similar to: pacf

Hi, I noticed that whenever I ran acf or pacf, the plot generated by R always includes two horizontal blue doted lines. Furthermore, these two lines are not documented in the acf documentation. I don't know what they are for, but it seems that they are important. Could someone tell me what they are and how are they calculated? Thanks, -- Tom [[alternative HTML version deleted]]

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hello, I have time series and need to draw simple and partial correlograms with associated Q-statistics (the same as in EViews). Can I do it in R? Thanks --------------------------------- [[alternate HTML version deleted]]

Hi there. Just a little status update how that integerization is coming along. I am trying to limit myself to 32 bit arithmetics. That means not using any __int64 or long long datatypes at any point. I have now replaced all steps up to including the estimation of the LPC filter coefficients with integer code. That is about a quarter of the total work completed, I would say. One problem that i

Hi All, I am doing econometric modeling of panel data (fixed effects). We currently use Eviews to do this, but I have discovered a bug in Eviews 7 and am exploring the use of R to build panel data models / longitudinal data models. I looked at the plm package but do not see how I can incorporate AR terms in the model using the plm package. I have an Eviews model with two AR terms, AR(1) and

hi, am trying to understand speex's algo. have a few questions. 1) autocorrelation: in the function, _spx_autocorr (for floating point version), there is a line ac[0] += 10; correct me if i am wrong, i suppose the addition of 10 is used to condition the autocorrelation matrix. wonder how the value of 10 is arrived at? 2) levinson durbin (LD) algo in the function _spx_lpc, i referred

Dear Sir, First of all Happy Holidays!,... I am writing to you because I am a bit confused about ARCH estimation. Is there a way to find what garch() exactly does, without the need of reading the source code (because I cannot understand it)? In Eviews (the results at the end) I am getting different results than in R (for those that have the program I do: Quick -> Estimage Equation ->

R: Is there an easy way to get the acf and pacf for an irregular times series? That is, the acf and pacf with lag lengths that are in units of time, not observation number. Thanks, Jason Higbee Research Associate Federal Reserve Bank of St. Louis The views expressed in this email are the author's and not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

I have used acf() and pacf() in R to get the acf and pacf values at max/lag=20 but the output did not show the values associated with lag numbers. lag numbers is shown in decimals. -- Rashid Ameer View my recent publication at * http://www.emeraldinsight.com/fwd.htm?id=aob&ini=aob&doi=10.1108/17538391211282854 * Details for my works are available directly at

Hi helpers, can you help me in plotting acf and pacf functions in R. I am using the code acf(variable name) but it is not working. Expecting your reply. Thanks -- View this message in context: http://n4.nabble.com/ACF-and-PACF-tp1477149p1477149.html Sent from the R help mailing list archive at Nabble.com.

> check the resulting file for subset blocksizes, sample rates, bits per sample, LPC filter order and rice partition order. That's all parameters that I need to check? FLAC tools have a lots of warnings about non-subset files during encoding, but unfortunately don't have easy way to check/test existing FLAC stream for subset compliance. "flac -a" generates the big text file

Dear list, according to the documentation of acf{ts} "the partial correlation coefficient is estimated by fitting autoregressive models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF

(Disclaimer: this is not an LPC vs. LSP question) After looking at the Vorbis code I was wondering why you were using LSP to quantize the spectral envelope instead of simply quantizing the cepstrum (DCT(log(envelope))) or modified cepstrum (DCT(envelope.^alpha)). To me it seems like when the information is already in the frequency domain, there's no need to go back to LPC. Also, I think a DCT

Hi, I am currently encoding 32 Khz/Qual-10/UWB encode mode, with MANUAL_ALLOC enabled ( similar to the c5x/c6x configuration). In file sb_celp.c, I noticed the scratch memory grow during recursive (UWB -> WB) calls to 'sb_encode'. 1. 'stack' was not tracked (with 'tmp_stack' as done at other palaces) after/before - auto-correlation/Levinson-Durbin scope (~line

Hi Jean-Marc I'm looking at the 1.0.5 source, and I'm not seeing an _spx_lpc(). There's an _spx_autocorr(), which is in lpc.c and is called near the start of the encoder function in nb_celp.c. The encoder seems to call the autocorr() function, then calls wld() to do something called Levinson-Durbin. Am I right in thinking that after the call to wld(), the st->lpc[] array

O.k., some more info: I just tested bandwidth widening to fix this. But I need to go to gamma values below 0.9 to become stable -- clearly too much widening, I think. I looked inside the Levinson-Durbin algorithm next. The lines #ifdef FIXED_POINT r = DIV32_16(rr+PSHR32(error,1),ADD16(error,8)); #else r = rr/(error+.003*ac[0]); #endif look interesting. While for floating point,

When I do ARMA(2,2) using one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std.

Hello Michael, see as an online resource: http://www.statsoft.com/textbook/sttimser.html or get hold on a time series analysis textbook, like one of the monographies written by Hamilton; Luetkepohl; Brockwell & Davis; Harvey or Box & Jenkins, to name but a few. In a nutshell, PACF 'eliminates' intermediate autocorrelations compared to ACF, e.g. an AR(1) process will ordinarily

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv