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# Applied Microeconometrics - Science topic

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Questions related to Applied Microeconometrics

I have run pooled mean group estimation (PMG) on a panel data using xtpmg command in Stata. However, i could not find method to test for autocorrelation and heteroskedasticity from my pmg results. In case, if there is autocorrelation and heteroskedasticity in pmg estimation, how do we remove them? I shall be grateful, if somebody can help me.

I am trying to run a regression of cobb douglas function:

The problem that my dataset capture the firm at a point of time,

So I have a dataset over the period 1988-2012.

Each firm appears one time!

(I cannot define if it is a panel/time series/cross section..)

I want to find the effect of labor, capital on value added.

I have information on intermediate input.

I use two methods Olley& pakes, levinsohn-patrin.

But Stata is always telling me that there is no observations!

my command:

levpet lvalue, free(labour) proxy(intermediate_input) capital(capital) valueadded reps(250)

Why the command is not working and telling that there is no observations?

(Is this due the fact that each firm appear only one time in the data?)

(If yes, what is the possible corrections for simultanety and selection bias in this data?)

Thanks in advance for your help,

Mina

Dear All,

I have Panel Data fits difference in difference

I regress the (Bilateral Investment Treaties-BIT) on (Bilateral FDI).
BIT: is dummy taking 1 if BIT exists and Zero Otherwise. While
Bilateral FDI: Amount of FDI between the two economies.

**Objective: Examine**If BIT enhances Bilateral FDI?The issue is : - Each country have started its BIT with another pair country at a

**fixed time (different from the others): NO Fixed Time for the whole data.****I am willing to assume different time periods in a random way and run my Diff in Diff (for robustness):**

**Year 2004**

**Year 2006**

**Year 2008**

**My questions :**

**(1) Do you suggest this method is efficient?**

**(2) Any suggestion random selection of time?**

Hi,

I am trying to combine parent data information to their children file.

1. I have one dta file for Children information and another for their parents who are in the separate data file. The children data set contains unique identifier for their parents ID as well which I can use who is the childs mother or father. What I want to do is to match some details such as Father and Mothers employment details, education level to their children database. Is there any efficient way to do this.

2. If I have combined the two Children and Adult dataset into one dta file, can I then do what I want to do above or I will have to do it separately as I mention in (1).

3. What if I have organised the children data into a panel dataset and now want to add this information about parents. Any efficient way to do from here then?

Looking for ideas.

Thank You.

I have 5 different questions in survey data which is used to measure degree of individuals risk-taking behaviour. I need to use all these information to find my Y-variable (capturing risk behaviour of individuals).

I am attaching the questions here along with dataset for some valuable input.

Hi everyone,

Please this question is directed to Stata users and those familiar with the pooled mean group estimator (PMG) that is within the ARDL framework.

I use Stata 13 and I received an error message while attempting to obtain optimal lag orders for my PMG model. I have 10 countries and 3 variables from 1980 to 2015. I used this command:

forval i = 1/10 {

ardl dcb dr gdp if (c_id==`i'), aic ec

matrix list e(lags)

di

}

This tells Stata to automatically generate AIC-optimal lag orders which it was able to do BUT only for the first 9 countries after which I got this error message:

**"Maximum number of iterations exceeded. r(498)"**

I also tried increasing the number of iterations by using this code:

forval i = 1/10 {

ardl dcb dr gdp if (c_id==`i'), aic ec maxcombs (2500)

matrix list e(lags)

di

}

I received the same output as before and same error message, and I still do not don’t have the optimal lag order for the 10th country.

Please what can I do, or can anyone advise on another way of obtaining optimal lag orders for each variable and per country?

Seeking to learn about matrix stability

Dear scholars,

Please can any one assist me with the steps/codes to follow in estimating the Carrion-i-Silvestre et al (2005) test using stata or Eviews.

Thank you

i want to shock wage rate change on output and productivity of and textile sectors. Could you advise about use of type of elasticity and code for swapping this wage rate to be endogenous variable ?

Dear all,

I'm using Latent Gold Choice to estimante a LC model. The model performs well when up to three classes are considered. However, when I try with four classes I got a message of no convergence ("estimation procedure did not converge, 25 gradients larger than 1.0E-3) and maximum number of iterations reached without convergence. Anybody knows the possible reasons? Mis-specification of the model? A large number of observations (in my case 250000 obs)? Something else?

Thank you

Hi! How do I interpret beta and aplha in VECM? I know alpha is the adjustment to equilibrium, and that a significant and negative coefficient of alpha shows that the variable is "caused" by the other variable. But how do I interpret when beta (cointegrating equation) is significant with one variable or another? Or both? I have 2 vectors.

I have a problem regarding price variable. The choice set has 4 alternatives, one of the attribute that characterizes each alternative is price: for example alternative from 1 to 4 has price (10,20,30,40). For some people (student for example) prices are discounted of 50%, so the price of the alternatives are (5,10,15,20). In such a case how it is possible to treat the price variable? Interact the price with a discount variable?Put as explanatory variable all the different price categorie (e.g full price, price children, price senior…)?Consider only the full price and set a dummy variable for the category?

Thank you

In conditional logit model attributes of the alternatives and characteristic of the choosers are included as explanatory variables. However, let us suppose that the choice can be made in different context or choice situation that can affect the choice?can these attributes be included as independent variable? (so adding variables that are not related neither to the agent's characteristics nor to the attributes of the alternative)?

Thanks

I estimated the translog cost function (KLEM model) by dropping one equation. now i am to test coefficients. but, as one equation is dropped, in this situation how to perform the test? For example, coefficient qi, (i=K,L,E,M) is zero.

and i am to test whether model A is nested in B or not. i use Likelihood values of model and B. is there any difference in likelihood values when one equation is dropped? If yes, how can i do it?

I am trying to understand how the authors conduct the Worldwide Governance Indicator (WGI), which is the most cited governance index nowadays at here . Though they try to publish as much details as possible to public access, I am not very convinced with the technical appendix (see page 97 - 99).

In short, they use Unobserved component model to estimate the non-observable characteristics of governance that the index has not covered. To derive the parameters of the model, they say "applying standard maximum likelihood estimation" (page 100).

I do not know how to estimate such estimation (at page 100) given the data that they have. Also, they do not provide any documents/methods that have been used.

If anyone has come across this problem, please can you explain the methodology behind that MLE? Any relevant programme/coding would be much greatly appreciated.

How a product safety and quality can be estimated by applying econometrics technique?

I am interested to estimate technical efficiency based on Battese and Coelli (1992) model. I have tried many times for the estimation both using the command prompt as well the use of instruction file in Frontier 4.1. But the dialog box immediately disappear once I end up the process by giving the command through running the FR0NT41 .EXE. Could anyone guide me where is the problem and how could i estimate it.

in GMM application (xtabond2) we need to classify our variables as endogenous, predetermined and exogenous variables. what is the criteria for doing so? how would we get to know which variable falls into endogenous category?

I am currently working on a panel of count data with severe underdispersion (a lot of ones). Poisson is not efficient and generalized Poisson seems a better option. Gpoisson in Stata (Harrris, Yang, and Hardin, 2012) provides efficient estimates for cross section/pooled data. Does anyone know of any such routine for panel data with endogenous regressors?

Panel data in nominal form - Unit root test is required?

I'm analyzing panel data and would like to include and determine the firm specific and industry specific effect.

my research aim is to identify the affect of fresh graduates on labor demand and supply, whether this is supply is surplus or deficit according to market needs.

Simultaneous (or multiprocess) event history models have been developed over the most recent years, as a very particular and advanced type of duration models. Does anyone know if:

i) there is already some type of multiprocess event history models allowing for competing risks in BOTH processes (e.g., any way of modelling two main transition processes, where each transition may assume two or more different modes or routes)?

ii) there is any package that allows the estimation of these models in STATA?

The idea would be to estimate both processes jointly. One of the processes has been already studied through a discrete-time competing-risks model, but it would be nice if some methodology would allow the joint estimation of this competing-risk model (where transitions may occur through 2 routes) with another one, for another choice problem (precisely, a multinomial choice problem where agents decide among 4 alternative occupations), in order to allow for potential interdependencies (through unobservables) between the two processes.

I have tested the unit root for my panel data, some of the variables are stationary in first difference, and some of them in level, should I take any log for my variables? But the dependent variable is stationary at first difference.

Keynes postulated that the marginal propensity to consume (MPC), the rate of change of consumption for a unit (say, a dollar) change in income, is greater than zero, but less than 1. I would like to know if this always holds. If not, under which conditions may this theory fail to hold?

Heckman procedures have been widely used in empirical research to correct for selection bias. However, for duration models (survival analysis/time-to-event data), selection correction is still under development. There is an important contribution by Boehmke et al (2006) in American Journal of Political Science, which resulted in the program "DURSEL" for STATA.

Does anyone know any subsequent advance to correct for selection bias in duration models, especially for STATA?

Thanks in advance!

SUR (Seemingly Unrelated Regressions) models are well-suited for cross-section, whenever we have two or more equations (for the same cross-section units) whose errors are believed to be correlated. Extensions of SUR models to panel data, however, seem to be conceptually different – each “separate” equation corresponds to each time period of the panel, rather than to a different dependent variable. (The same is true even using the user-written command XTSUR (for STATA)).

Is there any possibility of extending SUR model to a dataset in panel format, in order to estimate two equations and allow for correlation among their errors, and still control for unobserved heterogeneity of the panel units?

I am using a panel dataset for several institutions and I would like to estimate two regressions for two different dependent variables (two different sources of revenues). It is believed that the errors of these two regressions may be correlated. How could we estimate such a model using something like SUR does for cross-section?