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Questions tagged [time-series]

statistical techniques for application to data whose observations concern an entity or phenomenon at different points in time.

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General conditions in which dynamic panel effects are bounded by OLS and FE models?

Typically under the model that looks like this: $$ y_{it} = \rho y_{it-1} + u_i + e_{it} = \rho y_{it-1} + v_{it} $$ Define the OLS estimator as the estimator for $\rho$ that estimates the following ...
Daycent's user avatar
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For a diff and diff should I use stationary series or non stationary?

I am trying to do a diff and diff analysis on the impact of QE for US (treatment) and Canada (control). What kind of series should I use and what dummys should I include?
user47940's user avatar
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Simplest explanation of “Why isn’t an asset that produces recurring reliable dividends worth infinite money?”

Let’s say there exists an asset that is known to produce $1 of liquid value annually, indefinitely. In practice, this asset is worth a finite amount of money. However, to a naive intuition one could ...
escapecharacter's user avatar
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Good practice in time series tests with different start/end dates

I am testing purchasing power parity on monthly data, in a sample of about 70 to 200 countries depending on which price index / currency pair is used. Would it be considered bad practice to use ...
Pavel Filip's user avatar
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2 answers
106 views

Why is GDP a random variable?

I have seen in time series models that GDP is considered a random variable. At first, I found this troubling because I could not see any random process underlying the measurement. However, the ...
Santiago Valdivieso's user avatar
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Why given real and manually deflated series are not same in FRED?

when I deflate a series (for specific example, take M1 (M1SL) series from FRED) by CPI, it is not exactly the same as the one the that FRED provides i.e. M1REAL. It seems like M1REAL is a scaled up ...
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How to interpret detrended data in GDP modelling?

So I detrend 5 variables that I fit in an Almon model to predict GDP. The problem is beforehand I detrend them and their values become much smaller. Also, I detrend the GDP itself and it becomes ...
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How to Find the Relationship Between Variables in Time-Series Data?

I would like to find the relationship between two variables (spending and earning) in time-series data and I am wondering if VAR would be a good way to approach this? If not, which approach(es) would ...
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What should I do before an OLS regression and Johansen cointegration test with time series data?

I have to make a regression with real exports as the dependent and then an aggregated GDP income variable and the real effective exchange rate as independent variables. My variables are time series (...
ORESTIS TZIAMALIS's user avatar
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Help with Simulating a Time Series Process in R

I'm having difficulty expressing the following process in its most reduced form. Every time I try to simulate it with the arima.sim function, I end up with a non-stationary AR part that cannot be ...
NoNameBoyy's user avatar
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Dickey Fuller Test

Consider the following AR(1) model: \begin{equation} Y_t = \alpha + \phi Y_{t-1} + \varepsilon_t \end{equation} I want to test the existence of a unit root for $Y_t$, and thus, I intend to implement a ...
Dimitru's user avatar
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Stationarity in Time Series

Could you illustrate why a random walk process without a constant term exhibits stationarity in its first moment but not in the second?
Dimitru's user avatar
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Self-selection problem can be solved in panel data analysis?

Assume that there are two variables $x$ and $y$ and they both have trend. For example, (case 1) During covid-19, government spending($x$) and inflation($y$) are both increased (case 2) A productive ...
guest's user avatar
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Normalization for model comparisons

I have a time series applying the Markov Switching model, which is estimated in about 15 different versions. One or two of the time series had to be normalized in order to converge. That is 1-2 out of ...
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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 ...
a12345's user avatar
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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}...
Albert Paradek's user avatar
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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 $...
Ishan Kashyap Hazarika's user avatar
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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 ...
Ishan Kashyap Hazarika's user avatar
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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 ...
david's user avatar
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1 answer
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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 ...
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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})...
BlankerHans's user avatar
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237 views

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 ...
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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})\...
user_A's user avatar
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2 votes
1 answer
51 views

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 ...
Gian's user avatar
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1 answer
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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: ...
BlankerHans's user avatar
2 votes
0 answers
168 views

How do I set up a staggered difference-in-differences design in R?

I have data which looks like this: ...
econdid's user avatar
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2 votes
0 answers
90 views

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 ...
Blipo's user avatar
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2 votes
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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)...
André Goulart's user avatar
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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 ...
Blipo's user avatar
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2 votes
0 answers
48 views

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 ...
Letícia Fagundes's user avatar
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2 answers
59 views

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{...
Shushue's user avatar
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1 vote
1 answer
34 views

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 ...
Matthew Farquhar's user avatar
1 vote
2 answers
77 views

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 ...
viktor nikiforov's user avatar
1 vote
1 answer
122 views

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 ...
eddie's user avatar
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1 vote
0 answers
55 views

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 ...
studentp's user avatar
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0 answers
163 views

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 ...
studentp's user avatar
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1 vote
1 answer
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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 ...
Alex Stephenson's user avatar
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1 answer
87 views

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 ...
BAL's user avatar
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0 votes
1 answer
30 views

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 ...
BAL's user avatar
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0 answers
37 views

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 ...
Sarthak Gurnani's user avatar
1 vote
0 answers
13 views

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 ...
csilvia's user avatar
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2 votes
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48 views

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 ...
Fam's user avatar
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4 votes
1 answer
69 views

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 $L=1$ ...
Michael Gmeiner's user avatar
1 vote
0 answers
12 views

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 ...
akm's user avatar
  • 131
0 votes
2 answers
55 views

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 ...
Tiago's user avatar
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1 vote
2 answers
66 views

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 ...
studentp's user avatar
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1 vote
1 answer
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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_{...
Giorgetto's user avatar
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27 views

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 ...
tjaqu787's user avatar
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