Questions tagged [time-series]
statistical techniques for application to data whose observations concern an entity or phenomenon at different points in time.
235
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Forecasting and Structural Breaks
Let's say I am using walkforward evaluation for forecasting a certain time series with ARIMA and I want to test for structural breaks. And then I detect structural breaks. How can I use this ...
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Is there a good catalogue of time serries available from the Federal government?
I have two closely related questions that I will present here in separate postings. It seems that lately I have spent a great deal of time looking for particular time series that may or may not exist. ...
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Granger-Sims causality and subtle differences
For a bivariate process $(\textbf{X},\textbf{Y})=( (X_t, Y_t)^\top, t\in\mathbb{Z})$, we say that the process $\textbf{X}$ Sims-causes the process $\textbf{Y}$ (notation $\textbf{X}\overset{Sims}{\to}...
- 111
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Separating VAR results by value of dummy variable
Suppose I have a dataset of this form: it is a time series dataset with some variables $y^1_t$ to $y^n_t$, and some dummy variables $d^1_t$ to $d^m_t$.
Suppose I make a VAR model where the variables $...
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MSVAR with predetermined regimes
Suppose I have a VAR model that relates income, consumption and investment. And suppose I want the parameters to vary by the political party in power: A, B or C.
As far as I know, Markov Switching VAR ...
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can i use a variable for ardl testing
i have done unit root testing on my time series variable using both the adf and pp test. at level difference my variables all appeared non stationary.i used the option 'trend' and speratley 'drift' on ...
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30
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Analysis of seasonal adjustments and moving averages: seeking guidance
I'm looking to seasonally adjust the price data I have using a moving average with a 12-month window size. As window 12 is being used, the first 11 values in the 'Oil_adjusted' column are being filled ...
- 31
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How to formulate intra-day return?
So I have time series data on the bitcoin price. For each day there is an open price and a close price. In many papers they calculae the return like this:
$$ R_t = \Delta P / P_{t-1} = ln(P_t/P_{t-1})...
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Critical Value for Chow Test
What are the degrees of freedom for the F-distribution used to find critical values for the Chow Test?
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Does discount rate and cost of capital mean the same thing to compute levelised cost of energy (LCOE)?
I came to understand that cost of capital and discount rate are two different parameters, although they are also used ...
- 111
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45
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OLS estimation of AR and BLUE
Is it valid to apply OLS to the AR process?I learn that there exist correlation between explanatory variables and error term in AR model. I'm wondering how can show that $E(u_t|1,y_1,y_2,...,y_{t-1})\...
- 13
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Running vector autoregressions with non-stationary data
I am reading Diego Kaenzig "The Macroeconomic Effects of Oil Supply News: Evidence from OPEC Announcements". I have a question about the baseline specification and in particular the ...
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Which sample size would be best to investigate effects of War in Ukraine on volatility of stock prices using GARCH models?
I am planning to investigate the effect of the war in Ukraine on the stock market prices by including in a GARCH model a dummy identifying the burst of the war.
I am wondering if I should strictly use ...
- 21
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confidence interval for standard deviation of bitcoin price
I am actually doing some research on bitcoin for my bachelor thesis. I have daily data on the price of bitcoin from here. I calculated with python:
...
- 91
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Ex post predictability of "mps" in Bauer, Swanson (2022)
I am reading the paper "A Reassessment of Monetary Policy Surprises and High-Frequency Identification" by Bauer and Swanson (2022). In Section 2, there is a Bayesian updating model that goes ...
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How do I set up a staggered difference-in-differences design in R?
I have data which looks like this:
...
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80
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Regression OLS estimator with $x_t=1/t$
I found some questions in a book and wanted to train for my upcoming exam (expect to see some more posts from me idk), anyhow this is the question.
To solve point A I rewrote the whole equation in ...
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Why don't Time Series books talk about exogeneity like in classical linear regression models?
Consider the following linear regression:
$$y_t = \beta_0 + \beta_1 x_{t} + u_t$$
Typically, we need to assume (assuming a random sample):
\begin{equation}\label{I}\tag{I}
E[u_t]=0, \quad cov(u_t ,x_t)...
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Time series question from book (ma)
I'm trying to understand this subject and I'm stuck in a question found in a book.
I understand white noise characterization, but I'm having some problems with this... maybe it's too much theory? I ...
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Applications of a certain type of stochastic processes in macroeconomic, macroeconometric or finance
A compound Poisson random vector $Y$ is well defined in this site in wikipidia.
Nothing prevents me from compound strictly stationary stochastic processes instead of compound random vectors. The ...
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46
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Comparison of two AR(1) regression models (lead in dependent vs lag in dependent variable)
What is the difference between these time series AR(1) regression models (lead in the dependent vs lag in the dependent variable)?
$$\begin{align}y_t = constant + \alpha y_{t-1} + error \tag{1}\label{...
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The difference of a difference for a control variable in a regression?
I am conducting a time series analysis. My dependent variable is income inequality, which has been logged and then differenced. In other words, my Y variable is now the difference in log income ...
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2
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Include 'year' as an independent variable to deal with autocorrelation?
I'm working on an econometrics project for which I'm trying to study the impact of factors such as p.c. GDP growth, exports, inflation and interest rate on the debt-to-GDP ratio of a country, for 4 ...
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Machine learning and Inflation forecasting
Is machine learning a good tool to use in order to forecast inflation for the short term (next 2 - 3 years on a monthly or quarterly basis)?
I want to be able to forecast inflation for Canada for the ...
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55
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Deriving ADF unit root test form for the time series with quadratic deterministic trend
I have the following time series process $y_t $
$$\Delta y_t = \delta + \gamma t + \epsilon_t$$
where $e_t$ is white noise process with the variance of $\sigma^2$.
I guess that whereas $\Delta y_t$ is ...
- 120
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96
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Application of Maximum Likelihood estimation (MLE) to the Generalized Least Square (GLS) model
I have the following regression
$$y = X\beta +u$$
where $y$ and $u$ are $(n\times 1)$ and $X$ is a fixed $(n \times k)$ matrix with full column rank and $\beta$ is an unknown $(k\times 1)$ vector of ...
- 120
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The effect of stationarity of forecasting
If all that matters in forecasting is getting an accurate forecast then why is using non stationary data a problem. Say you use non stationary data to create a forecast that performs well in a pseudo ...
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1
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69
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How to assess the quality of a forecast?
Let's say I have a time series model (VAR model for example). How can I know that my forecast is good ? I could use the R2 but is there something else? I also know I could just use in sample ...
- 457
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Which variable keep/get rid in a time series model?
Let s say I have a big VAR model with many variables. Then I run the model. How can I know which variables I should keep or get rid of if I want to ameliorate my model ?
What if my model has so many ...
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can someone help to interpret ACF and PACF graph?
can someone help to interpret ACF and PACF graph?
in the graphs, I don't see any decay. am I right about that assumption
other than that what can I comment about both?
the data plotted is squared ...
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Is there granger causality that can be applied to panel data with structural breaks?
I have panel data containing various macroeconomic variables (for example: interest rate, output, unemployment).
I would like to run a Granger causality test on some of them, but there are structural ...
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A conceptual question about the limitation of the MA processes
We know that linear time-series techniques are frequently used in macroeconometrics. The Wold Representation Theorem states that any covariance-stationary process may be expressed as an MA process ...
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Proof that a Unit Root process is Difference Stationary
Consider $$y_t =a_1 y_{t-1}+a_2 y_{t-2} +...+a_p y_{t-p} +\varepsilon_t $$
The characteristic polynomial would be: $$(1-a_1L -a_2L^2 -...-a_pL^p) $$
Suppose that there is a unit root, say that $L=1$ ...
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Time series: predicting covariates for missing years from historical values
I'm working with a time-series dataset where my dependent variable is measured annually from 2018-2021 but my covariates are not all measured over the same period. For instance, $x_1$ is measured from ...
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What type of data we use to predict volatility of an asset with GARCH or ARCH models?
When we are trying to feed time-series data to a GARCH or ARCH model, what kind of data should we give the model?
A: Absolute difference between daily prices over-time
B: % of the difference between ...
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Which econometric model should I use?
I have the following model
Expenditures = a + $b_1$ Output + $b_2$ Labor + $b_3$ CapitalStock + $b_4$ D
where D is dummy variable that is Categorical. If tax is imposed, it takes 1. Or if tax is not ...
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Derivation of autocovariances Lewis (2021) RES
I am studying this paper, and I don't understand the derivation of the covariances at the bottom of page 3090.
Basically I have two shocks: $\varepsilon_{1t}$ has constant volatility $E[\varepsilon_{...
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26
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Modeling asymmetric effects
I was at a colloquium with the Bank of Canada and the presenter was showing some impulse response functions (IRFs) of rate change effects. Intuitively people tend to borrow more when rates decrease ...
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How to combine labor market time series when there is a methodological change that creates a break?
Any suggestions on how to combine time series of labor market variables (such as, for example, labor force participation) when there is a methodological change in the way the variable is calculated by ...
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Testing for serial correlation with General Regressors
This is from Introductory Econometrics (Wooldridge) 5th Edition page 420.
Consider:
$$y_t =\beta_0 +\beta_1 x_{1t}+\beta_2 x_{2t}+...+\beta_k x_{kt}+u_t$$
where $Cov(u_t, x_{jt})=0$ for all $j$ but ...
- 3,458
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How to create/replicate Diebold's Canadian Employment Index?
In his textbook "Elements of Forecasting", Francis Diebold presents on page 130 the following series describing it as the Canadian Employment Index, quarterly, seasonally adjusted.
The data ...
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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 ...
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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 ...
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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 ...
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1
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152
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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 ...
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47
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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. ...
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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 ...
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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):
$$x_t = \phi x_{t-1} + \phi_2 x_{t-2} + \phi x_{t-3} + \...
- 567
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$I(g)$-terminology
During my studies I came across the $I(g)$-symbol, in the sense that $Z_t \sim I(g)$ for an integer $g$ and $Z_t$ is a times series. What exactly does this mean, and does it have to do with ...
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How to recognize correlation in spurious regression case
Assume we are given two independent random walks
$$
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} \...
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