, VAR studies do not report estimated parameters or standard test statistics. Coefficients of estimated VAR systems are thoug ht of little utilization in themselves plus the high (i.e. P ÃÆ'Æ’- (k ÃÆ'Æ’- k) autoregressive coefficients) number of them will not invite for individual reporting. Instead, the approach of Sims (1980) is usually employed to summarize the estimated VAR systems by IRF. IRF traces out of the effect of your exogenous shock or an innovation in the endogenous variable on each of the endogenous variables in the system as time passes, to provide an answer towards following question: Is there a effect of any shock of size ÃÆ'Ã… ½Ãƒâ€šÃ‚ ´ within the system at time t about the state with the system at time t + ÃÆ' Ãƒ ¢Ã¢â€š ¬Ã… ¾, without other shocks? Especially, VARs impulse responses mainly examine the way the dependent variables respond to shocks from each independent variable. The accumulated link between units impulses are measured by appropriate summation with the coefficients of the impulse response functions (Lin 2006). However, Lutkepohl and Reimers (1992) stated the traditional impulse response analysis requires orthogonalization of shocks. And also the results vary with the ordering of the variables inside VAR. The greater correlations between residuals are, a lot more important the variable ordering is. So as to overcome this challenge, Pesaran and Shin (1998) developed the generalized impulse response functions which adjust the influence of any different ordering with the variables on impulse response functions. To spot orthogonalised innovations in each one of the variables as well as the dynamic responses to such innovations, the variance-covariance matrix from the VAR was factorized when using the Cholesky decomposition method suggested by Doan (1992). This process imposes an ordering on the variables within the VAR and attributes every one of the outcomes of any common components towards first variable within the VAR system. The impulse response functions are generated by way of a Vector Moving Average (VMA), a representation o f any VAR in standard form with regards to current and past values of the innovations (ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt). We derive the VMA, assuming you can find just one lag without constant term. yt = ÃÆ'Ã… ½Ãƒâ€šÃ‚  0 + ÃÆ'Ã… ½Ãƒâ€šÃ‚  1yt-1 +ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt ÃÆ'Ã… ½Ãƒâ€šÃ‚  1 is really a matrix of coefficients in the reduced form and ÃÆ'Ã… ½Ãƒâ€šÃ‚  0 is usually a vector of constants. Lagging this method one period and substituting for yt-1: yt = ÃÆ'Ã… ½Ãƒâ€šÃ‚  0 + ÃÆ'Ã… ½Ãƒâ€šÃ‚  1 (ÃÆ'Ã… ½Ãƒâ€šÃ‚  0 + ÃÆ'Ã… ½Ãƒâ€šÃ‚  1 yt-2 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt-1) + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt = (I + ÃÆ'Ã… ½Ãƒâ€šÃ‚  1) ÃÆ'Ã… ½Ãƒâ€šÃ‚  0 + t-2 + ÃÆ'Ã… ½Ãƒâ€šÃ‚  1ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt-1 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt if we go on substituting n times, eventually we have the following expression: yt = (I+ÃÆ'Ã… ½Ãƒâ€šÃ‚  1 +ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ +0+t-n+1 + t-i

Breaking Down the Pros and Cons of Grade Retention

Grade retention is a process in which a teacher believes that it would benefit a student to keep them in the same grade for two consecutive years. Retaining a student is not an easy decision and should not be taken lightly. Parents often find the decision agonizing, and it can be difficult for some parents to climb entirely on board. It is necessary to note that any retention decision should be made after much evidence is collected and after several meetings with parents. It is essential that you do not spring it on them at the final parent/teacher conference of the year. If grade retention is a possibility, it should be brought up early in the school year. However, intervention and frequent updates should be the focal point for most of the year. What Are Some Reasons to Retain a Student? There are many reasons that a teacher may feel that retention is necessary for a particular student. The biggest reason is typically the development level of a child. Students enter school at around the same chronological age but with ​varying developmental levels. If a teacher believes that a student is behind developmentally compared to the majority of students in their class, then they may wish to retain the student to give them â€Å"the grace of time† to mature and catch up developmentally. Teachers may also choose to retain a student because they simply struggle academically when compared to students at the same grade level. While this is a traditional reason for retention, it is necessary to note that unless you figure out why the student is struggling, it is likely that the retention will do more harm than good. Another reason teachers often retain a student is due to the student’s lack of motivation to learn. Retention is often ineffective in this case as well. Student behavior can be another reason that a teacher chooses to retain a student. This is especially prevalent in lower grades. Poor behavior is often tied to the developmental level of the child. What Are Some Possible Positive Effects? The biggest positive effect of grade retention is that it provides students who are truly behind developmentally a chance to catch up. Those type of students will begin to thrive once they are developmentally on grade level. Being in the same grade two years in a row can also provide a student with some stability and familiarity, especially when it comes to the teacher and the room. Retention is most beneficial when the child that is retained receives intensive intervention specific to the areas in which they struggle throughout the retention year. What Are Some Possible Negative Effects? There are many adverse effects of retention. One of the biggest negative effects is that students who are retained are more likely to drop out of school eventually. It is also not an exact science. Research says that students are more negatively impacted by grade retention than they are positively affected by it. Grade retention can also have a profound impact on a student’s socialization. This becomes especially true for older students who have been with the same group of students for several years. A student who has been separated from their friends could become depressed and develop poor self-esteem. Students who are retained are likely physically bigger than their classmates because they are a year older. This often causes that child to be self-conscious. Students who are retained sometimes develop serious behavior issues, especially as they age. What Grade(s) Should You Retain a Student? The rule of thumb for retention is the younger, the better. Once students reach fourth grade, it becomes virtually impossible for retention to be a positive thing. There are always exceptions but, overall, retention should be primarily limited to early elementary school. There are so many factors that teachers need to look at in a retention decision. It is not an easy decision. Seek advice from other teachers and look at each student on a case-by-case basis. You could have two students who are remarkably similar developmentally but due to external factors, retention would only be appropriate for one and not the other. What Is the Process for a Student to be Retained? Each school district typically has its own retention policy. Some districts may oppose retention altogether. For districts that do not oppose retention, teachers need to make themselves familiar with their district’s policy. Regardless of that policy, there are several things a teacher needs to do to make the retention process much easier throughout the year. Identify struggling students within the first few weeks of school.Create an individualized intervention plan to meet that students individual learning needs.Meet with the parent within a month of initiating that plan. Be straightforward with them, provide them with strategies to implement at home, and be sure you let them know that retention is a possibility if significant improvements aren’t made over the course of the year.Adapt and change the plan if you are not seeing growth after a few months.Continuously update the parents on their child’s progress.Document everything, including meetings, strategies used, results, etc.If you do decide to retain, then follow all school policies and procedures dealing with retention. Be sure to monitor and comply with dates concerning retention as well. What Are Some Alternatives to Grade Retention? Grade retention is not the best remedy for every struggling student. Sometimes it may be as simple as providing a student with some counseling to get them going in the right direction. Other times it is won’t be that easy. Older students, in particular, need to be given some options when it comes to grade retention. Many schools provide summer school opportunities for students to attend and make improvements in the areas in which they struggle. Another alternative would be to place a ​student on a plan of study. A plan of study puts the ball in the student’s court sort of speak. A plan of study provides students with specific objectives that they must meet over the course of the year. It also provides assistance and increased accountability for the student. Finally, a plan of study details specific consequences for not meeting their specific objectives, including grade retention.