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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How to write the equation of Dynamic Factor Model built from DynamicFactorMQ() in Python?

The output below corresponds to the estimated Dynamic Factor Model and I want to know how to write the equation by referring to the below output. I used ...
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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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Dynamic Panel Data First Difference with gap in Independent Variables

I have a question about a dynamic panel data model with irregularly spaced data. Consider dynamic panel with lagged dependent variable: $$ Y_{it} = \rho Y_{it-1} + \alpha X_{it} + \mu_{i} + \epsilon_{...
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One-step prediction in JAGS of a dynamic model with unknown variance

I have the following problem. I have a linear dynamic model as follows: $$\theta_{0}\sim N(0,10)$$ $$v,w\sim \text{InverseGamma}(0.1,0.1)$$ $$\theta_{t}\sim N(\theta_{t-1},w), \hspace{0.3cm} y_{t}\sim ...
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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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What does mean to have the number of used observations greater than the sample size in the Generalized Method of Moments(GMM) result?

I specified my model as follow: The result below gives a value of the Sargan test around 0.3, which is good according to Roodman. However, the number of used observations is greater than the sample ...
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Dynamic panel with binary outcome

Suppose I have a dynamic panel data model with unobserved heterogeneity. $$ y_{it} = \alpha y_{1,t-1} + \boldsymbol{x'_{it}\beta} + \epsilon_{it} $$ $$ \epsilon_{it} = \mu_{i} + \upsilon_{it} $$ I ...
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Pooling dynamic panel models (dpm) with mice::pool in r

I have multiply imputed panel datasets that I have constructed with amelia. I put them in a list and constructed a corresponding list of ...
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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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Dynamic regression, models with coefficients = 0 chosen as top models

I am running auto.arima on part of a time series (training data) using all possible combinations for several external regressors. I then choose the top 5 models according to fit to testing data using ...
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Using Decomposition to Extrapolate seasonality, cycle and trends of predictors

I'm creating a dynamic regression model in which macroeconomic indicators are predictors/features in the model. I need to forecast these features n-steps into the future. I am planning to decompose ...
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Lagged variables in multilevel models

Is it okay to use lagged dependent variables as predictors in multilevel models? i.e. If there are $j$ groups of $i$ individuals each measured $t$ times, to model $$ y_{ij} = \beta_{0j} + y^{-1}_{ij} ...
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Dynamic poisson-time series regression

Basically, my question is similar to Time series dynamic poisson regression, yet I didn't get any clue by its answer. I'm modeling dengue incidents with GLM (poisson regression; because the response ...
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2 votes
1 answer
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Transfer Function Clarification

I'm seeking a little clarification on the specific application of transfer functions for time series. I've followed the Box-Jenkins approach for selecting potential exogenous predictors... using R's ...
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Regressing across multiple different time series using exogenous variables?

To make this situation clear, I'll use a somewhat silly, but conceptually simple example. Imagine I record teams of movers carrying furniture down the block. I measure the furniture's position/speed ...
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Multilevel dynamic linear models in R

I am interested in fitting a multilevel bayesian structural time series with a hierarchical structure of the dynamic regression coefficients. The reason I want to do this is is that I have a number of ...
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Modelling dynamic panel data

I have a question about a dynamic panel data model. Consider the panel data model: $y_{it} = \alpha_i + x'_{it}\beta + \delta y_{i,t-1} + \epsilon_{it}$. This cannot be estimated because $\alpha_i$ is ...
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3 votes
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Time series model for demand forecasting?

I have a time series $Y_t$ (example:university applications received in a certain month) which I want to forecast. I have another time series $X_t$ and I know that $Y_t$ is related to past lags of $...
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