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I have a fixed effect model of the form $y_t = \alpha + \beta * x_t + \epsilon$ The response variable $y_t$ is stationary. I estimate $\alpha$ and $\beta$ using OLS. My question is, under above setup, ...
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I have a time series data, where 3 of 5 variables are non-stationary and 2 of them are stationary. I differenced the non-stationary variables to create a stationary data series, then tried to build an ...
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I have a doubt about the distribution of each single random variables in a stationary sequence. In particular, I found the following definition for stationary sequence: A sequence of random variables $...
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I came across a question that goes like this: For the time series $$ X_t = aX_{t-1} + 0.5 X_{t-2} + \varepsilon_t,$$ what should be the value of $a$ that makes it a I(1) process? And a note says: ...
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Fumio Hayashi in Ch. 1 "Econometrics" states (p. 3) A model is a set of restrictions on the joint distribution of the dependent and independent variables. That is, a model is a set of joint ...
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I have a quick question regarding persistent effects in non-stationary models. Suppose we estimate a VECM (cointegrating VAR) for two non-stationary variables X1 and X2 (in first differences) and ...
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So Here is the defintion of Stationary Time Series: A Stationary time series is a time series whose statistical properties do not change over time. My first question is what is over time actually mean?...
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The book of Hamilton points out we can either test only $\rho=1$, or test both $\rho=1$ and $\alpha=0$ in case 2 of the Augmented Dickey-Fuller test. But, whatever we do, the null hypothesis implies $...
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I have a question connected with DCC-GARCH models. Let's have K timeseries, some of them are I(1) series, some - trend-stationary (T) series. For TS time series I can substract trend directly (this ...
Dmitriy's user avatar
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Here is the AR(1) regression model: $$ y_t = \beta_0 + \beta_1 x_{t1} + \cdots + \beta_k x_{tk} + \varepsilon_t $$ $$ \varepsilon_t = \rho \varepsilon_{t-1} + u_t $$ $$u_t \sim \text{i.i.d. } N(0, \...
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Let's say that we have observations, $x_t$, of a stock price over some period of time ($t = 0, 1, 2,\dots$) and want to model future behavior of the stock price using stochastic processes/time series ...
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I am currently working through chapter 5 of Advances in Financial Machine Learning by Lopez de Prado, which focuses on fractional differencing. As an simple example, I am looking at time series of S&...
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I would like to ask regarding my research. I am going to use the cointegration tests Trace and Eigen, but I need optimal lags for them. Then I want to use VAR or VECM model, but it will be based on ...
Katarína's user avatar
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I have encountered different existence conditions for the $m$-th-order unconditional moments of $\epsilon_t$ in the EGARCH process defined as $$\epsilon_t=\sigma_t\eta_t$$ $$\eta_t\sim iid(0,1)$$ $$ \...
stat1002's user avatar
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To give an overview: I am currently conducting an interrupted time series analysis on daily social media data, focusing on posting frequencies. Initially, I attempted to implement a segmented ...
kalkun's user avatar
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