Questions tagged [lags]
A lagged value in a time series is a value of a variable corresponding to an earlier time. For example, in a monthly time series, the first lagged value will be the value for the previous month and so on.
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Regression analysis and time series data
I'm trying to model the effect of flow at entry and flow at exit on the produced energy in a thermal power plant ,The data is collected every 2h for two years,in my internship they want me to model ...
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How to detect whether Y or X is the lead or lag variable in a time-series using the regression of Y(t) vs X(t-1) and X(t) vs. Y(t-1)?
Coefficient in Black, P-Value in Blue and Standard Error in Red. Using these results, can I deduce whether X or Y is the lead or lag variable in the time-series?
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Why are lags of the dependent variable a no-no in traditional random effects models?
This post says:
Lagged versions of the dependent variable are a no-no in traditional random effects models. The problem is that they are correlated with the random intercept and produce inconsistent ...
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Why can't I estimate the target variable when I apply a lag of variable
I applied lag to the exposure variable to adjust for its previous time-point's influence.
However, the coefficients of the lagged variables are estimated to be excessively high, leading to the current ...
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Time series iterated multi-step forecasting autocorrelation issue (propagation of error)
I am trying to model some (quite ugly) time series data with lagged values of the outcome variable using a non-parametric machine learning model as I do not know how to correctly specify the model. ...
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Including a function of the lagged dependent variable
I am trying to establish a relation between a new regulation and the stock market investment behaviour of people. To do so, I have a set of panel data, which has people (i) over multiple years (t).
<...
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Does Granger causality also tells me how big the time lag is?
I want to determine how big the time lag is for two time series.
In this example, how long the twitter sentiment about a stock leads the stock price.
I use Python with statsmodels.tsa.stattools ...
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How many lags to include? (Temperature data set)
I have a time series of daily temperature with autocorrelation that looks like this:
Of course, temperature is heavily autocorrelated. When looking at the partial autocorrelation plot, lags are ...
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Interpreting lagged exogenous variables in ARMAX and regression with ARMA errors
There is an interesting post about the connection of lagged exogenous variables and the autoregressive time series model: Forecasting - Lags vs. AR terms for Exogenous Variables
Consequently, by using ...
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Selecting optimal lag values for Neural Network in univariate time series forecasting - How many lags to use as input variables?
What is the recommended approach for selecting lag values in a univariate time series forecasting problem, specifically for input variables in a feedforward neural network (FFNN)? In my research ...
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Lagging a quadratic independent variable
In the theoretical framework for my model, there is concern about reverse causality between Y and my main X variable. Additionally, I believe there's an information lag effect where economic agents ...
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Lagged variables for Correlation analysis - fill or trim?
I'm attempting to find out if there's any correlation between a dependent variable and lagged independent variables. Simple question yet I can't find any answers to this - should the dataset be ...
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are moving average and lag modeling the same?
I am not clear on the difference between the two concepts but I am interested in air pollution exposure in a given period of time and in the literature, I know that lag models are used. I have also ...
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ADF Interpretation
below is my ADF test for regression residuals. I use alpha=5%
My interpretation is : my residuals are non-stationary as my model does not take into account the 4 lags suggested by ADF. In other words, ...
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Is it possible to complete a Time-Lagged Multilevel Model with time-variant predictors that change systematically with time?
I have a data set of 139 individuals who provided responses to a series of questionnaires at 5 times points (0, 3, 6, 12 and 18 months) across treatment. Time is included as a covariate in the model (...
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Choose optimal lags in months for annual data
I try to forecast annual monthly price changes using annual monthly X changes. To choose the best lag, I run the VAR model and aim for the minimal AIC value. The problem is that the price is ...
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Lagged nonlinear independent variable?
I came across a paper that write the following:
The first set of independent variables, REPRESSION(1)i,t-1, REPRESSION(2)i,t-1, REPRESSION(3)i,t-1, REPRESSION(4)i,t-1, are binary indicators measuring ...
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Time-lagged cross correlation between two measurements across multiple people
I have collected time-series data from a few hundred people. For each person, I have two measurements that I am interested in correlating.
Specifically, I want to identify the time point at which ...
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Cross-lagged Pearson correlation in R
I have a dataset where I have two recordings (sessions) of two different variables.
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Lag variables on OLS after PCA (Principal Components Analysis)
I have about 60 macroeconomic and financial indicators; all of them stationary (logs and differencing), with monthly data for the last 25 years. I am trying to predict changes in a financial variable (...
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How good are inflation expectations as a predictor on inflation?
I want to research inflation expectations and how well they predict inflation. I have found some past articles but none of them explain how to forecast inflation using inflation expectations. I have a ...
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Should I lag the independent variable or dependent variable to get delayed effects of the IV on DV?
I'm doing research where I am getting the delayed effects of my Xs on my Y using a 2-way fixed effects approach. Since all of my Xs will end up being delayed, should I lag my Y by -1 or should I lag ...
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Creating a New Predictor Variable From a "Lagged Response Variable"?
Our TA in our stats class told us that when working with regression models, it is generally unadvisable to create a new predictor variable from a "lagged regression variable" (unless you are ...
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Significant Partial Autocorrelations at High Lags
I've been doing some exploratory data analysis on a time series dataset and had some questions about interpreting a PACF plot that I generated. I initially displayed the plot with a maximum lag of 400,...
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Specify Vector Error Correction Model with different lagged effects?
I'm fitting a Vector Error Correction Model. The general form is like this:
$$\Delta y_t = A_0 + \sum_{i=1}^{t-1} A_1 \Delta y_{t-i} + \lambda EC_t + \nu_t$$
In which $EC_t$ is the error correction ...
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Johansens Cointegration test - VARselect output showing all Lag 1's
I am running a cointegration test on 4 input variables over 1 year and when I run the VARselect it outputs:
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Cross correlation analysis on Bray-Curtis dissimilarity matrix with environmental variables
I'd like to use a cross correlation analysis to examine whether there are temporal lags between environmental factors and changes in ecological community structure, but I am not sure how to structure ...
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Textbook exercise-ARMA model
I have tried hard but cannot solve the below exercise. Any extra hint or solution will be appreciated!
Given the below stationary series:
\begin{align*}
y_t=\frac{L-\phi}{1-\phi L}\varepsilon_t,
\end{...
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Lagged regression with more than one predictor time-series
I have n + 1 different discrete time series. One of them is {Yt}, which I call ‘response time series’, and the other n are {Xt,i} (i = 1,…,n) which I call predictor series. I define n time lag ...
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Should all variables in a VAR model have the same number of lags?
I'm performing an econometric analysis and all my variables (in daily frequency) are stationary, so I've decided to use a VAR model. After analyzing the data in Stata, it suggests all variables should ...
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Creating auto-correlated random time series
I want to create random time series that follow a given auto-correlation function. For this I am using an AR(n) model approach:
$$X_t = \sum_{i=1}^n\alpha_i X_{t-i} + \epsilon_t$$
where $\epsilon$ is ...
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Can a Variable Be Both Dependent and Independent?
We can see that the GDP growth, represented by "y" is the dependent variable and independent variable. I would like to perform quantile regression in Eviews, with ...
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Should a covariate be lagged in a GARCH-X model?
I am modelling Dow Jones returns using a GARCH(1,1) model but I also want to estimate a GARCH(1,1) by inserting a covariate to check if this covariate affects the volatility in some ways. The ...
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Lagged variables, data leakage and machine learning
I am reading a paper that fits a random forest (RF) to some data that is grouped by company and quarter. In the data engineering stage, the authors include 'lagged' variables of many of the ...
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Does $p=0 \implies \sum_{i=1}^{p} \phi_i L^i = 0$?
Let us take this $\operatorname{AR}(p)$ equation
$$\left(1 - \sum_{i=1}^{p} \phi_i L^i \right)X_t = \mu + \epsilon_t$$
as an example.
When $p=0$ I read this to mean
\begin{align*}
\mu + \epsilon_t &...
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Interpret high p and q orders of GARCH models
I am currently working with GARCH models (sGARCH, E-GARCH and GJR-GARCH). My question is very general. I chose my p and q orders with the help of AIC criterion. The best models are sGARCH(2,3),
E-...
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If a time-series achieves max-likelihood at GARCH(1,1), would EGARCH, or other GARCH variations achieve global maximum likelihood at p=1, q=1?
If I find that a time-series fits GARCH(1,1), would EGARCH, or other GARCH variations still be X-GARCH(1,1)?
user318514
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Do Vector Autoregression models have the same p, and q order terms as a ARMA model or same number of ACF/PACF?
Do Vector Autoregression models have the same p, and q order terms as a ARMA model?
Do you have n (Partial) autocorrelation function plots (P)ACF, one for each of the n time-series, or do you still ...
user318514
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Time series analysis with non-stationary count data (Poisson/Negative binomial models)
I am trying to model the relationship between real-world events and specific features present in tweets related to these events. Whereas my dependent variable (events) is a count variable and its time ...
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Temperature Lag calculation
I am working on a data science project on an industrial machine. This machine has two heating infrastructures. (fuel and electricity). It uses these two heatings at the same time, and I am trying ...
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ACF and PACF Plot
I am a first year stat student. We are tasked to create a SARIMA model from trial and error using ACF and PACF plot. Now here is my generated plot:
Now I am trying to understand the plot but I don't ...
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Prediction with different lags of independent variables
I am wondering if it is suitable to make 1-step ahead forecast with independent variables of different lags？
Suppose that in practice, one wants to forecast the 1-step head (i.e. 1 month) value of ...
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What high order lags mean in the VAR/SVAR?
I am new to VAR/SVAR and microeconomics.
I learn (from here) that a k-dimension p-order SVAR can be written as $X_{i,t}=f_i(X_t^{-i},X_{t-1},...,X_{t-p},\epsilon_{i,t})$, where $i$ indexes the number ...
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Lagged predictors in irregular-time / asynchronous / time-unconstrained data
In growth curve modeling or other approaches, when time is constrained/synchronous/regular (i.e. panel/wave data; all observations occur synchronously), lagged prediction is trivial - simply add t-1 ...
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Include predictor at t when including that same predictor at t-1?
I am very confused about whether I need to include the predictor at t when I also include the predictor at t-1 in a linear mixed effects model.
For example, consider this simple model:
$$DV = \beta_0 +...
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What's the difference between instantaneous and lagged effect?
I am working on causal discovery in time series.
I know the difference between instantaneous and lagged causal effect based their graphical definition. Specifically, if we are studying causal ...
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Lag based numerical features or ID categorical variable?
I have to develop a Machine Learning regression model to predict customer’s delay in paying invoices. In addition to the invoice related variables, of course a very important variable is the customer.
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Calculating AICc for regression with ARMA errors
I am unable to manually calculate the AICc for a regression with ARMA errors.
I would appreciate any help, such as: (1) pointing out what I am doing wrong or not doing; (2) advice on a textbook that ...
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Regression assumption violation: lagged dependent variable as regressor
I am studying regression and a bit lost with conceptually understanding why having an independent variable correlated with the error term is a regression assumption violation.
Just to give more ...
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I have a VAR model, can I use the R-square values to explain how good the model explains the dependent variable and if yes, how will it be done
I have a VAR model, can I use the R-square values to explain how good the model explains the dependent variable (explanatory power of the model) and if yes, how will the values of the R-square be ...
