Unanswered Questions
5,900 questions with no upvoted or accepted answers
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Implementation of CoVaR (a systemic risk measure) in R
I'm trying to estimate CoVaR using bivariate DCC GARCH in R. The concept of CoVaR is the dependence adjusted of VaR, which was first introduced by Adrian and Brunnermeier (2011). However, this ...
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18
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Time series regression with overlapping data
I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean ...
16
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What is tantile regression?
My question follows on this discussion of medials and tantiles vs medians and quantiles from earlier this year:
When would we use tantiles and the medial, rather than quantiles and the median?
As ...
CommunityBot
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14
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700
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Convolutional neural network for multi-variate time series?
I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence.
I searched the net for the available designs in the literature and i found ...
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13
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415
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Is autocorrelation not worth addressing with small N?
Consider a simple regression context in which there is a small set of response values, $Y$, and corresponding dates, $X$. (For simplicity, we can assume the dates are equally spaced.) We would like ...
12
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Empirical Prediction interval for time series forecast based on quantile regression
As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
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3
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Need advice on change point (step) detection
I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to ...
CommunityBot
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10
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5k
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Stationary vs Stability
I am searching for an example of an unstable VAR($p$) process (its reverse characteristic polynomial has no roots inside and on the complex unit circle) which is stationary. I come up with this ...
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9
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Why don't we typically worry about stationarity in panel data models with fixed effects?
Why don't we typically worry about stationarity in panel data models with fixed effects?
In time series analysis, stationarity is often a crucial assumption. However, I've noticed that in applied ...
CommunityBot
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9
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1
answer
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PyMC3 implementation of Bayesian MMM: poor posterior inference
Google released a whitepaper on Media Mix Modelling (MMM) in 2017; vanilla MMM (established in the 1960s) uses multivariate regression. It's a decent mechanism to understand which of your marketing ...
CommunityBot
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9
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Why does uncertainty of the autocorrelation coefficient increase as lag increases?
The Python module statsmodels contains functions for ACF and PACF. Below is an example from the docs with a plot that shows the (zero-centered) confidence ...
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9
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1
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Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?
I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
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9
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7k
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What is difference between interrupted time series and regression discontinuity design
Say that one has data over time, t, on an outcome, y. There is an event that happens at t==0....
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9
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1k
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Fisher's test of periodicity
I have evenly sampled time series on which I applied Fourier transform. I am trying do determine if the signal contains statistically significant periodic components. I have succeeded with determining ...
8
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290
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Time series: sample vs. population + population vs. realizations of random process
Suppose we have $120$ monthly observations (Jan 2000 - Dec 2009) of unemployment rate and suppose we would like to use these in order understand the unknown underlying stochastic process that ...
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