Autocorrelation is the correlation of a series of data with itself at some lag. This is an important topic particularly in the analysis of time-series data.

learn more… | top users | synonyms

2
votes
1answer
61 views

Forecasting a seasonal time series in R

Forecasting airline passengers seasonal time series using auto arima Hi, I am trying to model some airline data in an attempt to provide an accurate monthly forecast for June-December this year using ...
0
votes
0answers
6 views

Univariate fixed effect Vs Multivariate model -Negative Covariance, positive parameter estimate, but why?

I am trying to compare the results of two models. The first model looks at y with x as a fixed effect. The second looks at the covariance between x and y. Both models have repeated measures for x ...
1
vote
0answers
32 views

Quantity like correlation

I want to calculate this sort of quantity, $f()$, for my data. $x$ and $y$ are time series. $f$ behaves like a pseudo-correlation, but is different in the sense that even if the values jump up and ...
2
votes
1answer
97 views

Why autocorrelation affects OLS coefficient standard errors?

It seems that OLS residuals autocorrelation is not always an issue, depending on the problem at hand. But why residuals autocorrelation would affect the coefficient standard errors? From the Wikipedia ...
0
votes
0answers
23 views

when should I need to be concerned about autocorrelation?

I have a series of wave heights observations (2observations per hour for each day in 7 months) and I'm trying to model a regression with wind data (same frequency of observation). Since waves ...
0
votes
0answers
28 views

Why does Random Forest Regression perform worse than autoregression

I have a dataset of NFL games. Each game has one row for each team in the game. Each team's row contains the team's statistics in that game (such as points scored, passing yards, red zone attempts, ...
0
votes
0answers
29 views

Matlab: Unable to plot partial autocorrelation plot

For a time series I wanted to plot separately the partial auto correlation. Below is the graph for a time series which shows PACF plot of the time series $x$ which I wanted to reproduce: This ...
0
votes
1answer
19 views

How is the impulse-response function of a given system related to the autocorrelation function?

If I have the autocorrelation function of an observed system output, how does this relate to the impulse-response function of that system, if I don't have information about the input?
0
votes
0answers
24 views

Rule of Thumb for minimum length of time series for Autocorrection estimation

I had a related question answered here: Rule of Thumb for minimum length of time series for AR(1) estimation However the answer gives rise to a new question. I want to be able to estimate the Auto ...
2
votes
1answer
30 views

Rule of Thumb for minimum length of time series for AR(1) estimation

I have a data set of 350 points, I want to estimate the lag 1 auto correlation for different sub-sets of the data. More precisely I want to take non overlapping windows of length 1,2,3....n and ...
5
votes
2answers
96 views

Detecting whether website visits are automated

I am trying to detect automated visits to a website. A typical data set for an automated client is of the form: ...
2
votes
2answers
133 views

Is residuals autocorrelation always a problem?

I read that OLS underestimates variance when residuals are autocorrelated. I see why autocorrelation would be a problem in time series analysis, in the sense that the coefficient are not efficient ...
2
votes
0answers
24 views

Interpretation of the partial autocorrelation function for a pure MA process

I have been working with some time-series theory and I noticed something that I can understand "mathematically", but not based on the intuitive explanations of what the partial auto-correlation ...
2
votes
0answers
22 views

Variance-covariance matrix as the sum of variance covariance matrices

I have a variance-covariance matrix, $\mathrm{V}$. This allows me to take a vector, $x$ of independent random variables drawn from a known distribution, and induce a required variance-covariance ...
1
vote
1answer
90 views

Binning method: looking for an example

I heard and read several times of the use of 'binning' methods to estimate the uncertainty and the auto-correlation time of a sample generated by MCMC but I can't find a textbook example of it being ...
2
votes
3answers
106 views

Library routine for rolling window lag 1 autocorrelation?

I am looking for a library routine that will calculate the lag 1 autocorrelation of a time series with a rolling window; meaning "slide a window of size N points along the time series, calculate the ...
1
vote
1answer
27 views

Residual autocorrelation versus lagged dependent variable

When modeling time series one has the possibility to (1) model the correlational structure of the error terms as e.g. an AR(1) process (2) include the lagged dependent variable as an explanatory ...
2
votes
2answers
84 views

Why is it desirable to have low auto-correlation in MCMC?

I keep reading about the need to check for autocorrelation in MCMC. Why is it important that the autocorrelation is low? What does it measure in the context of MCMC?
2
votes
1answer
38 views

Is the Durbin-Watson test appropriate for count data

In determining if there is any serial correlation in a time series of count data, is the Durbin-Watson statistic or similar approaches appropriate? I ask this question because the dwtest implemented ...
1
vote
1answer
72 views

Getting Residuals to be White Noise

I'm on a time series project for an undergraduate course. For the project I'm trying to come up with an ARIMA model for the housing starts data set. ...
1
vote
1answer
61 views

Estimation of regression with autocorrelated errors

In a book it is written that, In regression work we typically assume that the observational errors are pairwise uncorrelated. But in most time series data , the successive residuals have tendency to ...
1
vote
0answers
17 views

Two-tailed test [duplicate]

In one-tailed test , we give our decision at $\alpha$ level of significant. But in two-tailed test , why do we give our decision at $2\alpha$ level of significant? Why do we not give the decision of ...
0
votes
1answer
50 views

Spatially auto-regressive two-stage model

I'm working on a project in which I use a 'Generalized Spatial Two-Stage Least Squares' model, mostly known as $y= X \beta + \lambda W y + u$ and $u = \rho M u + \epsilon_n$ where $y$ and $u$ are ...
1
vote
1answer
48 views

Durbin Watson test statistic

I applied the DW test to my regression model in R and I got a DW test statistic of 1.78 and a p-value of 2.2e-16 = 0. Does this mean there is no autocorrelation between the residuals because the ...
2
votes
0answers
34 views

Extremal serial dependence

As part of my analysis of heavy-tailed time series of company returns, I would like to check whether extreme returns exhibit serial dependence, i.e. if extreme events are followed by extreme events. ...
2
votes
0answers
48 views

ACF and PACF plot analysis

I am new to ARIMA, and I am trying to understand these lag plots. Are the following ACF and PACF suggesting that the lag of my time series is 4? If I am wrong, please help me understand these plots. ...
0
votes
0answers
19 views

how to deal with autocorrelation

My study is related to determinants of corporate liquidity and I am working on eviews. My model gives durbin watson stat value 0.89. after applying fixed effect estimation the value changes to 1.47. ...
2
votes
0answers
119 views

consequences of lagged dependent variables in panel data and how to deal with it?

I have some elementary problems understanding the consequences of using/adding a lagged dependent variable in my predictive model. I’m trying to predict values $Y_{i,t+\tau}$ for $\tau=1-3$ with: ...
2
votes
0answers
17 views

Methods for measuring snowball effects in a “complete” longitudinal dataset

I'm looking for ways to test for "cumulative advantage" effects in a longitudinal dataset (see image) I guess the data set is principally similar to this: http://www.caldercenter.org/whatis.cfm , ...
2
votes
1answer
122 views

Can First Differencing Cause Negative Serial Correlation

Ex. series, say stock prices 103 101 102 150 101 102 100 First differenced 2 1 48 -49 1 -2 Notice you could guess a very large negative number following the very large positive in the first ...
0
votes
0answers
25 views

Poisson autocorrelation function

I want to draw an autocorrelation function, call it, AC(tau) where tau is the offset in the autocorrelatin. The vector I'm feeding into the AC is the spike train of a neuron whose action potential ...
0
votes
0answers
16 views

Postestimation results after VAR analysis show autocorrelation in residuals

I'm performing a VAR analysis on news effects and S&P500 returns. Now, I specified the number of lags (5) according to Schwarze's Bayesian Information Criterion (SBIC) and ran some postestimation ...
1
vote
0answers
37 views

Testing significance of correlation between two autocorrelated series

Say I collected the shin bones of N different skeletons; they are all around 30cm long, and I measured different properties P1, P2, P3, P4 and P5 along these bones every 3mm (so I have 100 data points ...
0
votes
0answers
53 views

Which type of residuals to use for the Durbin-Watson test (autocorrelation)

I want to check if there is residual autocorrelation in my model and the test for this is the Durbin-Watson test. I am using R and my question is if it makes a difference which type of residuals one ...
1
vote
1answer
30 views

Autocorrelation of convolution integral

Work out the autocorrelation $r_Y(\tau) = E[Y(t)Y(t+\tau)]$ with $Y(t) = \int_{-\infty}^{\infty} h(t-u) x(u)$ and $X$ a WSS, ergodic process I always get: $h(t)* h(t+\tau) * r_X(\tau)$ (with $*$ ...
1
vote
0answers
84 views

Residual autocorrelation in Poisson (Neg.Bin) models - Durbin Watson test

I am running some Poisson (or Neg.Binomial depending on overdispersion) models and i want to check for residual autocorrelation due to the nature of the data (monthly cases). I am using R and i am ...
2
votes
0answers
70 views

Whitening Transformation using a Hadamard product Variance Matrix

I want to whiten a vector $X$ by transforming the variance-covariance matrix so the variance-covariance matrix of the transformed series will be the identity matrix $I$. $X$ is a time-series column ...
1
vote
0answers
16 views

Is lm.morantest valid on residuals from geeglm fit of binary data?

Is it valid to apply lm.morantest (in the package spdep) to test for spatial autocorrelation among residuals from a Generalized Explanatory Equation (geeglm in geepack) model fit of binary data? If ...
1
vote
1answer
33 views

Unexplained symmetry when computing Power Spectral Density of white noise

I'm trying to learn more about noise, power spectral density (PSD) and statistical variances. With regard this I'm trying to compute the power spectral density of white noise. However, when I do I get ...
5
votes
1answer
71 views

What is the difference between (universal) kriging and spatial autoregressive models?

As part of a course on missing observations in social/survey statistics I am trying to explore existing methods of predicting either point pattern or polygon data. I got quite confused by all the ...
1
vote
1answer
27 views

Correlation definition between two set

How can I define correlation between two set x and y: {$(x_1,y_1),(x_2,y_2),(x_3,y_3),...(x_n,y_n)$} Is this definition correct: ...
1
vote
1answer
38 views

Estimating auto-correlation with unequally spaced data

I'm working on a time series problem where the spacing between observations is usually 12 or 24 hours, but this is not guaranteed. I'd really like to estimate the auto-correlation function, and I've ...
1
vote
1answer
52 views

Bias of Panel Generalization of Durbin-Watson

I'm working with an unbalanced panel dataset. (Country-Time) of approximate dimensions H=100 individuals i and average time length over individuals $mean(T_i)\approx7.5$. And about n= 8 regressors ...
1
vote
0answers
25 views

Can time-invariant variables cause autocorrelation?

I am running a pooled OLS regression and Random effects regression. I have tested for autocorrelation for both methods. In the pooled OLS model I find serial correlation but for the RE model I find ...
0
votes
0answers
34 views

Why does this autocorrelation formula hold?

I have been unable to understand the highlighted subsection of David Kenny's Correlation and Causality, which I downloaded from Kenny's website.
2
votes
2answers
72 views

does a log transformation of the dependent variable affect autocorrelation?

I have panel data and have used the xtserial command in Stata to test whether there is autocorrelation. When I take the log of the dependent variable, the test shows that there is autocorrelation. ...
1
vote
1answer
81 views

How to interpret autocorrelation

I have calculated autocorrelation on time series data on the patterns of movement of a fish based on its positions: X (x.ts) and Y (...
0
votes
0answers
33 views

Sample autocovariance of Durbin–Watson test

I understand Durbin–Watson test, but I can't understand this sentence. I cannot prove it. The Durbin-Watson test statistics is asymptotically equivalent to (rootT*C), where C is the sample ...
8
votes
1answer
95 views

What causes a U shaped pattern in the spatial correlogram?

I've noticed in my own work this pattern when examining a spatial correlogram at varying distances a U-shaped pattern in the correlations emerges. More specifically, strong positive correlations at ...
2
votes
1answer
180 views

Autocorrelation of discrete time series

I am currently planning on calculating the autocorrelation for various lags given a time series. However, my elements of the time series are "discrete" and abstract classes; i.e., no integers. For ...