time series analysis

  1. 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...
  2. 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).
  3. 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...
  4. 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...
  5. 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...
  6. 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...
  7. 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...
  8. 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...
  9. 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...
  10. 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?
  11. 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...
  12. 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...
  13. 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...
  14. 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.
  15. purifyz

    Which type of time series to use?

    Hey, I have a data set which includes weekly/monthly sales and questions about customer satisfaction. I have already done EFA/CFA based on these satisfaction questions (turns out it's just one satisfaction factor) I would like to regress sales on satisfaction, but I think I should include a...
  16. Y

    Variance ratio and Hurst exponent tests

    Hi, I have used the Chow Denning test and the Hurst exponent (Peng, Whittle and R/S methods) to examine if a particular time series follows a random walk. My results are conflicting between the 2 tests. From what I can fathom, the Hurst exponent does account for multiple variances (although I...
  17. E

    Descriptive statistics on dummy variables in financial time series

    I got daily returns for the last 15 years. I would like to find the the descriptive statistics(mean,Stdev,skewness, kurtosis) impact on last Thursday of every month. I have created a dummy variable to reflect the same. How to find the descriptive statistics only for those days alone?
  18. U

    How to run a lagged (time series?) model in R?

    Hi there, I am wanting to run a lagged model where a predictor (X) at T1 is regressed on an outcome (Y) at T2, controlling for Y at T1 plus 2 covariates at T1, over a total of 20 time points (so as to test whether X causes Y). I could use a cross-lagged SEM in Lavaan, but most papers I've...
  19. T

    Time series Analysis : Vector autoregression in R

    I am finding relation between two time series M & M1. M and M1 both found to be stationary at first difference and also cointegrated at first difference. Using VARselect in R,I found out 4 as lag length for M and 6 as lag length for M1. Then I have tested for Granger and I got ...
  20. T

    VECM interpretation - Johansen-Procedure

    X1 , X2 , X3 and X4 are time series which are stationary at level.I want to establish long term relation between them.I am planning to use it as forecasting model for my work.I want to create this model in terms of equation.I have tested all of them for KPSS test and got p=0.01 for all of...