Questions tagged [time-series]
statistical techniques for application to data whose observations concern an entity or phenomenon at different points in time.
202
questions
2
votes
0answers
35 views
Taylor rule estimation with OLS serial autocorrelation
I'm estimating the equation:
$$i_{t}=\beta_0+\beta_1\tilde\pi_t+\beta_2\tilde y_t+\varepsilon_t$$
Where $\hat\pi_t=\pi_t-\pi^{target}$ and $\hat y=\ln y_t-\ln y^{\ast}$, are the inflation deviations ...
1
vote
1answer
29 views
What are good (i.e. non-random walk) time series to perform forecasting on?
I have developed a recurrent neural network (RNN) model for time-series forecasting. I now want to test its performance against more standard statistical/econometric models such as ARIMA or VAR. The ...
0
votes
0answers
10 views
Identify time vaying variables in VAR
I am having a panel VAR and would like to identify which varaibles remain constant and which vary over time. I have added time varition in the model.
I would like to avoid to use bayesian approarch . ...
0
votes
0answers
20 views
What are claims on a central government?
I downloaded some data from the World Bank's World Development Indicators database.
I found a time series called Claims on central government, etc (% of GDP) with code ...
1
vote
0answers
15 views
What is difference between Interrupted Time Series (ITS) and Regression Discontinuity in Time(RDiT) analysis?
Can anyone give some details on the difference between Interrupted Time Series (ITS) and Regression Discontinuity in Time(RDiT) analysis? How to choose between them? which one is more robust? Or some ...
1
vote
1answer
34 views
Cholesky Identification in Structural VAR
Can you suggest me a framework in macroeconomics or finance where identification of a Structural VAR model through Cholesky ordering is still considered credible (in your opinion)? I'm looking for a ...
0
votes
0answers
5 views
Converting from Quarterly to Monthly Data for Output Gap
I am currently working on the quarterly output gap data on FRED. However, I would like to convert the data series into a monthly one.
Would an appropriate methodology be to divide the dataset by 3 ...
1
vote
1answer
26 views
Filling gap in data with correlated series
I have two time series, of different length.
A time series is GDP growth.
The gdp growth is the series I need, and it is also the longer series, but it has two gaps in two periods one after the other. ...
1
vote
0answers
32 views
Deflating value series in currency A with CPI based on prices in currency B?
Say we have time series data on the annual value of imports in the UK from the US (in US dollars). Would it be appropriate to deflate the latter using the UK Consumer Price Index (which is based on ...
6
votes
3answers
280 views
Does I(1) imply a process is cointegrated with its lag?
My question is about the definition of cointegrated.
$y_t =y_{t-1}+u_t$
$u_t =\eta_t +0.5\eta_{t-1}$
where $\eta_t\sim N(0,1)$ is i.i.d. white noise.
I claim that $y_t$ and $y_{t-1}$ are cointegrated ...
1
vote
1answer
42 views
Algebra for two period forecasting in AR (3) Model
I wondered if some folks could help fill in a knowledge gap for me with some time-series algebra please regarding the following AR (3):
$$x_t = \phi x_{t-1} + \phi_2 x_{t-2} + \phi x_{t-3} + \...
1
vote
1answer
32 views
$I(g)$-terminology
During my studies I came across the $I(g)$-symbol, in the sense that $Z_t \sim I(g)$ for an integer $g$ and $Z_t$ is a times series. What exactly does this mean, and does it have to do with ...
3
votes
1answer
36 views
How to recognize correlation in spurious regression case
Assume we are given two independent random walks
$$
Y_t = Y_{t-1} + \varepsilon_{1, t}, \quad \varepsilon_{1, t} \sim \mathcal{N}(0, 1) \\
X_t = X_{t-1} + \varepsilon_{2, t}, \quad \varepsilon_{2, t} \...
0
votes
1answer
38 views
Interpreting an ADF test in R
Although being a mathematician, I am fairly new to time series and R. On an assignment I was being asked to check a time series for stationarity in R, only using the $\texttt{adf.test}$ function that ...
3
votes
0answers
13 views
Quantifying treatment effect in multivariate interrupted time series
I have a multivariate time series dataset, from which I am building an ITS (Interrupted Time Series) model by using facebook's Prophet to construct the counterfactual. Let's say I have a y variable ...
0
votes
1answer
53 views
Stationary vs. Non-stationary data in a BVAR model
I am replicating a paper using BVAR model and I first I have run the model with non-stationary data. Then I just wanted to compare the results with stationary data and I launched the model but I get ...
2
votes
1answer
35 views
Frequency and time of a time series suddenly changed, are usual methods valid
I have a time series data that used quarterly information on a variable, but has now shifted to bi-monthly releases, from September 2008 to July 2021. This is a survey based measure, released at the ...
3
votes
1answer
65 views
Durbin Watson Test for an AR(1) process
$(1) y_t =\beta y_{t-1} +\epsilon_t$
$(2) \epsilon_t =\rho \epsilon_{t-1} +v_t$
Where $v_t$ is i.i.d white noise. I know that OLS estimates of (1) are biased. It would then follow that estimates of $...
3
votes
0answers
46 views
AR(q) Strongly Stationary
Consider an AR(q) process, $u_t$. If the roots of a characteristic polynomial are outside of the unit circle, the AR(q) process is weakly stationary.
I've seen this proof that proceeds by showing the ...
0
votes
1answer
70 views
Estimation of variables
I am trying to estimate some variables, but I would like to know if what I am doing is right.
I am using some data about labour productivity (in log) and, by using Stata, I filtered (Hodrick-Prescott) ...
1
vote
1answer
96 views
Finding consistent but inefficient GMM estimate
Consider the following linear model
$$y_t = x_t' \beta +u_t$$
where $t =1,...,T$ and $x_t = (x_{1t} x_{2t} ... x_{kt})'$ , $ \beta$ is $k \times 1$ vector of unknown coefficients, $u_t$ is an iid ...
2
votes
2answers
34 views
Intercept in 2nd-stage Error Correction Model (ECM) regression — yes or no?
When doing a two-step ECM regression, do we add an intercept in the 2nd stage regression? I've seen course notes that add an intercept in the ECM, but some do not, so I'm confused if I should include ...
1
vote
1answer
33 views
K-step ahead forecast of VAR(2)
I am trying to determine how to write the K-step ahead forecast of a VAR(2), with two variables, as a weighted average of its mean and last observations. I understand that one must use companion form, ...
0
votes
0answers
38 views
IRF vs Autocorrelation function of AR(2)
Will the impulse response function and autocorrelation function of an AR(2) coincide? I say that they will not, because $IRF_{t}(t+k)$ (that is, the effect of a unit shock at time $t$, on the process ...
0
votes
0answers
15 views
Best method for extracting cycle component of time series
I have macroeconomic time series data, including both quantity and proportion variables (i.e. aggregate consumption, unemployment rate, etc.). I want to work with the rawest version of the data, ...
1
vote
1answer
22 views
How to obtain real money balances data
Theoretically real money balances ($m_t$) are defined as:
$m_t=\frac{M_t}{P_t}$
Where $M_t$ are nominal money balances, and $P_t$ is the price index of the economy. If I were to make an empirical ...
1
vote
0answers
44 views
Detrending multiplicative model
I have a GDP quarterly series and IND.P monthly series, which I need to detrend. I am following Bernanke et al. paper from 1995 and the way they do it is:
...
0
votes
1answer
27 views
What does "sample period" in this case mean?
Dasgupta,2019 documented that
the treated group comprises all firms that are headquartered in
countries that have passed a leniency law by year t. The control group
comprises firms in countries that ...
0
votes
1answer
98 views
How to handle the missing values' issue of newly listed stocks?
I am trying to test some asset pricing models on 10 portfolios for the period of 2010-2020. The problem is that three of these portfolios included stocks that are newly listed in 2017 and 2018, so I ...
1
vote
2answers
53 views
Examples of the use of Vector Autoregressive Models
I am self-learning Vector Autoregressive Models currently, and have practiced on a few datasets. But I wanted to read some actual research papers that use VAR, so that I get an idea of the level of ...
1
vote
0answers
29 views
What is the importance of moving seasonality in seasonal adjustment?
Both Stable and Moving Seasonality are computed in Sliding span test (or D8 F-Stat test) and M7 statistics. But what is the purpose of finding out the presence of moving seasonality in seasonal ...
1
vote
0answers
28 views
Modelling GDP growth rates with an AR(1) process
I am trying to model the growth rates of real output per worker. I read that GDP growth rates are often assumed to be stationary and are modelled using an AR(1) process, so I took logs and then took ...
0
votes
1answer
21 views
Nominal Values or Real Value & stationary
I'm going to estimate a time series regression which has consumption as a dependent variable.
My data source give it in nominal form and unfortunately there are no other sources. It is
Question :
Can ...
1
vote
1answer
127 views
Converting monthly data to quarterly
I have monthly shock series, which I want to convert to quarterly form. I have seen several methods like taking average of 3 months or summing 3 months for making a quarter. I would like to know how I ...
0
votes
0answers
21 views
rebasing an index with inconsistent ratios between old and new data points?
I have a question about rebasing indices, hoping someone might be able to help.
I'm working with two sets of index data for residential property prices from the Australian Bureau of Statistics. the ...
3
votes
0answers
21 views
Extract residuals from Uhlig's rejection method - Lag selection
I wanna determine the number of lags for a Baysian VAR/FAVAR model, which I implement based on R's VARsignR package. To compute the AIC/BIC I'd need the residuals of the model estimation. How can I ...
2
votes
1answer
35 views
Regressing (Very) Smooth Time Series
What are the possible problems/issues of regressing smooth time series with almost no fluctuation?
Here is a specific example.
Is there anything I have to pay attention to when interpreting ...
2
votes
0answers
30 views
Do you know how to compute the IRF of a GARCH (1,1)
We have the following model (GARCH (1,1) )
$y_t=\sigma_t\epsilon_t$
$\sigma_t^2 = \omega + \beta*\sigma_{t-1}^2 + \gamma*y_{t-1}^2$
Note that we can rewrite the latter as:
$\sigma_t^2=\frac{\omega}{1-\...
4
votes
1answer
99 views
Should Prices (or Price Indices) be modelled with deterministic trend?
I always face a dilemma on whether to assume prices to have a time trend or not while modelling. It is also partly a statistics problem. Let me explain. Assume I have time series, $y_t$ of price of a ...
4
votes
1answer
495 views
Stationarity vs weak dependence
I am doing an undergraduate course in econometrics where we are using the text Introduction to Econometrics by Dougherty. While going through time series, it was mentioned that one of the necessary ...
0
votes
0answers
29 views
Could a time series be a pooled OLS?
Hello to everyone on the forum,
I am currently working with intraday electricity price data. In the electricity market each hour is considered as a different product, because the demand and production ...
0
votes
0answers
25 views
Comparison of coefficients in log(y_t) and log(y_t/y_0) LHS specifications in LP-IV
I would have a question related to econometrics. Likely not all the details are needed, but please bear with me.
My goal is to use local projection with an instrument to find out the response of an ...
0
votes
1answer
32 views
Stationarity, ADF/KPSS, Autocorrelation and Heteroscedastiy
i have a time series which is not stationary due to ADF/KPSS test, but is is in its first difference. So ADF and KPSS tell me it is starionary so it has a constant mean/variance/autocorrelation. But i ...
1
vote
0answers
23 views
Does GMM have any assumptions that you can't test empirically (and must instead argue qualitatively for)?
My understanding is that you can empirically test some of the main assumptions required for using a GMM estimator. Namely, I understand that you can test over-identifying restrictions with Hansen's J ...
0
votes
1answer
38 views
Variance in the Context of a AR (1) Model
I wondered if someone could help me in terms of the required algebraic steps from expressions (3) - (4), for the the moving average representation of the AR (1) below?
Would be appreciated.
$$y_t=a +\...
1
vote
1answer
173 views
Recursive Substitution in Time Series
Would appreciate some guidance on a matter of recursive substitution, where we have the AR model:
$$y_t = \alpha +\theta_1y_{t-1}+ u_t$$
And
$$E(y_t)= \mu_t$$
Where:
$$\mu_t = (1+\theta_1 + \theta_1^2+...
0
votes
0answers
45 views
What type of econometrics is practised most?
This might sound like a weird question, but what type of econometrics is most common in academia and / or the private sector?
Here I am thinking about time series econometrics (Bayesian and ...
1
vote
1answer
21 views
How does using HP filter allows us to estimate long-run values for variables such as output and unemployement?
I'm reading the paper "Okun's Law: Fit at 50?" written by Laurence Ball, Daniel Leigh and Prakash Loungani.
In it, in order to estimate Okun's Law in its level form, they use the HP filter ...
1
vote
1answer
65 views
Should one remove trend from time series before testing for cointegration?
Should one remove trend from time-series before testing for cointegration? I guess no, but I couldn't find any answers yet.
Also is it necessary to remove trend before estimating a VAR model if the ...
0
votes
0answers
54 views
Deconvolution in economics
Here it says
In mathematics, deconvolution is an algorithm-based process used to
enhance signals from recorded data. Where the recorded data can be
modeled as a pure signal that is distorted by a ...
