similar to: Autocorrelation using acf

Dear R list I am trying to understand the auto-correlation concept. Auto-correlation is the self-correlation of random variable X with a certain time lag of say t. The article "http://www.mit.tut.fi/MIT-3010/luentokalvot/lk10-11/MDA_lecture16_11.pdf" (Page no. 9 and 10) gives the methodology as under. Suppose you have a time series observations as say X =

Dear R users, suppose we have a random walk such as: v_t+1 = v_t + e_t+1 where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and v_t is the fundamental value of a stock. Now suppose I want a trading strategy to be: x_t+1 = c(v_t – p_t) where c is a costant. I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only

Dear Users, I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the innovations are supposed to follow a t-GARCH(1,1) proccess. By t-GARCH I want to mean that: e_t=n_t*sqrt(h_t) and h_t=ct2+a*(e_t)^2+b*h_t-1. where n_t is a random variable with t-Student distribution. If someone could give some guidelines, I can going developing the model. I did it in matlab, but the loops

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in

Dear R list, I have one very elementary question regrading correlation between two variables. x = c(44,46,46,47,45,43,45,44) y = c(44,43,41,41,46,48,44,43) > cov(x, y) [1] -2.428571 However, if I try to calculate the covariance using the formula as covariance = sum((x-mean(x))*(y-mean(y)))/8       # no of of paired obs. = 8 or     covariance = sum(x*y)/8-(mean(x)*mean(y)) gives

Dear R helpers Suppose I have a vector as vect1 = as.character(c("ABC", "XYZ", "LMN", "DEF")) > vect1 [1] "ABC" "XYZ" "LMN" "DEF" I want to reverse the order of this vector as vect2 = c("DEF", "LMN", "XYZ", "ABC") Kindly guide Regards Vincy [[alternative HTML

Dear R helpers I have a data which gives Month-wise and Rating-wise Rates. So the input file is something like month           rating           rate January        AAA             9.04 February      AAA             9.07 .......................................... .......................................... Decemeber     AAA            8.97  January           BBB           11.15 February        

Dear R helpers, xx = data.frame(country = c("USA", "UK", "Canada"), x = c(10, 50, 20), y = c(40, 80, 35), z = c(70, 62, 10)) > xx        country      x     y    z 1      USA        10    40  70 2      UK          50   80   62 3     Canada    20   35   10 I need to arrange this as a new data.frame as follows - country       type     values USA           

Dear R helpers I have a dataframe as df = data.frame(x = c(1, 14, 3, 21, 11), y = c(102, 500, 40, 101, 189)) > df    x   y 1  1 102 2 14 500 3  3  40 4 21 101 5 11 189 # Actually I am having dataframe having multiple columns. I am just giving an example. I need to subtract all the rows of df by the first row of df i.e. I need to subtract each element of 'x' column by 1. Likewise I

Dear R helpers I have following input f = c(257, 520, 110). I need to generate a decreasing sequence (decreasing by 100) which will give me an input (in a tabular form) like 257, 157, 57 520, 420, 320, 220, 120, 20 110, 10 I tried the following R code f = c(257, 520, 110) yy = matrix(data = NA, nrow = 3, ncol = 6) for (i in 1:3)      {      value = NULL      for (j in 1 : 6)           {

Dear R helpers Suppose, x = c(0,  1,  2,  3) y = c("A", "B", "C", "D") z = c(1, 3) For given values of z, I need to the values of y. So I should get "B" and "D". I tried doing y[x][z] but it gives > y[x][z] [1] "A" "C" Kindly guide. Regards Vincy [[alternative HTML version deleted]]

Dear R helpers, I have one basic doubt about the value of pi. In school, we have learned that pi = 22/7 (which is = 3.142857). However, if I type pi in R, I get pi = 3.141593. So which value of pi should be considered? Regards Vincy [[alternative HTML version deleted]]

Hi, I have one basic doubt. Suppose X ~ N(50,10). I need to calculate Probability X = 50. dnorm(50, 50, 10) gives me [1] 0.03989423 My understanding is (which is bit statistical or may be mathematical) on a continuous scale, Probability of the type P(X = .....) are nothing but 1/Infinity i.e. = 0. So as per my understanding P(X = 50) should be 0, but even excel also gives 0.03989422. Obviously

Dear R helpers, I have recently installed R version 2.15.0 I just wanted to calculate mean(16, 18) Surprisingly I got answer as > mean(16, 18) [1] 16 > mean(18, 16) [1] 18 > mean(14, 11, 17, 9, 5, 18) [1] 14 So instead of calculating simple Arithmetic average, mean command is generating first element as average. I restarted the machine, changed the machine, but still the

Hi, I want to fit a regression model with one independent variable. The error part should be fitted an ARMA process. For example, y_t = a + b*x_t + e_t where e_t is modelled as an ARMA process. Please let me know how do I do this in R. What code should I use? TIA Aditya [[alternative HTML version deleted]]

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

hi: you should probably send below to R-Sig-Finance because there are some econometrics people over there who could also possibly give you a good answer and may not see this email ? Also, there's package called mar ( I think that's the name ) that may do what you want ? Finally, I don't know how to do it but I think there are ways of converting a multivariate arima into the

Hi, I would like to estimate a VAR of the form: Ay_t = By_t-1 + Cy_t-2 + ... + Dx_t + e_t Where A is a non-diagonal matrix of coefficients, B and C are matricies of coefficients and D is a matrix of coefficients for the exogenous variables. I don't think the package {vars} can do this because I want to include contemporaneous cross-variable impacts. So I want y1_t to affect y2_t and I