Questions tagged [dynamic-regression]
Dynamic regression is a type of regression, where one of the independent variables is a lagged dependent variable.
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Nickell Bias in dynamic Fixed Effect model for large T
From various sources and reading, I get to know that, the Nickell Bias associated with employing fixed effects with a lagged dependent variable is small when T is large. I understand the notions ...
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model for dynamic panel with the effect of y(t-1) to x(t)
I am not sure if my model is dynamic or vector auto-regressive
The model
Variables: X(t), Y(t) and C(t). Where C(t) are control variables
time t1, t2, ... tn
At any time t (take t=t2) then we have ...
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Is there a way to do Dynamic Harmonic Regression in R using multiple variables?
The company I work for would like to forecast weekly transactions, given a certain weekly sales budget (i.e. predicted weekly sales) for a period of time.
We are a highly seasonal business, ...
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Short-term gas demand forecasting
I'm a 22 year old Statistics student with a big problem to solve. English isn't my first language, so I apologize in advance for any mistakes in my grammar.
I'm trying to make a short-term gas ...
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Possible statistical modeling method for identifying dynamic influence of different factors over time
I'm trying to build a statistical model which could capture the dynamic influence of different factors over years, for example, customers who buy a specific brand over different years, and I'm trying ...
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How to measure correlation between x and y where you are unsure, if there is a delay between the two?
It might be easier to describe a scenario, lets say the is variable x (inflation rate) and variable y (the amount spent on holiday flights).
If you were interested in the correlation between x and y ...
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Estimate an ARMA disturbance model from measurement output data
Assume that we have a first order dynamical system
$$G(s) = \frac{1}{0.2s + 2.1}$$
I run this with an input $u = 10sin(t)$ and with gaussian noise.
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dealing with serial correlation in dynamic panel model (pgmm)
For my bachelor thesis I create a differentiated dynamic panel model with individual effects. In R, the summary of the pgmm() object also outputs two ...
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Forecast Assistance given Monthly Time Series Data
I'm looking for some help to determine what type of model I should use for the given data set:
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R: dynamic model with GMM estimator in first difference
To summarise the problem, I have a project in R where I need to study the correlation between the means of promotions of each of the last 6 months on the sales of the current month
I have a panel data ...
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Help in interpreting Dyanmic regression results
I study the effect of a change in positive and negative sentiment on the weekly change in different Cryptocurrency returns. For my regression I'm using lagged values of the independent variables to ...
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Dynamic linear Regression model using dynlm
I came across the package "dynlm" which offers the function dynlm to create lagged independent variables.
I have 3 time series as Independent variables (PSVI,NVSI,BTC_RET) and I want to ...
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Is there a way to tell when a time-series can no longer be predicted by the same model?
I am modeling a time series using a multiple (dynamic) linear regression model. I suspect that at some point, the model no longer accurately predicts the true series. Is there a way to find the point ...
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Autocorrelation of the independent variable (Panel setting) with R
I have panel data with only a few individuals and time points and, after testing accordingly, I would like to create an RE model in R. For this I have some questions:
I found out that I have ...
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Is there a test/model to tell whether an individual deviates from a group?
I'm working with time-series data on a handful (about 12) of individuals. Under normal conditions, the variable of interest for each individual is correlated among the group, i.e. if ...
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Difference between Dynamic Factor Model and Factor Augmented VAR?
One simple question:
What is the difference between the dynamic factor model and factor augmented vector autoregressive (FAVAR) model?
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Auto. Arima and ARIMAX for multi variate time series forecasting
I'm trying to do multivariate time series forecasting using the forecast package in R. The data set contains one dependent and independent variable. From the cross-correlation the 0 day lag of the ...
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Regression modelling options for time-varying contribution of exogeneous variables
When building a regression model for observations in time $y_t = f(\beta, x_t, \varepsilon_t)$, I want the magnitude of $x_t$ to have different contribution to $y_t$ at different time moments. I ...
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Can I apply the same DCC-GARCH model on sub-samples to investigate differences in co-movements?
I am using DCC-GARCH for a master thesis in which I am investigating the co- movement of the Green bond and other markets pre and during the current economic crisis. Based on the signficant ARCH/GARCH ...
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Implications of insignificant dccalpha and dccbeta for DCC-model used for co-movement analysis
I'm using a DCC-ARMA(1,0)-GARCH(1,1) model to investigate co-movement of the green bond market and other markets. The ARCH/GARCH parameters of the univariate ARMA(1,0)-GARCH(1,1)models are significant ...
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Interpretation of dccalpha and dccbeta in DCC-GARCH model
I've used DCC-ARMA(1,0) -GARCH(1,1) to model green bond co-movement with some other marekts. In the output, I get the parameters "dccalpha" and "dccbeta". However, I do not know ...
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How can I implement a dynamic linear model in R?
Can someone explain me how to implement a dynamic linear model in R?
The concept is similar to a transfer function, which mathematically is defined as:
$$
y_t=c+w(B)x_t + N_t
$$
Where $y_t$ is the ...
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A VECM in logs or a ARDL in the first difference of logs?
Suppose I have a number of time series that appear to have exponential growth at similar rates, with errors I believe to be generally proportional to the level of the variable. I believe that one of ...
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Penalizing autoregressive models (with R packages)
Consider the class of linear or quasi-linear models that include an autoregressive term, such as autoregressive distributed lag models, ARIMA models, VAR and VECM models, and so forth. In general the ...
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Dominance analysis in linear regressions with ARIMA errors
I have a question regarding dominance analysis in linear regressions with ARIMA errors. I am currently working with stress models for the banking industry. In certain cases, we are using dynamic ...
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Intercept in a dynamic panel model
I have been taught that including fixed/random effects in a dynamic panel model yields inconsistent estimates when using OLS and hence motivates the usage of other estimation methods.
However, does ...
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Skepticism about the claims of instrument variable validity/exclusion through a statistical test—the Arellano-Bond Test (cross-posted)
I am an applied researcher and occasionally come across papers that have panel data and that use dynamic models with both a fixed-effects term and lagged DV (or multiple autoregressive terms):
$y_{it} ...
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Choice between static and dynamic panel regression
I have a panel dataset with countries as individuals observed per year. My analysis concerns a macroeconomic study and as often happens in these cases (I would not be wrong but they are commonly ...
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Is it possible to create a binary classifier from bayesian network?
I have a labeled data set consisting of longitudinal data and I would like to train a dynamic bayesian network. The output should be probability of the observation being 1 in a selected step given the ...
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How can I get LSDV estimator of a two-way dynamic panel model?
I am now trying to explain a dynamic panel data model as follow:
$y_{it} = \alpha y_{i,t-1} + x_{it}'\beta + \mu_i + \lambda_t + \nu_{it}$
Here I want to compute its LSDV estimator $\tilde\beta$, ...
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What are issues or best practices related to using fitted values from one model as predictors in a second regression forecast model?
I am creating two models, where the fitted values of one SARIMAX type model are used as an exogenous predictor for a second dynamic regression model. I want to understand what assumptions of the gauss ...
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Can we use pooled ols on dynamic panel without individual fixed effect (but with group fixed effects) and no serial correlation in error
Is it correct that dynamic panel without individual fixed effect (but with group fixed effects), no serial correlation in error can be estimated consistently through pooled ols?
For example, suppose ...
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Penalizing non-OLS models
Let’s consider some common linear time series models for which OLS does not usually yield unbiased coefficient estimates. These include ARIMA and ARIMAX models, regression models with ARIMA errors, ...
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Product of independent beta distribution and gamma distribution
I'm reading Bayesian Forecasting and Dynamic Models by Mike West and Jeff Harrison. The following conclusion is about the variance discounting in dynamic linear regression model. On page 362, it says,
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How to get first difference of count variable for poisson regression
Poisson regression using Panel data requires the dependent variable ($y_{it}$) to be a non-negative count variable. I need to take the first difference of the dependent variable to deal with reverse ...
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Transfer Function in Dynamic Linear Model to Quantify Intervention Effect?
I have a time series consisting of 48 quarterly observations, relating to the area of land that was subject to development. I am trying to investigate whether a change in policy after 2011 caused an ...
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How to build a function with the result of arima in R?
I use: arima(y, order = c(3,1,1) in R to get ARIMA(3,1,1), result as follows:
Call:
arima(x = y, order = c(3,1,1))
Coefficients:
...
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Differences between Dynamic Regression Model and Intervention Model?
I am currently studying on dynamic regression and intervention model but I haven't have much resources on hand about the differentiating both of them. I understand that intervention model is a special ...
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Why does the Anderson-Hsiao adjustment pick the twice-lagged first difference?
My understanding is that the Anderson-Hsiao adjustment, described here as an example, addresses concerns about potential or actual autocorrelation by taking a first difference of the dependent ...
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Algorithm for dynamic linear regression with stochastic volatility?
Is there any paper or textbook on how to estimate dynamic linear regression model with stochastic volatility?
The observation equation and state equation,
$$Y_t = \beta_t'X_t + \epsilon_{t}$$
$$\...
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Data preprocessing for dynamic linear regression
In DLM, what kind of preprocessing should be done before fitting the model?
$$Y_t = \beta_t'X_t+\epsilon_t$$
$$\beta_t = \beta_{t-1}+\eta_t$$
Should I transform both $Y$ and $X$ into stationary time ...
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Standard errors with delta method
Trying to recreate other author's results. E.g. this paper. Introduction to the model is on page 10, while table with results is presented on page 13. Under the table there's a small note that SE were ...
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How to run a Sargan-Hansen test?
I'm estimating a model with a lagged dependent variable and fixed effects for panel units. I understand that this can result in Nickell Bias but I didn't think it would be a big problem because there ...
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Best practice for dynamic panel data estimation with multilevel structure in a $T \gg N$ setting
We plan to estimate a dynamic panel model with both, varying intercept and varying slopes. Further, we also want to include group-level predictors for the varying effects in second-stage regressions.
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Training model vs model on whole data in time series forecasting in r
I have daily time series data of almost two years starting for Jan 2018 to Nov 2019 and need to forecast for next two months Dec and Jan.
My train data(Jan 2018-Aug 2019)is up to Aug 2019 and its ...
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Linearisation of Kalman filter
Assume we have the following state-space model:
$$
z_{k} = \theta_{k} z_{k-1} + v_{k}\\
\theta_{k} = \phi \theta_{k-1} + w_{k},
$$
where $v_{k}$ and $w_{k}$ are independent and normal for all $k$. The ...
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Can I estimate base sales in a Marketing Mix Model (MMM) with muddled promotional data?
I am building a marketing mix model (MMM) for an online casino, but they regularly run promotions and marketing campaigns that overlap. Trying to determine a baseline level of sales has been difficult....
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Covariance in system with lagged reverse causality
Is there an easy way to find the covariance between $x_t$ and $\epsilon_t^1$ in a system like
$$y_{t} = \beta x_{t} + \epsilon^1_{t}$$
$$x_{t} = \alpha y_{t-1} + \epsilon^2_{t},$$
potentially under ...
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Auto.arima coefficients with exogeneous variables
I have the following output from the auto.arima function with specified xreg:
...
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Calculating standard errors for long-run (cumulative) multiplier in a Distributed Lag model
I have a distributed lag model of the form:
lm(wellbeing ~ temperature + temperature_lag1 + temperature_lag2 + time + individual, df)
where I'm interested in ...
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