# Tag Info

6

Using corruption is part of it but a bit restrictive way to measure government "quality". You may use aggregate indicators as the one developed by the Worldwide Governance Indicators (WGI) project from the World Bank. They reports aggregate and individual governance indicators for over 200 countries and territories over the period 1996–, for six dimensions ...

5

Use -areg- in Stata, and the standard errors will come out as in the textbook. Specifically, the command areg lpassen lfare ldist ldistsq y98 y99 y00, absorb(id) vce(robust) will produce the desired result. -xtreg- with fixed effects and the -vce(robust)- option will automatically give standard errors clustered at the id level, whereas -areg- with -vce(...

4

I'm still not sure if I'm doing something wrong. However, it is useful to note that I get the same results in R. library(foreign) library(plm) library(lmtest) df <- read.dta("airfare.dta") fe.out <- plm(lpassen ~ lfare + ldist + ldistsq + y98 + y99 + y00, data=df, index = c("id", "year"), method = "within", effect = "individual") ...

3

FE logit requires the idiosyncratic errors to be IID across $i$ and $t$, quite a strong assumption. Also the regressors should be strictly exogenous, but it's the same for linear FE models. In your application, the fact that FE logit wouldn't converge will make a good argument against FE logit, and will satisfy some referees but not all. An important ...

3

The technique described in the question is almost correct. Consider a panel data set consisting of three cross-sections ($a$, $b$, and $c$) and three time-periods ($1$, $2$, and $3$). Let y denote the column vector with the observations of the dependent variable, x the column vector with observations of the first explanatory variable, and z the column vector ...

3

Having read up on your question it seems the fixed effect is fixed. If this is indeed the case it will have zero variance and hence zero covariance with any variable.

3

To understand the issue let's review what is the so call robust variance-covariance matrix estimates (VCE) and the implied "robust" standard errors. The robustness is meant to allow for violations of homoscedasticity in the cross-sectional dimension or heteroscedasticity. There are various heteroscedastic robust VCE which are known as the Sandwich estimators ...

2

By stnadard OLS regression results, in the simple regression $$y_t = \alpha + \beta z_t + v_t, \;\;\;t=1,...,T$$ we have that $$\hat \beta = \frac {\sum_{i=1}^T (z_t - \bar z)(y_t-\bar y)}{\sum_{i=1}^T (z_t - \bar z)^2}$$ and $$\hat \alpha = \bar y - \hat \beta \bar z$$ So the residuals are $$\hat v_t = y_t -(\hat \alpha +\hat \beta z_t)$$ Then, ...

2

You have done two different things. Your fixed-effects model captures the within-group over-time functional relationship between $debt_{it}$ and $y_{it}$ (that is, how much average difference in $y_{it}$ is there between two periods with a 1-unit difference in $debt_{it}$ within a country). In your data, there is limited within-group variability in $debt_{... 2 I cannot precisely answer your questions because I do not know which exactly regressions you want to perform as @jmbejara says and which papers are you referring to that use Fama-MacBeth regression. Are they on financial or other literatures? I haven't seen Fama-MacBeth on other literatures (I do not follow any other literature to be exact), so please post ... 2 Just to make sure I understand: You have a daily panel (with missing values), probably weekday only, running from 2013 to mid-2017 with$n=3$cross-sectional units. You believe that after 2015, there is a stronger incentive to do something different at quarter end, so the average basis should be different on month end days during that period. The typical ... 1 As a first pass, I would interpret as with any other dummy variable's coefficient. Assuming a linear model, when two countries share a border, their export similarity index rises, on average, by 0.15 when all other variables are held fixed. If you're writing a report on this model, you'll obviously need to discuss at length what this actually means. The ... 1 It all depends on how you define the 'wage premium between different areas'. The average NY resident makes$51,000 more per year than the average IL resident. There are 110 medical workers and 90 natural gas workers in NY and IL combined, so you could try weighting the wage premium of medical workers by 110/200 and the wage premium of natural gas workers ...

1

Yes, you can consider this as panel data, but the key is to understanding why, as this affects how you explain and interpret your panel regression. In treating each cohort as the same unit over time, you are assuming that each cohort has a constant, unobserved fixed effect. That is, people in a particular age category and gender tend to have an unobserved ...

1

For unemployment rates, check the ILO. For inflation rates, see the World Bank

1

Note that the fixed effects estimates use only within-firms differences, essentially discarding any information about differences between firms. If predictor variables vary greatly across firms but have little variation over time for each firm, then fixed effects estimates will be imprecise and have large standard errors. So, if there is not enough variation ...

1

No, I don't see how the dummy variable you are proposing would give you the same (or even similar) analysis as in the original model. I would recommend dealing with the endogeneity in another way. IV estimation, or one of the more recent methods, difference-in-difference, matching etc. One of the will be suitable.

1

CPI is a stock while GDP is a flow. Re-sampling of stocks to higher frequencies can be approximated with a number of choices, but probably the most common is linear interpolation. In some contexts filling forward and filling backward are common. But in principle you could fit any function you want through all the points you have and use the values from the ...

1

The data sample is so small that formal testing for stationarity would be essentially worthless. Inspect visually your individual series for any obvious trend. This would be the case where even with a short sample non-stationarity would be a problem.

1

This is a large comment. Fama-MacBeth only corrects for cross-sectional correlation in a panel and suffers from the error-in-variables problem (your $Z_t$, also check Chordia, Goyal and Shanken (2015)). Depending on the exact nature of your dataset's variables the answer to your questions is "it depends"; please check Goyal (2012), Mitchell (2009) and ...

1

Interpretation: Model 1: Within the same unit, $y_{it}-y_{is}$ is expected to be $(x_{it}-x_{is})\beta_1$. That is, within a unit, between two periods with different $x$, $y$ is expected to differ by the difference in $x$ times $\beta_1$. Model 2: In the same period, $y_{it}-y_{jt}$ is expected to be $(x_{it}-x_{jt})\beta_2$. That is, in a period, between ...

1

what exactly is the variation used in model 3 with both unit and time dummies? The variation that is being used to identify $\beta_3$ is basically the individual level deviations away from both the individual mean and average across individuals for the year. So to the degree that your variable of interest is varying over time but does not vary in a ...

1

Assuming that you mean "fixed effects" of econometricians, not of statisticians, you can check it as follows. You have $v_{it} = u_i + e_{it}$ consistently estimated (as $\beta$ is consistently estimated), where $u_i$ are the fixed effects and $e_{it}$ are the idiosyncratic errors. Under the assumption that $x_{it}$ is strictly exogenous to $e_{it}$ (which ...

1

This is an interesting result, not a bad result. If there are no regressors other than time dummies, then I think OLS = RE = FE. (I've done a few experiments with reg y i.year, xtreg y i.year, fe, and xtreg y i.year, re, but I have not proved.) If $X_{it}$ have trends and are correlated with fixed effects, anything can happen. For example, run the following ...

1

Prior to any trasnformation: Energy consumption is a per capita variable, specifically "Kgr per capita" Real GDP is a per capita variable Trade Openness is a per capita variable Energy price is a unit price of "crude oil" I see two problems: Is "Energy Price" the "price per Kgr" -because usually oil prices are not quoted per Kilogram. But it should ...

1

Here is a detailed worked example of how to use the xsmle command, including an example of the Spatial Durbin model. As the document says, it is estimated using Maximum Likelihood. The model can be of Random Effects or Fixed Effects. Key screenshots below: You need to use model(sdm) in the options to get the spatial Durbin model. To get the dynamic ...

1

I'm assuming you know how to do spatial weights matrices and such, so I will just skip that and throw out a disclaimer that I use R as my primary statistical programming language, not STATA, so let's first observe a generic spatial model: spatreg $ylist$xlist, weights(W) eigenvalues(E) model(lag) The lag in that function can be replaced with an error ...

1

You can regress residual squares (from RE or FE depending on your estimation) on $X_{it} \hat\beta$ and its square using the clustered standard errors (the vce(cl id) option), and read the F statistic and the associated p value. This is basically the same as Het test for cross sectional models (White's simplified test). xtreg y x1 x2, re predict uhat, ue ...

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