Tagged Questions

2
votes
1answer
31 views

ACF and PACF of AR process with non-zero mean

Calculating the acf and pacf of an AR process with zero mean is straightforward, but does anyone knows how to proceed when the mean is not zero? Of course my intention is to calculate the theoretical ...
3
votes
2answers
74 views

Confidence bands in case of fitting ARIMA in R?

I want to look at the acf and pacf of my data, to identify the model for my mean equation, so I want to fit an ARMA for my mean equation and later on model the conditional variance by a ARCH/GARCH (I ...
0
votes
2answers
75 views

Need a clear and simple auto-regressive model example

This may be hard to find, but I'd like to read a well-explained auto-regressive model example that: uses minimal math extends the discussion beyond building a model into using that model to forecast ...
3
votes
2answers
233 views

Correlogram in R like in Stata?

In STATA I can create a "Correlogram" to find the appropriate lag order in case of time series. E.g. I know I can use the acf or Acf of the forecast package to calculate the ACF and PACF and to ...
2
votes
2answers
54 views

Omit 0 lag order in ACF plot

How can I omit the zero lag order in an acf plot? See this picture: generated by ...
1
vote
1answer
61 views

What to do about Seasonality Patterns in ACF, Time Series Data

I'm dealing with a time series data and I'm trying to construct a time series model for this particular dataset. I'm new to R and tried using the the auto.arima ...
0
votes
0answers
29 views

Autocorrelation impact on the Coefficient of Variation

Many articles use the Coefficient of Variation (CV) to report variation in time series. But isn't the standard deviation, used to measure the CV, no more reliable when autocorrelation is present ? ...
1
vote
1answer
134 views

Help interpreting ACF- and PACF-plots

My ACF- and PACF plots are illustrated below: The first one is in original scale and the second picture is zoomed. What process would you classify this? AR, MA or ARMA? =) Thank you for any ...
0
votes
0answers
59 views

How to calculate confidence interval for autocorrelation- and cross correlation functions?

I would like to calculate the confidence interval for my autocorrelation function. Does this calculation differ in any way from the normal way of calculating confidence intervals? How would I ...
0
votes
0answers
39 views

How to decide the order of my ARMAX-model for each component?

I'm doing time series analysis with 78888 x 8 matrix of data. The matrix includes the response data (the series I'm interested in predicting) and exogenous data. The data is hourly sampled and ...
0
votes
0answers
25 views

Time delay estimation for non linear physical signals

I have a non linear physical system with 4 Inputs and 2 Outputs and I want to model it using a time series model such as NARX model. Because I'm new in system modeling and signal processing I do not ...
1
vote
2answers
66 views

Time-series autocorrelations all positive

I've got 36 months of timeseries data, and eyeballing it, it has a linear trend upward. I wanted to do a little more than just eyeball it though. So I put together a correlogram of autocorrelation ...
1
vote
0answers
125 views

How to measure the “average” autocorrelation of a time-series signal with itself

I have a short time series signal (say around 30 samples), and I would like to check whether or not it's oscillating. One approach I came up with was to measure the autocorrelation of the signal with ...
0
votes
0answers
58 views

Is there a serial autocorrelation test for FGLS-FE fitted with pggls function in R?

a simple question here: is there any AR1 and AR2 test for FGLS-FE fitted with the pggls function of plm package in R? (one example would be the Baltagi-Wu LBI test) Thanks for your attention!
1
vote
1answer
193 views

Reasons for autocorrelation in time-series residuals

Why are residuals usually autocorrelated in time-series data? Could it stem from the autocorrelation of the response variable? Is the reason that in some cases the differencing (i.e., the differences ...
0
votes
0answers
116 views

Variance of a series of correlated variables

I have started with a time series of 5000 random numbers drawn from uniform distribution with mean 0 and variance of 1. I then construct a Variance-Covariance matrix and use this to induce ...
1
vote
2answers
104 views

Help understanding the following picture of ACF

I'm having a bit trouble understanding the blue dotted lines in the following picture of autocorrelation function: Could someone give me a simple explanation, what they are telling me x) Thnx
2
votes
1answer
89 views

Forecasting model inputs that are both auto-correlated and are calibrated over time?

How does one account for model inputs that are both a) auto-correlated and b) calibrated over time? I'm interested in forecasting the outcomes of sporting events. Let's say that each team has a score ...
8
votes
2answers
344 views

What's the deal with autocorrelation?

To preface this, I have a pretty deep mathematical background, but I've never really dealt with time series, or statistical modeling. So you don't have to be very gentle with me :) I'm reading this ...
0
votes
0answers
30 views

What is Ljung-Box-Q-test and why do we need it? [duplicate]

Possible Duplicate: Help with the Ljung-Box test for time independence of residuals I'm learning to use Matlab's econometrics toolbox and I came across with this "Ljung-Box-Q-test"... Can ...
2
votes
0answers
34 views

Confusion related to modelling of temporal correlation

I was a bit confused about the modelling of temporal correlation in a certain paper. Lets say, I have vector $\bf{x}$ of dimension m, and a time series $\bf{x_1},\bf{x_2},...\bf{x_N}$. Now I want to ...
0
votes
0answers
134 views

Generalized least squares with insignificant predictor variable

Suppose I have fitted an standard linear regression mode $Y=\beta_0+\beta_1X_1+\beta_2X_2+\beta_3X_3+\epsilon$. Based on the ACF plot or PACF plot of the residuals for this regreesion model, I found ...
0
votes
0answers
68 views

Autocorrelation of nonstationary IMA(1,1) process

I knwo that the autocorrelation in MA(1) process varies between -.5 and +.5, if we consider d(t)=c+e(t)−θ⋅e(t−1),then for positive values of Theta, autocorrelation is negative and for negative values ...
0
votes
3answers
811 views

AR(1) selection using sample ACF-PACF

The following graph shows the ACF (sample autocorrelation function) and PACF (partial autocorrelation function) of the residuals in a linear regression. There is a sinusoidal decay in the ACF and two ...
2
votes
1answer
90 views

AcF and Stationarity

Very often in time series literature, it is remarked that if a series is non-stationary the AcF will decrease to zero very slowly while the opposite occurs for a stationary series. What's the basis ...
7
votes
1answer
497 views

Interpreting seasonality with ACF and PACF

I have a dataset where empirical intuition say I should expect a weekly seasonality (i.e., the behavior in saturday and sunday is different from the rest of the week). Should this premise be true, ...
3
votes
1answer
145 views

Random walk or not?

I'm trying to understand whether the observed time series can be described as a random walk or not. When I check autocorrelations of the differences, none of the autocorrelation bars for the ...
2
votes
2answers
258 views

Top-Down or Bottom-Up Approach for demand forecasting

I have 5000 SKUs which all of them are highly positive autocorrelated, to get the item level forecast for all5000 SKUs (disaggregate forecast) which approach can provide more accurate forecasts, BU ...
2
votes
0answers
406 views

Newey-West t-statistics

I have a time-series which is autocorrelated by construction, and might be heteroscedastic. I have calculated the sample mean of this time-series, and would like to calculate the t-statistic ...
0
votes
1answer
89 views

Contradictory Results in autocorrelation tests

I have a time-series model, with stationary variables. Testing for outocorrelation hasn't been easy: - a can't calculate DW stat because of my small number of observations; - the GB test indicates ...
2
votes
1answer
176 views

How to model binary dependent data with temporal autocorrelation?

I am trying to model annual tree nut production using climate predictors. The nut data (dependent) is a binary timeseries (0,1 - representing unsuccessful and successful nut production), with one ...
6
votes
1answer
477 views

Box-Ljung test on white noise series

I generate this data in R: set.seed(111) ds=rnorm(1000) When I perform Box-Ljung test to test the independency: ...
5
votes
4answers
3k views

How to perform pooled cross-sectional time series analysis?

For 86 companies and for 103 days, I have collected (i) tweets (variable hbVol) about each company and (ii) pageviews for the corporate wikipedia page (...
1
vote
2answers
326 views

Skewness, kurtosis and normality of a time series

I have a sample size of $21$ with $496$ observations.Can I presume an approximately normal distribution,and use a $t$-test to compare the difference in means, and difference in various financial ...
3
votes
2answers
990 views

Most appropriate way to make a time series stationary? (i.e. remove serial correlation?)

So I have this data set of 56 users with 52 weeks worth of weekly average data for blood pressure and exercise level recordings. I would like to use change point analysis ...
1
vote
0answers
127 views

Some questions about VAR-models, $\Phi$-matrix-coefficients and partial-(auto-)correlations

There is an abundance of literature about VAR-models, which teaches how to test preconditions, specify and estimate VAR-models for stationary and also cointegrated time-series. However, I'm still a ...
4
votes
3answers
330 views

Autocorrelation from multiple time series samples

I have multiple samples of a time series (for example, the time series might be minutely samples from 12am to 3pm, and I have that for ten different days) and I'd like to compute the autocorrelation ...
3
votes
1answer
248 views

Testing periodogram “peaks”: sine-like wave or AR/MA/ARMA noise?

I'm performing an harmonic fit to data I know (from physical constraints) come from a periodic source of the form $$\sum_j^M \sum_i^N a_{i,j}\sin(2\pi f_it)+b_{i,j}\cos(2\pi f_it)$$ using the ...
5
votes
1answer
216 views

Why autocovariances could fully characterise a time series?

I read from textbook that 'Autocovariance can fully charaterise the time series' joint distribution', I do not fully understand the connection between covariance and joint distribution here. Please ...
0
votes
0answers
161 views

How to find and describe regularities in a distribution of interarrival times of a recurring event?

I want to see if there are regularities in interarrival times of a recurring event in discrete time. I know that I can fit distributions to the interarrival time distribution, but are there other ...
3
votes
3answers
714 views

Creating auto-correlated random values in R

We are trying to create auto-correlated random values which will be used as timeseries. We have no existing data we refer to and just want to create the vector from scratch. On the one hand we need ...
3
votes
1answer
1k views

What is the difference between serial correlation and having a unit root?

I may be mixing up my time series and non time series concepts, but what is the difference between a regression model that exhibits serial correlation and a model that exhibits a unit root? In ...
0
votes
3answers
305 views

Good reference on sample autocorrelation?

I'm not a statistician but I'm writing my thesis on mathematical finance and I think it would be neat to have a short section about independence of stock returns. I need to get better understanding ...
2
votes
2answers
1k views

Is there a difference between an autocorrelated time-series and serially autocorrelated errors?

I'm pretty sure that I'm missing something obvious here, but I'm rather confused with different terms in the time series field. If I understand it correctly, serially autocorrelated errors are a ...
2
votes
0answers
176 views

Lag Selection Modelling 'Pseudo' Panel Data

I have what I would call a pseudo panel, where my dependent variable varies over time and space (regional death counts), but my x variable of interest does not (national wage time series). Basically, ...
3
votes
1answer
508 views

Analysing periodicity in sparse time series

I am interested in looking for periodicities in a several day long recording of electrical activity. The traces present a very steady baseline over which, from time to time, some short events (300-500 ...
1
vote
0answers
110 views

How do I model a sine/cosine on a cycle derived from a Baxter filter?

I have a cycle that I filtered out from an original series using a Baxter deterministic filter. However, the cycle plot still has some noise and I would like it to be more determinisitc and follow a ...
5
votes
2answers
1k views

Fitting multiple linear regression in R: autocorrelated residuals

I'm trying to estimate a multiple linear regression in R with an equation like this: regr <- lm(rate ~ constant + askings + questions + 0) askings and ...
2
votes
0answers
144 views

Using autocorrelation plots to choose the number of inputs for a neural network predicting time series

A neural network applied to time series needs to have the number of input nodes defined. Each input is applied to a time point previous to the current point being predicted. If $D$ is the number of ...
3
votes
3answers
576 views

How to deal with gaps/NaNs in time series data when using Matlab for autocorrelation and neural networks?

I have a time series of measurements (heights-one dimensional series). In the observation period, the measurement process went down for some time points. So the resulting data is a vector with NaNs ...

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