# Tagged Questions

The autoregressive (AR) model is a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.

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### Error correction model: Interpretation, confidence bounds

I have estimated an error correction model (ECM) regressing Household Debt on GDP. I am trying to show that Household Debt and GDP are two forces in a two-equation system that will cause a cycle. In ...
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### How to interpret the coefficient of an error correction model (ECM)?

I have run an error correction model. The coefficient is -0.6. I would like to know how to interpret this result.
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### Writing a function n R [closed]

Hello, How Can i write this function in R? and any simulation codes about SETAR model including codes, links, books or any guide, would be appreciated.? Thanks in advance
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### How do you define long and short run in an ARDL model?

I am writing up my regression analysis of an ARDL model which includes the long run equation and the short run dynamics. My reader however, would like to know what I mean by long run and short run. ...
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### spss neural network forecasting with lags

I have 240 monthly data points and would like to leave 36 out of sample for neural network forecasting in SPSS. I made the covariates as lag of 1 [AR(1)] and have several questions to ask: What are ...
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### AR(2) process: are leptokurtic residuals OK?

I have a time series of logarithmic returns. After inspection of the ACF and PACF plots, I tried to fit AR(2), MA(2) and ARMA(1,1) models and eventually found out that the AR(2) version can possibly ...
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### Where can I find sound information about Periodic Autorregressive Multivariate models?

I am reading an article that mentions Periodic Autorregressive Multivariate Models and their noises; however, in no section have the authors explained or shown references to these models. I looked ...
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### In an ARDL model is it mandatory to conduct short run and long run test? [closed]

I am wondering if I can only report on the long run analysis only?
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### What are the steps in using ARDL model in regression? [closed]

I am running a regression model by using an ARDL model. Can someone outline the necessary tests I must conduct, please?
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### How to reduce the residuals by autocorrelation of residuals?

I have two long matrix with Observations and Predictions, with 76 columns each. I need to reduce the residuals by means of autocorrelation of error correction. I also would like to select the best ...
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### Causality and stationarity of AR models

Studying AR models, I found that there are two properties that these models can have stationarity and causality. For what concerns stationarity, I have studied that this condition is satisfied if ...
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### In the midasr package in r, how is the AR* model different than the AR model?

At the end of the ?midas_r documention example what is the fourth parameter option in the mls() of the lagged dependent variable "*" doing that is different that the "regular" AR(1) model above? I've ...
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### Modelling auto-correlated binary time series

What are the usual approach to modelling binary time series? Is there a paper or a text book where this is treated? I think of a binary process with strong auto-correlation. Something like the sign of ...
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### Solving for a difference equation for $s_{t}$

Given $f_{t}=u_{t} - \bar{P}$ and the law of motion for $u_{t} = \rho u_{t-1} + \epsilon_{t}$, where $0<\rho<1$, $\epsilon_{t}$ is mean-zero iid and can be interpreted as a domestic price level ...
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### Coeffs change using Yule-Walker vs. ML autoregression correction

When estimating a small-sample time series regression (in SAS, using PROC AUTOREG), I was surprised that the coefficients changed so much when changing from Yule-Walker to maximum likelihood. The ...
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### Distribution of two correlated multivariate normal distributions

Let $\varepsilon_{s}^{FR}$ follow a Gaussian Markov process so that \${\varepsilon_{s}^{FR} = \rho \varepsilon_{s-1}^{FR} + \xi_{s}^{FR}, \: \xi_{s}^{FR} \sim \mathrm{i.i.d.} \: N(0,\sigma^{2}_{\xi^{...