time series analysis

  1. K

    Time Series Analysis with Gaps in Data - is it possible?

    Hey everyone, I am currently doing a coursework for uni and I need some help. I don't have a background in stats and this is not a statistics course so I am confused. I have a dataset of daily air quality, which have numerous gaps - some small (one day missing), some big (over a month of...
  2. L

    Time series analysis - residuals arent stationary

    Hey everyone, i'm struggling with modelling some times series to get residuals with white noise characteristics. I use SPSS for ARIMA modelling and exponential smoothing and Gretl for stationary testing with the Augmented-Dickey-Fuller and the KPSS-Test. My workflow: At first I use Gretl to...
  3. L

    Time series analysis - different results for same cross correlation in SPSS?!

    Hey everyone, i have a question about the results I get when I do a cross correlation of two white noise time series with SPSS 21.0. Why do I get two different results depending on how much variables I insert in the window for the cross correlation? For example: If I insert the variables...
  4. R

    how to create a portfolio using xts time series

    Hello, I have already corrected the stationary of my elements with diff, but to analyze the VaR (), CVaR (). It is necessary to create a portfolio, I have seen many packages that have left me confused. How is the procedure to create a crypto portfolio? that is I must assign all the columns of...
  5. S

    Country comparison over time with growth rates

    Hello there! I am struggling at the analysis part of my thesis. I want to compare countries according to their growth rates over time. So in other words, do they increase or decrease over time more than the others or less… I saw that time series panel data regression with dummy coding for the...
  6. K

    Variable that should appear significant is insignificant

    As part of my University research project, I have decided to study the impact of Immigration on house prices but find that the immigration Variable that should be statistically significant according to literature appears to be insignificant. I don't know whether this was down to the method I...
  7. M

    Time Series Analysis: time lag and decomposition

    Hi everyone, currently I'm working on a project where I want to test effects of external indicators on internal sales data. So I already collected a lot of data and now want to compare two time series. But there are some things I'm not sure about: Is it recommendable to decompose each time...
  8. B

    autocovariance and power spectral density

    I need to find the solution to this question, could someone help? Consider the stochastic process {Xt;t ∈ Z}. Let fX(λ) = |1 +1/3 eiλ|2 be the spectral density function and RX (t) be the autocovariance function of {Xt; t ∈ Z}. What is the value of RX(1).
  9. A

    plot mean value according to confidence interval

    Hi all, i have a very simple question, because i am not good at statistics but i need that :) I have two signals, one is lets say 'x' another is 'time', so its a time serie. I need to find the upper and lower values of my signals according to confidence interval and then show them on a graph...
  10. M

    Two ways to include temporal autocorrelation

    Hi, assume that we have time series with autocorrelated values described by the regression model Y_i ~ X_i. As far as I understand, in AR-regression models, this correlation is considered by assuming that residuals are autocorrelated. Instead, I could introduce additional predictors...
  11. K

    Measuring dependence over time?

    Hello there, I would like to know what would be the best method to measure the relationship between variables like life expectancy and income over time (years)? In my case I have a time series with around 30 years. I would like to examine if there is a dependence between variables like life...
  12. M

    time series analysis

    There is an aggregated measure represented by a variable A, modeled as a time series from a process. There was a need forecast A and also to find out the historical amount of data of A that is the best reflector of future values of A (as there was a data storage capacity issue). Using a...
  13. M


    There is an aggregated measure represented by a variable A, modeled as a time series from a process. There was a need forecast A and also to find out the historical amount of data of A that is the best reflector of future values of A (as there was a data storage capacity issue). Using a...
  14. M

    Time Series Analysis

    Describe what role Exploratory Data Analysis (EDA) has in the application of time series modeling. Include: (a) Why is testing for a normal distribution important? (b) Why is testing for skew and kurtosis important? (c) What role does hypothesis testing play? (d) What distribution is used for...
  15. A

    Instrumental variables with S&P 500 Health Care Index

    Hello everyone, I am new to this forum and I would need some help on a time series regression model with financial variables. I am regressing Y=S&P500 Health Care index c X=bookmaker odds for the victory of Hillary Clinton x=S&P500 x=USD/EUR everything is in dlog, and I use the lag of...
  16. M

    When to use Augmented Dickey Fuller test vs Dickey Fuller Test - Time series

    Checking a variable for I(1) process, when should i use a ADF vs DF test?
  17. G

    Granger casuality: which kind of variables can you compared?

    Good morning and thanks in advice for your time. Can I use Granger casuality for studying if a financial variable (stock price of a company) can be caused by a non financial variable (like the number of hours the employers work)? If not, do I have to consider my non financial variables as...
  18. S

    Interpreting VECM/ ECM result

    Hi, I am currently working on a paper where I run a VECM and get significant values. However, I am not quite sure about how to interpret the output and how take the different values and express them with an equation. I run the test in SATA using the vec command Attached is a picture of...
  19. S

    What tests and analysis for 50 year data collected on fashion trends

    So, it is said that whatever is in fashion once comes back someday or the other. We have collected data on 10 different garment types, each having 5 different sub-types on what was in fashion in 50 years. (Eg- Jeans->Waist->High/Low/Mid-rise) We plan on assigning each sub-type a grade/number...
  20. A

    R Arima Equation Question - Please help!

    Hello, I am new to R and I am trying to conduct a time series ARIMA analysis for my work.