I am looking to build an econometric model and I am wondering if using annual data vs monthly or quarterly data is going to produce a less accurate model. If the dependent variable is affected by seasonality, would it be innappropriate to use monthly or quarterly input data? Or is it still generally better to use a higher frequency of input data regardless? I would prefer to use monthly or quarterly inputs but I am worried this would skew my model. Any suggestions or insight would be greatly appreciated.
Solved – Econometric Model and deciding the frequency of data collection
data miningeconometricsfrequencypanel data
Related Solutions
Use a logistic model predicting the probability of a recession using independent variables such as the slope of the yield curve, trailing stock market returns, short-interest rate, and credit spreads.
Having an unbalanced panel is not a problem nowadays. In the past, when econometrics had to be done by hand, inverting matrices for unbalanced panels was more difficult but for computers this is not a problem. The only worry connected today with this is the question why the panel is unbalanced: is it due to attrition? If yes, is this attrition random or related to characteristics of the statistical units? For instance, in surveys people with higher education tend to be more responsive and stay in the panel longer for that reason.
Regarding the fixed effects model, have you checked whether the variables that are time-invariant in theory are actual not varying over time? Sometimes coding errors sneak in and then all the sudden a variable varies over time when it shouldn't. One way of checking this is to use the xtsum command which displays overall, between, and within summary statistics. The time-invariant variables should have a zero within standard deviation. If they don't then something went wrong in the coding.
Having a negative Hausman test statistics is a bad thing because the matrices that the test is built on are positive semi-definite and therefore the theoretical values of the test are positive. Negative values point towards model misspecification or a too small sample (related to this is this question).
If you cluster your standard errors you also need a modified version of the Hausman test. This is implemented in the xtoverid command. You can use it like this:
xtreg ln_r_prisperkg_Frst_102202 Dflere_mottak_tur i.landingsfylkekode i.kvartiler_ny markedsk_torsk gjenv_TAC_NØtorsk_år_prct lalder_fartøy i.fangstr r_minst_Frst_torsk gjenv_kvote_NØtorsk_fartøy_prct i.lengde_gruppering mobilitet, fe vce(cluster fartyid)
xtoverid
Rejecting the null rejects the validity of the assumptions underlying the random effects mode.
The xtset command only takes into account the unit id for fixed effects estimation. The time variable does not eliminate time fixed effects. So if you do
xtset id time
xtreg y x, fe
will give you the exact same results as
xtset id
xtreg y x, fe
The time variable is only specified for commands for which the sorting order of the data matters, for instance xtserial which tests for panel autocorrelation requires this. This has been discussed here. So if you want to include time fixed effects, you need to include the day dummies separately via i.day, for example. In this context, the season and year dummies make sense so it's good that you use them.
Best Answer
More data (collected at higher frequencies) is not really more data as higher levels of auto-correlation exist. What I have recommended is a two-fold approach 1) At what frequency would you like to detect recent unusual activity or in other words what frequency do you wish to make forecasts i.e. end of day , end of week , end of month .,etc. Once you have determined that then consider higher frequencies from that point. So if you had said quarterly as the answer to the first question , you now have to assess/specify what forecast length you are concerned about. For example you might select/specify 1 quarter. Now you can use monthly data to predict the next quarter OR daily data to predict the next quarter or hourly data to predict the next quarter. These three different approaches can all be used and you can compute the accuracy of each and then select the winner from this 1 origin. Make sure that you repeat this experiment for a few origins not just 1.