Fixed and random effects of panel data analysis

Fixed and random effects of panel data analysis Panel data (also known as longitudinal or cross-sectional time-series data) is a dataset in which the behavior of entities are observed across time. With panel data you can include variables at different levels of analysis (i.e. students, schools, districts, states) suitable for multilevel or hierarchical modeling. In this document we focus on two techniques use to analyze panel data:_DONE_ Fixed effects Random effects FE explore the relationship between predictor and outcome variables within an entity (country, person, company, etc.). Each entity has its own individual characteristics that may or may not influence the predictor variables (for example being a male or female could influence the opinion toward certain issue or the political system of a particular country could have some effect on trade or GDP or the business practices of a company may influence its stock price). When using FE we assume that something within the individual may impact or bias the predictor or outcome variables and we need to control for this. This is the rationale behind the assumption of the correlation between entitys error term and predictor variables. FE remove the effect of those time-invariant characteristics from the predictor variables so we can assess the predictors net effect. _DONE_ Another important assumption of the FE model is that those time-invariant characteristics are unique to the individual and should not be correlated with other individual characteristics. Each entity is different therefore the entitys error term and the constant (which captures individual characteristics) should not be correlated with the others. If the error terms are correlated then FE is no suitable since inferences may not be correct and you need to model that relationship (probably using random-effects), this is the main rationale for the Hausmantest (presented later on in this document). The equation for the fixed effects model becomes: Yit= ÃŽÂ ²1Xit+ ÃŽÂ ±i+ uit[eq.1] Where ÃŽÂ ±i(i=1à ¢Ã¢â€š ¬Ã‚ ¦.n) is the unknown intercept for each entity (nentity-specific intercepts). Yitis the dependent variable (DV) where i= entity and t= time. Xitrepresents one independent variable (IV), ÃŽÂ ²1 is the coefficient for that IV, uitis the error term _DONE_ Random effects assume that the entitys error term is not correlated with the predictors which allows for time-invariant variables to play a role as explanatory variables. In random-effects you need to specify those individual characteristics that may or may not influence the predictor variables. The problem with this is that some variables may not be available therefore leading to omitted variable bias in the model. RE allows to generalize the inferences beyond the sample used in the model. To decide between fixed or random effects you can run a Hausman test where the null hypothesis is that the preferred model is random effects vs. the alternative the fixed effects (see Green, 2008, chapter 9). It basically tests whether the unique errors (ui) are correlated with the regressors, the null hypothesis is they are not. Testing for random effects: Breusch-Pagan Lagrange multiplier (LM)The LM test helps you decide between a random effects regression and a simple OLS regression. The null hypothesis in the LM test is that variances across entities is zero. This is, no significant difference across units (i.e. no panel effect). Here we failed to reject the null and conclude that random effects is not appropriate. This is, no evidence of significant differences across countries, therefore you can run a simple OLS regression. EC968 Panel Data Analysis Steve Pudney ISER University of Essex 2007 Panel data are a form of longitudinal data, involving regularly repeated observations on the same individuals Individuals may be people, households, firms, areas, etc Repeat observations may be different time periods or units within clusters (e.g. workers within firms; siblings within twin pairs)+DONE_ Some terminology A balanced panel has the same number of time observations (T) on each of the n individuals An unbalanced panel has different numbers of time observations (Ti) on each individual A compact panel covers only consecutive time periods for each individual there are no gaps Attrition is the process of drop-out of individuals from the panel, leading to an unbalanced and possibly non-compact panel A short panel has a large number of individuals but few time observations on each, (e.g. BHPS has 5,500 households and 13 waves) A long panel has a long run of time observations on each individual, permitting separate time-series analysis for each_DONE_ Advantages of panel data With panel data: à ¢Ã¢â€š ¬Ã‚ ¢ We can study dynamics à ¢Ã¢â€š ¬Ã‚ ¢ The sequence of events in time helps to reveal causation à ¢Ã¢â€š ¬Ã‚ ¢ We can allow for time-invariant unobservable variables BUTà ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¢ Variation between people usually far exceeds variation over time for an individual à ¢Ã¢â‚¬ ¡Ã¢â‚¬â„¢ a panel with T waves doesnt give T times the information of a cross-section à ¢Ã¢â€š ¬Ã‚ ¢ Variation over time may not exist or may be inflated by measurement error à ¢Ã¢â€š ¬Ã‚ ¢ Panel data imposes a fixed timing structure; continuoustime survival analysis may be more informative Panel Data Analysis Advantages and Challenges Cheng Hsiao May 2006 IEPR WORKING PAPER 06.49 Panel data or longitudinal data typically refer to data containing time series observations of a number of individuals. Therefore, observations in panel data involve at least two dimensions; a cross-sectional dimension, indicated by subscript i, and a time series dimension, indicated by subscript t. However, panel data could have a more complicated clustering or hierarchical structure. For instance, variable y may be the measurement of the level of air pollution at station _ in city j of country i at time t (e.g. Antweiler (2001), Davis (1999)). For ease of exposition, I shall confine my presentation to a balanced panel involving N cross-sectional units, i = 1, . . .,N, over T time periods, t = 1, . . ., T._DONE_ There are at least three factors contributing to the geometric growth of panel data studies. (i) data availability, (ii) greater capacity for modeling the complexity of human behavior than a single cross-section or time series data, and (iii) challenging methodology. Advantages of Panel Data Panel data, by blending the inter-individual differences and intra-individual dynamics have several advantages over cross-sectional or time-series data: (i) More accurate inference of model parameters. Panel data usually contain more degrees of freedom and more sample variability than cross-sectional data which may be viewed as a panel with T = 1, or time series data which is a panel with N = 1, hence improving the efficiency of econometric estimates (e.g. Hsiao, Mountain and Ho-Illman (1995)._DONE_ (ii) Greater capacity for capturing the complexity of human behavior than a single cross-section or time series data. These include: (ii.a) Constructing and testing more complicated behavioral hypotheses. For instance, consider the example of Ben-Porath (1973) that a cross-sectional sample of married women was found to have an average yearly labor-force participation rate of 50 percent. These could be the outcome of random draws from a homogeneous population or could be draws from heterogeneous populations in which 50% were from the population who always work and 50% never work. If the sample was from the former, each woman would be expected to spend half of her married life in the labor force and half out of the labor force. The job turnover rate would be expected to be frequent and 3 the average job duration would be about two years. If the sample was from the latter, there is no turnover. The current information about a womans work status is a perfect predictor of her future work status. A cross-sectional data is not able to distinguish between these two possibilities, but panel data can because the sequential observations for a number of women contain information about their labor participation in different subintervals of their life cycle. Another example is the evaluation of the effectiveness of social programs (e.g. Heckman, Ichimura, Smith and Toda (1998), Hsiao, Shen, Wang and Wang (2005), Rosenbaum and Rubin (1985). Evaluating the effectiveness of certain programs using cross-sectional sample typically suffers from the fact that those receiving treatment are different from those without. In other words, one does not simultaneously observe what happens to an individual when she receives the treatment or when she does not. An individual is observed as either receiving treatment or not receiving treatment. Using the difference between the treatment group and control group could suffer from two sources of biases, selection bias due to differences in observable factors between the treatment and control groups and selection bias due to endogeneity of participation in treatment. For instance, Northern Territory (NT) in Australia decriminalized possession of small amount of marijuana in 1996. Evaluating the effects of decriminalization on marijuana smoking behavior by comparing the differences between NT and other states that were still non-decriminalized could suffer from either or both sorts of bias. If panel data over this time period are available, it would allow the possibility of observing the before- and affect-effects on individuals of decriminalization as well as providing the possibility of isolating the effects of treatment from other factors affecting the outcome. 4 (ii.b) Controlling the impact of omitted variables. It is frequently argued that the real reason one finds (or does not find) certain effects is due to ignoring the effects of certain variables in ones model specification which are correlated with the included explanatory variables. Panel data contain information on both the intertemporal dynamics and the individuality of the entities may allow one to control the effects of missing or unobserved variables. For instance, MaCurdys (1981) life-cycle labor supply model under certainty implies that because the logarithm of a workers hours worked is a linear function of the logarithm of her wage rate and the logarithm of workers marginal utility of initial wealth, leaving out the logarithm of the workers marginal utility of initial wealth from the regression of hours worked on wage rate because it is unobserved can lead to seriously biased inference on the wage elasticity on hours worked since initial wealth is likely to be correlated with wage rate. However, since a workers marginal utility of initial wealth stays constant over time, if time series observations of an individual are available, one can take the difference of a workers labor supply equation over time to eliminate the effect of marginal utility of initial wealth on hours worked. The rate of change of an individuals hours worked now depends only on the rate of change of her wage rate. It no longer depends on her marginal utility of initial wealth._DONE_ (ii.c) Uncovering dynamic relationships. Economic behavior is inherently dynamic so that most econometrically interesting relationship are explicitly or implicitly dynamic. (Nerlove (2002)). However, the estimation of time-adjustment pattern using time series data often has to rely on arbitrary prior restrictions such as Koyck or Almon distributed lag models because time series observations of current and lagged variables are likely to be highly collinear (e.g. Griliches (1967)). With panel 5 data, we can rely on the inter-individual differences to reduce the collinearity between current and lag variables to estimate unrestricted time-adjustment patterns (e.g. Pakes and Griliches (1984))._DONE_ (ii.d) Generating more accurate predictions for individual outcomes by pooling the data rather than generating predictions of individual outcomes using the data on the individual in question. If individual behaviors are similar conditional on certain variables, panel data provide the possibility of learning an individuals behavior by observing the behavior of others. Thus, it is possible to obtain a more accurate description of an individuals behavior by supplementing observations of the individual in question with data on other individuals (e.g. Hsiao, Appelbe and Dineen (1993), Hsiao, Chan, Mountain and Tsui (1989)). (ii.e) Providing micro foundations for aggregate data analysis. Aggregate data analysis often invokes the representative agent assumption. However, if micro units are heterogeneous, not only can the time series properties of aggregate data be very different from those of disaggregate data (e.g., Granger (1990); Lewbel (1992); Pesaran (2003)), but policy evaluation based on aggregate data may be grossly misleading. Furthermore, the prediction of aggregate outcomes using aggregate data can be less accurate than the prediction based on micro-equations (e.g., Hsiao, Shen and Fujiki (2005)). Panel data containing time series observations for a number of individuals is ideal for investigating the homogeneity versus heterogeneity issue. (iii) Simplifying computation and statistical inference. Panel data involve at least two dimensions, a cross-sectional dimension and a time series dimension. Under normal circumstances one would expect that the 6 computation of panel data estimator or inference would be more complicated than cross-sectional or time series data. However, in certain cases, the availability of panel data actually simplifies computation and inference. For instance: (iii.a) Analysis of nonstationary time series. When time series data are not stationary, the large sample approximation of the distributions of the least-squares or maximum likelihood estimators are no longer normally distributed, (e.g. Anderson (1959), Dickey and Fuller (1979,81), Phillips and Durlauf (1986)). But if panel data are available, and observations among cross-sectional units are independent, then one can invoke the central limit theorem across cross-sectional units to show that the limiting distributions of many estimators remain asymptotically normal (e.g. Binder, Hsiao and Pesaran (2005), Levin, Lin and Chu (2002), Im, Pesaran and Shin (2004), Phillips and Moon (1999)). (iii.b) Measurement errors. Measurement errors can lead to under-identification of an econometric model (e.g. Aigner, Hsiao, Kapteyn and Wansbeek (1985)). The availability of multiple observations for a given individual or at a given time may allow a researcher to make different transformations to induce different and deducible changes in the estimators, hence to identify an otherwise unidentified model (e.g. Biorn (1992), Griliches and Hausman (1986), Wansbeek and Koning (1989)). (iii.c) Dynamic Tobit models. When a variable is truncated or censored, the actual realized value is unobserved. If an outcome variable depends on previous realized value and the previous realized value are unobserved, one has to take integration over the truncated range to obtain the likelihood of observables. In a dynamic framework with multiple missing values, the multiple 7 integration is computationally unfeasible. With panel data, the problem can be simplified by only focusing on the subsample in which previous realized values are observed (e.g. Arellano, Bover, and Labeager (1999)). The advantages of random effects (RE) specification are: (a) The number of parameters stay constant when sample size increases. (b) It allows the derivation of efficient 10 estimators that make use of both within and between (group) variation. (c) It allows the estimation of the impact of time-invariant variables. The disadvantage is that one has to specify a conditional density of ÃŽÂ ±i given x Ëœ _ i = (x Ëœ it, . . ., x ËœiT ), f(ÃŽÂ ±i | x Ëœ i), while ÃŽÂ ±i are unobservable. A common assumption is that f(ÃŽÂ ±i | x Ëœi) is identical to the marginal density f(ÃŽÂ ±i). However, if the effects are correlated with x Ëœit or if there is a fundamental difference among individual units, i.e., conditional on x Ëœit, yit cannot be viewed as a random draw from a common distribution, common RE model is misspecified and the resulting estimator is biased. The advantages of fixed effects (FE) specification are that it can allow the individualand/ or time specific effects to be correlated with explanatory variables x Ëœ it. Neither does it require an investigator to model their correlation patterns. The disadvantages of the FE specification are: (a) The number of unknown parameters increases with the number of sample observations. In the case when T (or N for ÃŽÂ »t) is finite, it introduces the classical incidental parameter problem (e.g. Neyman and Scott (1948)). (b) The FE estimator does not allow the estimation of the coefficients that are time-invariant. In order words, the advantages of RE specification are the disadvantages of FE specification and the disadvantages of RE specification are the advantages of FE specification. To choose between the two specifications, Hausman (1978) notes that if the FE estimator (or GMM), ˆ ÃƒÅ½Ã‚ ¸_DONE_ ËœFE, is consistent whether ÃŽÂ ±i is fixed or random and the commonly used RE estimator (or GLS), ˆ ÃƒÅ½Ã‚ ¸ ËœRE, is consistent and efficient only when ÃŽÂ ±i is indeed uncorrelated with x Ëœit and is inconsistent if ÃŽÂ ±i is correlated with x Ëœit. The advantage of RE specification is that there is no incidental parameter problem. The problem is that f(ÃŽÂ ±i | x Ëœ i) is in general unknown. If a wrong f(ÃŽÂ ±i | x Ëœi) is postulated, maximizing the wrong likelihood function will not yield consistent estimator of ÃŽÂ ² Ëœ . Moreover, the derivation of the marginal likelihood through multiple integration may be computationally infeasible. The advantage of FE specification is that there is no need to specify f(ÃŽÂ ±i | x Ëœ i). The likelihood function will be the product of individual likelihood (e.g. (4.28)) if the errors are i.i.d. The disadvantage is that it introduces incidental parameters. Longitudinal (Panel and Time Series Cross-Section) Data Nathaniel Beck Department of Politics NYU New York, NY 10012 [emailprotected] http://www.nyu.edu/gsas/dept/politics/faculty/beck/beck home.html Jan. 2004 What is longitudinal data? Observed over time as well as over space. Pure cross-section data has many limitations (Kramer, 1983). Problem is that only have one historical context. (Single) time series allows for multiple historical context, but for only one spatial location. Longitudinal data repeated observations on units observed over time Subset of hierarchical data observations that are correlated because there is some tie to same unit. E.g. in educational studies, where we observe student i in school u. Presumably there is some tie between the observations in the same school. In such data, observe yj,u where u indicates a unit and j indicates the jth observation drawn from that unit. Thus no relationship between yj,u and yj,u0 even though they have the same first subscript. In true longitudinal data, t represents comparable time. Generalized Least Squares An alternative is GLS. If is known (up to a scale factor), GLS is fully efficient and yields consistent estimates of the standard errors. The GLS estimates of _ are given by (X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X) à ¢Ã‹â€ Ã¢â‚¬â„¢1X0à ¢Ã‹â€ Ã¢â‚¬â„¢1Y (14) with estimated covariance matrix (X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X) à ¢Ã‹â€ Ã¢â‚¬â„¢1 . (15) (Usually we simplify by finding some trick to just do a simple transform on the observations to make the resulting variance-covariance matrix of the errors satisfy the Gauss-Markov assumptions. Thus, the common Cochrane-Orcutt transformation to eliminate serial correlation of the errors is almost GLS, as is weighted regression to eliminate heteroskedasticity.) The problem is that is never known in practice (even up to a scale factor). Thus an estimate of , ˆ , is used in Equations 14 and 15. This procedure, FGLS, provides consistent estimates of _ if ˆ  is estimated by residuals computed from consistent estimates of _; OLS provides such consistent estimates. We denote the FGLS estimates of _ by Ëœ_. In finite samples FGLS underestimates sampling variability (for normal errors). The basic insight used by Freedman and Peters is that X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X is a (weakly) concave function of . FGLS uses an estimate of , ˆ , in place of the true . As a consequence, the expectation of the FGLS variance, over possible realizations of ˆ , will be less than the variance, computed with the . This holds even if ˆ  is a consistent estimator of . The greater the variance of ˆ , the greater the downward bias. This problem is not severe if there are only a small number of parameters in the variance-covariance matrix to be estimated (as in Cochrane-Orcutt) but is severe if there are a lot of parameters relative to the amount of data. Beck TSCS Winter 2004 Class 1 8 ASIDE: Maximum likelihood would get this right, since we would estimate all parameters and take those into account. But with a large number of parameters in the error process, we would just see that ML is impossible. That would have been good. PANEL DATA ANALYSIS USING SAS ABU HASSAN SHAARI MOHD NOR Faculty of Economics and Business Universiti Kebangsaan Malaysia [emailprotected] FAUZIAH MAAROF Faculty of Science Universiti Putra Malaysia [emailprotected] 2007 Advantages of panel data According to Baltagi (2001) there are several advantages of using panel data as compared to running the models using separate time series and cross section data. They are as follows: Large number of data points 2)Increase degrees of freedom reduce collinearity 3) Improve efficiency of estimates and 4) Broaden the scope of inference The Econometrics of Panel Data Michel Mouchart 1 Institut de statistique Università © catholique de Louvain (B) 3rd March 2004 1 text book Statistical modelling : benefits and limita- tions of panel data 1.5.1 Some characteristic features of P.D. Object of this subsection : features to bear in mind when modelling P.D. à ¢Ã¢â€š ¬Ã‚ ¢ Size : often N (] of individual(s)) is large Ti (size of individual time series) is small thus:N >> Ti BUT this is not always the case ] of variables is large (often: multi-purpose survey) à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ Sampling : often individuals are selected randomly Time is not rotating panels split panels _ : individuals are partly renewed at each period à ¢Ã¢â€š ¬Ã‚ ¢ à ¢Ã¢â€š ¬Ã‚ ¢ à ¢Ã¢â€š ¬Ã‚ ¢ non independent data among data relative to a same individual: because of unobservable characteristics of each individual among individuals : because of unobservable characteristics common to several individuals between time periods : because of dynamic behaviour CHAPTER 1. INTRODUCTION 10 1.5.2 Some benefits from using P.D. a) Controlling for individual heterogeneity Example : state cigarette demand (Baltagi and Levin 1992) à ¢Ã¢â€š ¬Ã‚ ¢ Unit : 46 american states à ¢Ã¢â€š ¬Ã‚ ¢ Time period : 1963-1988 à ¢Ã¢â€š ¬Ã‚ ¢ endogenous variable : cigarette demand à ¢Ã¢â€š ¬Ã‚ ¢ explanatory variables : lagged endogenous, price, income à ¢Ã¢â€š ¬Ã‚ ¢ consider other explanatory variables : Zi : time invariant religion ( ± stable over time) education etc. Wt state invariant TV and radio advertising (national campaign) Problem : many of these variables are not available This is HETEROGENEITY (also known as frailty) (remember !) omitted variable ) bias (unless very specific hypotheses) Solutions with P.D. à ¢Ã¢â€š ¬Ã‚ ¢ dummies (specific to i and/or to t) WITHOUT killing the data à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ differences w.r.t. to i-averages i.e. : yit 7! (yit à ¢Ã‹â€ Ã¢â‚¬â„¢  ¯yi.)_DONE_ CHAPTER 1. INTRODUCTION 11 b) more information data sets à ¢Ã¢â€š ¬Ã‚ ¢ larger sample size due to pooling _ individual time dimension In the balanced case: NT observations In the unbalanced case: P1_i_N Ti observations à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ more variability ! less collinearity (as is often the case in time series) often : variation between units is much larger than variation within units_DONE_ c) better to study the dynamics of adjustment à ¢Ã¢â€š ¬Ã‚ ¢ distinguish repeated cross-sections : different individuals in different periods panel data : SAME individuals in different periods à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ cross-section : photograph at one period repeated cross-sections : different photographs at different periods only panel data to model HOW individuals ajust over time . This is crucial for: policy evaluation life-cycle models intergenerational models_DONE_ CHAPTER 1. INTRODUCTION 12 d) Identification of parameters that would not be identified with pure cross-sections or pure time-series: example 1 : does union membership increase wage ? P.D. allows to model BOTH union membership and individual characteristics for the individuals who enter the union during the sample period. example 2 : identifying the turn-over in the female participation to the labour market. Notice: the female, or any other segment ! i.e. P.D. allows for more sophisticated behavioural models e) à ¢Ã¢â€š ¬Ã‚ ¢ estimation of aggregation bias à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ often : more precise measurements at the micro level Comparing the Fixed Effect and the Ran- dom Effect Models 2.4.1 Comparing the hypotheses of the two Models The RE model and the FE model may be viewed within a hierarchical specification of a unique encompassing model. From this point of view, the two models are not fundamentally different, they rather correspond to different levels of analysis within a unique hierarchical framework. More specifically, from a Bayesian point of view, where all the variables (latent or manifest) and parameters are jointly endowed with a (unique) probability measure, one CHAPTER 2. ONE-WAY COMPONENT REGRESSION MODEL 37 may consider the complete specification of the law of (y, ÃŽÂ ¼, _ | Z, ZÃŽÂ ¼) as follows: (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) _ N( Z_ _ + ZÃŽÂ ¼ÃƒÅ½Ã‚ ¼, _2 I(NT)) (2.64) (ÃŽÂ ¼ | _, Z, ZÃŽÂ ¼) _ N(0, _2 ÃŽÂ ¼ I(N)) (2.65) (_ | Z, ZÃŽÂ ¼) _ Q (2.66) where Q is an arbitrary prior probability on _ = (_, _2 , _2 ÃŽÂ ¼). Parenthetically, note that this complete specification assumes: y _2 ÃŽÂ ¼ | ÃŽÂ ¼, _, _2 , Z, ZÃŽÂ ¼ ÃŽÂ ¼(_, Z, ZÃŽÂ ¼) | _2 ÃŽÂ ¼ The above specification implies: (y | _, Z, ZÃŽÂ ¼) _ N( Z_ _ , _2 ÃŽÂ ¼ ZÃŽÂ ¼ Z0ÃŽÂ ¼ + _2 I(NT)) (2.67) Thus the FE model, i.e. (2.64), considers the distribution of (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) as the sampling distribution and the distributions of (ÃŽÂ ¼ | _, Z, ZÃŽÂ ¼) and (_ | Z, ZÃŽÂ ¼) as prior specification. The RE model, i.e. (2.67), considers the distribution of (y | _, Z, ZÃŽÂ ¼) as the sampling distribution and the distribution of (_ | Z, ZÃŽÂ ¼) as prior specification. Said differently, in the RE model, ÃŽÂ ¼ is treated as a latent (i.e. not obervable) variable whereas in the FE model ÃŽÂ ¼ is treated as an incidental parameter. Moreover, the RE model is obtained from the FE model through a marginalization with respect to ÃŽÂ ¼. These remarks make clear that the FE model and the RE model should be expected to display different sampling properties. Also, the inference on ÃŽÂ ¼ is an estimation problem in the FE model whereas it is a prediction problem in the RE model: the difference between these two problems regards the difference in the relevant sampling properties, i.e. w.r.t. the distribution of (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) or of (y | _, Z, ZÃŽÂ ¼), and eventually of the relevant risk functions, i.e. the sampling expectation of a loss due to an error between an estimated value and a (fixed) parameter or between a predicted value and the realization of a (latent) random variable. This fact does however not imply that both levels might be used indifferently. Indeed, from a sampling point of view: (i) the dimensions of the parameter spaces are drastically different. In the FE model, when N , the number of individuals, increases, the ÃŽÂ ¼i s being CHAPTER 2. ONE-WAY COMPONENT REGRESSION MODEL 38 incidental parameters also increases in number: each new individual introduces a new parameter.

The Importance of Teaching Mathematics and Science in English (Ppsmi)

UNIVERSITI TEKNOLOGI MARA SARAWAK English for Academic Purpose (BEL 311) Prepared by, Lauraricka Brenda Benjamin 2010992969 IM 110 Group 3 B Title, The Importance of Teaching Mathematics and Science in English (PPSMI) Date of Submission, 16th of December, 2011 Lecturer’s name, Nurul Ain Binti Sulaiman The Importance of Teaching Mathematics and Science in English (PPSMI) IIntroduction The whole idea of a good education system is to prepare out children so that they are better prepared to face the challenges of globalisation, liberalisation as well as the fast development in today’s information and communication technologies. What it has done is given our students, from a very young age, a new sense of confidence when using the international language of communication and it given them a good basic grounding from which they can and they should, continue to improve their mastery of a major second language, apart from Bahasa Malaysia. Ministry of education malaysia, we are behind you. (2009). Retrieved November 11, 2011, from http://www. ppsmi. wordpress. com Aidila Razak. (2011, November 5). Page welcomes ppsmi decision, seeks details. Malaysia Kini. Retrieved November 1, 2011, from http://www. malaysiakini. com/news/180610 IIBody AOne reason for the importance of PPSMI is as the competitive advantage. 1. To pursue a high standard of English for Malaysia’s children to ensure that their future is secure. a)It is time that parents step up to the plate and make the future of their children the priority. b)It will help them a great deal in their future pursuit, whatever field they choose to pursue. c)If the government doesn't find a way to improve our poor English in school, we will be nowhere when countries like China catch up. Like it or not, we need to master English. (2011). Retrieved December 15, 2011, from http://malaysiakini. om/news/180639 Thesis Statement Topic Sentence B. Teaching Mathematics and Science using English as the medium of instruction at school-level, significantly improves the quality of learning experience leading to the improvement in the proficiency of learners in those two subjects. 1. Teaching school-goers Mathematics and Science in English will improve the student's understandin g in the two subjects. a)Most materials which integrate explanatory style of conveying information regarding scientific and mathematical matters are crated with English as the main medium of communication. )Teaching the subjects in English would have helped broaden students’ knowledge of new terminologies and improve their command of the language. (Cheah, 2009) Mozie, A. K. (2009, September 11). Digital Zone. Retrieved December 15, 2011, from http://forum. digitalpowered. net/index. php? showtopic=31621 Cheah, C. S. (2009, December 5). Parents unhappy over decision to revert. Retrieved December 15, 2011, from http://www. pagemalaysia. org/print. php? type=F&thread=29 Topic Sentence C. All Malaysians should be multilingual. 1. Students in Malaysia should know languages that are different from their ‘mother language’. This will be able them to talk many learn and know many languages that are useful when they pursue their study in high level especially in overseas institutions. a)English will help in communication b)Children learn languages much faster and can learn multiple languages when they are below 10. In fact, it is best before age 7. (Feizrul, as cited in Hiaw, 2011) Feizrul Nor Nurbi. (2011, November 1). PPSMI – quality vs. Language. Malaysia-today. Retrieved November 30, 2011, from http://www. alaysia-today. net/mtcolums/lettersurat/44624-PPSMI- quality-vs-language. IIIConclusion PPSMI was introduced not to improve the students' command of English but to ease the process of learning, understanding and acquiring knowledge in Science and Mathematics. (Goh, 2009) Goh, W. L. (2009). Teaching maths and science in english. Retrieved November 30, 2011, from http://drchua9. blogspot. co m/2009/07/teaching-maths-and-science-in-english. html Topic Sentence Restatement of the thesis ————————————————- References Aidila Razak. (2011, November 5). Page welcomes ppsmi decision, seeks details. Malaysia Kini. Retrieved November 1, 2011, from http://www. malaysiakini. com/news/180610 Cheah, C. S. (2009, December 5). Parents unhappy over decision to revert. Retrieved December 15, 2011, from http://www. pagemalaysia. org/print. php? type=F;thread=29 Feizrul Nor Nurbi. (2011, November 1). PPSMI – quality vs. Language. Malaysia-today. Retrieved November 30, 2011, from http://www. malaysia-today. net/mtcolums/lettersurat/44624-PPSMI- quality-vs-language. Goh, W. L. 2009). Teaching maths and science in english. Retrieved November 30, 2011, from http://drchua9. blogspot. com/2009/07/teaching-maths-and-science-in-english. html Like it or not, we need to master English. (2011). Retrieved December 15, 2011, from http://malaysiakini. com/news/180639 Ministry of education malaysia, we are behind you. (2009). Retrieved November 11, 2011, from http://www. ppsmi. wordpress. com Mozie, A. K. (2009, Septemb er 11). Digital Zone. Retrieved December 15, 2011, from http://forum. digitalpowered. net/index. php? showtopic=31621

Obsessive Compulsive Disorder ( Ocd ) - 1601 Words

Obsessive Compulsive Disorder: A Brief Overview Obsessive-compulsive disorder (OCD) is a psychiatric disorder that is characterized by constant intrusive thoughts (obsessions) that normally cause anxiety or distress which can only be alleviated by performing repetitive actions (compulsions) (Rahimi, Haghighi, and Shamsaei, 2015). Nearly everyone has experienced something similar to these obsessions and compulsions at some point in their life but this experience can only be labeled as OCD when it occurs at least one hour per day and correlates with high levels of distress (Abramowitz et al., 2010). It’s important to distinguish the differences between the actual psychological disorder and informal use of the term â€Å"OCD.† For example, many people claim they have â€Å"OCD† because they are particular about how something is done such as doing the dishes or making the bed in the morning. However, true sufferers of this disorder suffer from great anxiety and torment from their obsessions and ca nnot restrain themselves from giving in to their compulsions (Huffman Dowdell, 2004, p. 462). OCD, according to the World Health Organization, is among the top ten most disabling medical conditions (Rahimi et al., 2015). There are two main components to obsessive-compulsive disorder: obsessions and compulsions. The first component, obsessions, are unwanted intrusive thoughts, images, or urges that will constantly plague an individual’s mind and cause tem a great source of anxiety andShow MoreRelatedObsessive Compulsive Disorder (OCD)1756 Words   |  8 Pages Obsessive Compulsive Disorder (OCD) is a disorder that can affect children and adults. In order to fully understand OCD, many different areas of the disorder must be reviewed. First, OCD will be defined and the diagnosis criteria will be discussed. Secondly the prevalence of the disorder will be considered. The different symptoms, behaviors and means of treatment are also important aspects that will be discussed i n order to develop a clearer understanding of the implications of obsessive compulsiveRead MoreEssay on Obsessive Compulsive Disorder (OCD)875 Words   |  4 Pagessevere Obsessive Compulsive Disorder. Obsessive-compulsive disorder is an anxiety disorder that triggers people to have unwanted fixations and to repeat certain activities again and again. Everyone has habits or certain ways of doing something with Obsessive Compulsive Disorder these habits severely interrupt the way they live their lives (Familydoctor.org Editorial Staff). About one in 40 people suffer from some form of Obsessive Compulsive Disorder (ABRAMOWITZ). Obsessive Compulsive Disorder oftenRead MoreLiving With Obsessive Compulsive Disorder (OCD)1190 Words   |  5 Pagesdifferences between both symptoms and experiences of six different authors who have been personally affected by obsessive-compulsive disorder (OCD).   Since OCD is not very well understood by many members of the public (Escape), I hope that the experiences of the authors that I researched will be able to paint a vivid picture of what life with OCD is like. Obsessive-compulsive disorder involves a chemical imbalance in the brain. This chemical imbalance is thought to be the main reason for obsessionsRead MoreObsessive Compulsive Disorder (OCD) Essay2901 Words   |  12 Pages   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Obsessive Compulsive Disorder, or OCD, affects an average 1.7% of the population according to the Stanford University School of Medicine.  Ã‚  The recognition of this psychological disorder has grown in the recent years.  Ã‚  As the knowledge of this disorder becomes more prevalent, those suffering have become more willing to seek help (OCDA).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  OCD is a condition â€Å"in which people experience repetitive and upsetting thoughts and/or behaviors† (OCDA).  Ã‚  While there are many variationRead MoreObsessive Compulsive Disorder (OCD) Essay800 Words   |  4 Pages Obsessive Compulsive Disorder And Its Effect On Life Obsessive-compulsive disorder, or OCD, involves anxious thoughts or rituals one feels and cant control. . For many years, OCD was thought to be rare. The actual number of people with OCD was hidden, because people would hide their problem to avoid embarrassment. Some recent studies show that as many as 3 million Americans ages 18 to 54 may have OCD at any one time. This is about 2.3% of the people in this age group. It strikes men and women inRead More Obsessive Compulsive Disorder (OCD) Essays2616 Words   |  11 Pagesis a very powerful piece of structure; it is truly limitless when speaking about its potential. With a functional organ comes a dysfunctional possibility. Obsessive Compulsive Disorder, (OCD), for instance, is nervousness in the mind. OCD is an anxiety disorder caused by repetitive intrusive thoughts and behaviors. It is a mental disorder marked by the involvement of a devotion to an idea or routine. Essentially, it is a false core belief which is believing that there is something wrong, causingRead MoreEssay on Obsessive Compulsive Disorder (OCD)3370 Words   |  14 Pages Obsessive compulsive disorder is a disease that many people know of, but few people know about. Many people associate repeated washing of hands, or flicking of switches, and even cleanliness with Obsessive Compulsive Disorder (OCD), however there are many more symptoms, and there are also explanations for those symptoms. In this paper, I will describe what obsessive compulsive disorder is, explain some of the effects of it, and explain why it happens. I will also attempt to prove that while medicationRead More Obsessive Compulsive Disorder (OCD) Essay1758 Words   |  8 PagesOCD: Whats in Control? Obsessive Compulsive Disorder (OCD) is an anxiety disorder that is the fourth most common mental illness in the U.S. (8). OCD affects five million Americans, or one in five people (3). This is a serious mental disorder that causes people to think and act certain things repetitively in order to calm the anxiety produced by a certain fear. Unlike compulsive drinking or gambling, OCD compulsions do not give the person pleasure; rather, the rituals are performed to obtainRead MoreObsessive Compulsive Disorder (OCD) Essay examples1375 Words   |  6 PagesObsessive Compulsive Disorder â€Å"I know my hands are clean. I know that I have touched nothing dangerous. But†¦ I doubt my perception. Soon, if I do not wash, a mind numbing, searing anxiety will cripple me. A feeling of stickiness will begin to spread from the point of contamination and I will be lost in a place I do not want to go. So I wash until the feeling is gone, until the anxiety subsides. Then I feel defeated. So I do less and less, my world becomes smaller and smaller and more lonelyRead MoreEssay about Obsessive Compulsive Disorder (OCD)474 Words   |  2 Pages Obsessive Compulsive disorder (OCD) is a mental illness that effects nearly 5 million Americans, and half a million children. Its a disease that fills the brain with unwanted ideas, and worries. OCD is a diseases that effects the Cerebral frontal cortex. Unfortunately there is no cure for OCD. Obsessive compulsive disorder can start developing as early as age five. In most cases OCD controls your life. Through out the rest of this paper I hope to inform you on Obsessive compulsive Disorders

Analysis Of Grendel s Grendel - 977 Words

From the novel Grendel (Gardner, 1971), Grendel is one of the three antagonists along with his mother and the dragon. Grendel has the combination of human and beast; he emerge in a society that mocks and threats him these scenes features racism and class level in the nineteenth century. Grendel is an individual who pleased to coexist with humanity, but also the murderous brute who kills for no reason. Grendel hears noises from the meadhall as he scramble through the woods. The twelve years of war causes Grendel to attack Hrothgar’s meadhall, and coldheartedly ravages the reckless community; this reflects to the African Americans who risked their lives protesting for their rights. Grendel, the grotesque of the society has many diversities to his character; he characterizes race, culture, and power. John Gardner uses the society to emphasis Grendel as the binary figure, who is the Otherness. Grendel’s desperation of fitting in the Danes society makes him a boundary dwelle r who portrays to slavery in the nineteenth century and racism in the twentieth century. Grendel wants to comprehend with the society but both have an antagonistic relationship between one another. Grendel lives under the water contributes to how slaves lives at the farms like animals; this illustrates how they isolate themselves from their society. Grendel is at war with the Danes for twelve years. The African Americans fought over twelve decades for their rights and powers. In the beginning of the novel,Show MoreRelatedAnalysis Of Grendel s Grendel 728 Words   |  3 PagesMartin Mraz Common Core English IV March 9, 2017 Grendel’s Pain In John Gardner s Grendel, Grendel states, Tedium is the worst pain (138). Grendel views boredom as an expression of his painful, purposeless existence. Perpetual boredom has steadily increased and intensifies in chapter ten leading Grendel to be anxious for something to happen. Ironically, Grendel claims nothing is happening, and all is boring, yet filled with longing and expecting for something big to happen soon. Grendel’s searchRead MoreAnalysis Of Grendel s The Epic Of Grendel 1446 Words   |  6 PagesI could not help but to write you a letter after reading your own letter that you sent to Ms. Susie West and her students. Through your letter, you make several remarkable points about your book Grendel as well as the infamous title character; however, one thing stood out to me the most. In the 11th and 12th paragraphs of your letter, you mention a â€Å"real question†, talking about â€Å"†¦of there isn’t a reachable god, and if life has no inherent meaning how should one live†? The metaphorical cogs in myRead MoreAnalysis Of Grendel s Mother 1066 Words   |  5 PagesYou are having a tough time because of your husband! How dare he makes you naked in front of everyone and ride horse as a test? Although it is a test to make sure you are a righteous woman, but it is a shame to treat you like that. I know Hrothgar s queen is the one every man thinks of righteous and beautiful, I do not think she has any autonomy! Hildeburh, daughter of the Danish king Hoc also did not do anything but mourn when she her son and brother were â€Å"killed in the first battle with [her husband]†Read MoreAnalysis Of Grendel s The Elegy / Epic Of Beowulf 1840 Words   |  8 Pagesreferences consistently. The protagonist that starts of the epic, Grendel, refers to as a descendant of Cain. His â€Å"monstrous† actions are often a direct reflection of the story of Cain and his brother found in Genesis chapter four. Although Grende l is known as a heinous monster, he is an embodiment of the faults of humans and a victim that has fallen prey to the power of jealousy, and the need to be known within a community. In other words, Grendel is a symbol of the possible evils that may arise throughRead MoreAnalysis Of Grendel s The Of The World s Weird Wall 1673 Words   |  7 Pages FINAL GRENDEL IOP SCRIPT       John Champlin Gardner, a well-known novelist that explores philosophy within his works of fiction. He is most notably known for his 1971 book Grendel which is based on the Old English epic poem: Beowulf retold from the monster’s perspective. Within the book, Grendel is repeatedly referenced to be a â€Å"walker of the world’s weird wall† and is forced into a life of isolation, with no ulterior purpose. (FLIP SLIDE—THESIS) In Grendel, John Gardner emphasises the titleRead MoreGrendel1242 Words   |  5 PagesGrendel as a Ridiculous Character Probably one of the greatest questions of the 19th century comes directly from John Gardner’s novel Grendel. Given a world with no inherent meaning, how should one live his or her life? Grendel lives in a world that he is not supposed to be in, acting out on emotion. Grendel represents the animalistic traits of humans. His actions are primitive and based around society’s acceptance throughout the novel. Grendel portrays a ridiculous character that is convincedRead MoreAnalysis Of John Gardner s The And The Brave Universe 1642 Words   |  7 PagesJohn Gardner s brilliantly composed Grendel is an analysis on the benefits and defects of both sorts of perspective: the existentialist significance free universe, and the brave universe, where each activity is pervaded with reason and power. Without a doubt, the book brings up numerous philosophical issues as to the significance of life and also to the way people characterize themselves. Furthermore, Gardner depicts persist ent examination, and last endorsement, of existentialist perspectivesRead MoreThe Individual Versus His Environment in The Stranger and Grendel1674 Words   |  7 Pages The Individual Versus His Environment in The Stranger and Grendel Due to the multifaceted nature of literature, analysis thereof is prone to generalization. One of the most grievous generalizations oft encountered involves failing to distinguish between a character and the novel it inhabits. Take John Gardener’s Grendel and Albert Camus’s The Stranger, for instance. It’s far too easy, when analyzing for dominant ideologies, to slap them both with the label of existentialism and be done withRead MoreBeowulf s Last Battle Of The Epic Hero954 Words   |  4 Pages Fifty years after killing the evil Grendel and his mother, the epic hero, Beowulf, faces his third and final monster, a dragon that has been attacking Geatland. But this last battle will be completely different than the ones before, and spells the end for our epic hero. !!!Beowulf s Last Battle Starting around line 2200 of the great epic poem __ Beowulf __, we have the entrance of the last of the monsters that Beowulf must overcome as part of the epic hero cycle. But this battle is unlikeRead MoreBeowulf As An Epic Hero Essay1545 Words   |  7 Pagesdestroyed in a war when he was an infant and he was sent to live on earth. Living in an innocent world where villains such Lex Luthor, Darkseid, and Brainiac pose a threat, Superman seems to have no choice but to use his powers to protect everyone. It s only fair, considering the fact that he is the only being capable of doing so. Neither the police nor the military possesses the strength to take down these monsters. Even Batman initially didn’t have the desire to fight. It was the fact that he