Ch. 21 Univariate Unit Root process 1 Introduction Consider OLS estimation of a AR(1)process, Yt= pYt-1+ut where ut w ii d (0, 0), and Yo=0. The OLS estimator of p is given by and we also have

where a subscribed element of a matrix is always read as arou, column. Here we confine the element to be real number a vector is a matrix with one row or one column. Therefore a row vector is Alxk and a column vector is AixI and commonly denoted as ak and ai,respec- tively. In the followings of this course, we follow conventional custom to say that a vector is a columnvector except for

Ch. 18 Vector Time series 1 Introduction In dealing with economic variables often the value of one variables is not only related to its predecessors in time but, in addition, it depends on past values of other variables. This naturally extends the concept of univariate stochastic process to vector time series analysis. This chapter describes the dynamic in

Ch. 16 Stochastic Model Building Unlike linear regression model which usually has an economic theoretic model built somewhere in economic literature, the time series analysis of a stochastic process needs the ability to relating a stationary ARMA model to real data. It is usually best achieved by a three-stage

Ch. 14 Stationary ARMA Process a general linear stochastic model is described that suppose a time series to be generated by a linear aggregation of random shock. For practical representation it is desirable to employ models that use parameters parsimoniously. Parsimony may often be achieved by representation of the linear process in terms of a small number of autoregressive and moving

Ch. 12 Stochastic Process 1 Introduction a particularly important aspect of real observable phenomena, which the random variables concept cannot accommodate, is their time dimension; the concept of random variable is essential static. A number of economic phenomena for which we need to formulate probability models come in the form of dynamic processes

Ch. 10 Autocorrelated Disturbances In a time-series setting, a common problem is autocorrelation, or serial corre- lation of the disturbance across periods. See the plot of the residuals at Figure

第六次上机实习: Matlab绘图-II/ Simulink仿真I 按要求完成下述练习 利用Maab绘图工具 plottools完成以下绘图任务 1.在图形模板 figurepalette绘制直角坐标的二维曲线sin(X+cos (x+sin(x))) FH

Preface The essence Th This document is intended to be a compilation of tips and tricks mainly related to efficient ways of manipulating arrays in MATLAB. Here, \manipulating arrays\includes replicating and rotating arrays or parts of arrays, inserting, extracting replacing, permuting and shifting arrays or parts of arrays, generating combinations and permutations of elements, run-length encoding and decoding

1.x-y数据存在 finalprojectdata.txt文件中。确定拟合该数据的最低阶多项式。提示:调用 polyfit函数 2.确定拟合的最低阶多项式分别在x=3.5,x=.2.和x=11.1处的值(精确到小数点3位)。提示:调用 polyval函数 3.绘出x-y数据以及拟合的最低阶多项式确定的函数在区间010]上曲线图(加标注加以区分数据)