Tagged Questions

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.

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Why does including latitude and longitude in a GAM account for spatial autocorrelation?

I have produced generalized additive models for deforestation. To account for spatial-autocorrelation, I have included latitude and longitude as a smoothed, interaction term (i.e. s(x,y)). I've based ...
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Outlier detection for generic time series

In this case, "generic" being the entire gauntlet of macroeconomic time-series that private and government statistical offices put out. Some background - I recently started working at a data provider ...
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How to test the autocorrelation of the residuals?

I have a matrix with two columns that have many prices (750). In the image below I plotted the residuals of the follow linear regression: ...
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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 (...
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Smoothness of a surface

I am currently working on a model which takes two parameters and produces a measurement statistic. Think of it as Z = f(X,Y). Z is a matrix of my statistics and I am creating a surface plot of it in ...
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PACF manual calculation

I am trying to replicate the calculation that SAS and SPSS do for the partial autocorrelation function (PACF). In SAS it is produced through Proc Arima. The PACF values are the coefficients of an ...
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Formula for autocorrelation in R vs. Excel

I am trying to figure out how R computes lag-k autocorrelation (apparently, it is the same formula used by Minitab and SAS), so that I can compare it to using Excel's CORREL function applied to the ...
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Literature on generating “similar” synthetic time series from observed time series

The motivation for this question is from Finance. I have some market data (daily time series) for the price of some securities and I would like to generate synthetic versions of these which are ...
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How to analyze longitudinal count data: accounting for temporal autocorrelation in GLMM?

Hello statistical gurus and R programming wizards, I am interested in modeling animal captures as a function of environmental conditions and day of the year. As part of another study, I have counts ...
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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 ...
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Can I trust a regression if variables are autocorrelated?

Both variables (dependent and independent) show autocorrelation effects. Data is time-series and stationary When I run the regression residuals appear not to be correlated. My Durbin-Watson statistic ...
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What to read from the autocorrelation function of a time series?

Given a time series, one can estimate the autocorrelation-function and plot it, for example as seen below: What is it then possible to read about the time series, from this ...
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Algorithm to produce autocorrelated uniformly distributed number

I would like to produce a time-series of autocorrelated probabilities (with a predefined mean level of autocorrelation). I've spotted this and this which I believe should give me what I'm looking ...
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Which one of these looks stationary?

Step 1. To answer "Final Question" ( linked: "THE FINAL QUESTION : Order of differencing, to achieve stationary and interpretation of arima() , acf, pacf?") Expecting to find correct order of ...
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Spatial autocorrelation — GLM, autocovariate, MEM (Moran's eigenvector mapping)

I am currently working on two marine species distribution modelling and also on their overlap distribution. For this I use a binomial logistic regression model (GLM) with response being, respectively, ...
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Issues with ordinary kriging

I was following this wiki article related to ordinary kriging Now my covariance matrix looks like this, for 4 variables ...
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Two sample t-test for data (maybe time series) with autocorrelation?

I am new to statistics, so pardon any mistakes in my question. I have two time series $X_i$ and $Y_i$. Assuming that they're stationary AR(1) processes with possibly different means, how do I test ...
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Is the model wrong if a coefficient changes from minus in correlation table to plus in OLS?

Perhaps a very basic question but one that has me confused. Say, in a correlation table the relationship between A and the DV (...
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Can you use a spline function of the spatial coordinates to control for spatial autocorrelation? [duplicate]

Possible Duplicate: Why does including latitude and longitude in a GAM account for spatial autocorrelation? I am interested in the effect of a predictor vector $X_i$ on a binary outcome ...
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Analyze and generate “clumpy” distributions?

Are there standard ways of analyzing and generating "clumpy" distributions? analyze: how clumpy is a given point cloud (in 1d, 2d, nd), what are its clumpy coefficients? generate or synthesize a ...
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When was the autocorrelation function invented? And what was the motivation for it?

I'm just very curious about the discovery process behind the autocorrelation function. When was it invented? Was it independently invented multiple times, for example? What was the motivation for ...
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Derivation of sample autocovariance

The autocovariance is defined as $$\gamma(t,s) = Cov(X_{t}, X_{s})=E[(X_{t}-\mu_{t})(X_{s}-\mu_{s})]$$ When we have a stationary process the only thing that matters is the lag between the variables: ...
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need derivation of join-count variance (spatial autocorrelation stat), know where it is?

I am using interlibrary loan to get Cliff and Ord's book Spatial Processes, but the semester just ended and it is slow now. On page 18ish of this book, Cliff and Ord show how the variance for the ...