Python Xtreg Python Xtreg. 36 Hausman test. 6566 Obs per group: min = 7 between = 0. Difference- in-Differences We will illustrate how to run a difference-in-differences regression to explain the effect of a treatment intervention on progression to secondary school. 4s Without clusters, the only difference is that -areg- takes 0. , xtreg_fe takes 2. Reading and Using STATA Output. For example, on any given day a particular guinea pig may yield different weight measurements due to differences in scale (equipment) and/or small fluctuations in weight during a day (slope on time) A) Linear model with random intercept Simulated Data: Non-Clustered Simulated Data: Clustered Models A and B are equivalent Pigs – Independent. The difference increases with more. As a result, OLS is biased. Generalized Difference-in-differences ! Advantage of generalized differences-in-differences is that it can improve precision and provide better fit of model " It doesn’t assume all firms in treatment (or untreated) group have same average y; it allows intercept to vary for each firm " It doesn’t assume that common change in y. Think of it as ols. The xt series of commands provide tools for analyzing cross-sectional time- series (panel) datasets: help xtdes Describe pattern of xt data help xtsum Summarize xt data help xttab Tabulate xt data help xtreg Fixed-, between- and random-effects, and population- averaged linear models help xtdata Faster specification searches with xt data help. In this FAQ we will try to explain the differences between xtreg, re and xtreg, fe with an example that is taken from analysis of variance. Empirically understanding payout policy, capital structure, or investment decisions arguably requires the use of firm fixed effects to control for unobserved, time-invariant differences across firms. I have a panel of different firms that I would like to analyze, including firm- and year fixed effects. The only apparent difference I found is the year effect, which is caused by contrast (xtreg sets the first year as reference, while plm directly estimates the effect for each year). xtreg with its various options performs regression analysis on panel datasets. pdf), Text File (. Diff-in-Diff with aggregated data. Stata Xtreg. Background Targeted temperature management is recommended after out-of-hospital cardiac arrest and may be achieved using a variety of cooling devices. 85 corr(u_i, Xb) -0. Difference-in-differences Facilitated by Nicole M. dta * Due to Baltagi et al. Then, you run xtreg Temperature treatment treated time. The only apparent difference I found is the year effect, which is caused by contrast (xtreg sets the first year as reference, while plm directly estimates the effect for each year). How can method 3 be wrong? Because it fails to account for the fact that the means we removed are *ESTIMATES*. differences in the coefficients for the fixed and random effects models, which might reflect the importance of omitted variable bias in the latter. This study was conducted to explore the performance and outcomes for intravascular versus surface devices for targeted temperature management after out-of-hospital cardiac arrest. Fixedeﬀect-xtreg-YoushouldalwaysestimateFE-modelsusing-xtreg-(exceptspeciﬁcation searching). Hofstede’s work provided researchers with a consistent quantification of cultural differences between countries, causing a surge in empirical studies about the impact of culture on the activities and performance of multinational firms (Kirkman et al. The difference is that pooling cross sections means different elements are sampled in each period, whereas panel data follows the same elements through time. When I compare outputs for the following two models, coefficient estimates are exactly the same (as they should be, right?). xtreg is used for panel data; fe indicates other variables have fixed effect﻿ OR: select Statistics -> Longitudinal/panel data -> Linear models -> Linear regression (FE,RE,PA,BE) Output: The output shows you that it is a fixed-effects regression, with a group variable idcode. Patient(s): African American and Caucasian women identified by random. txt, text replace set seed 123456 set obs 500 * generate. mengolah data yang baik dengan stata. Give or take a few decimal places, a mixed-effects model (aka multilevel. 36 Hausman test. _regress y1 y2, absorb(id) takes less than half a second per million observations. xtreg is used for panel data; fe indicates other variables have fixed effect﻿ OR: select Statistics -> Longitudinal/panel data -> Linear models -> Linear regression (FE,RE,PA,BE) Output: The output shows you that it is a fixed-effects regression, with a group variable idcode. 0276 avg = 7. Then, you run xtreg Temperature treatment treated time. 0000 We have mechanically succeeded in greatly reducing the ˜2, but not by enough. Same as Stata website file psidextract. xtreg, be provides what is known as the between estimatorand amounts to using OLS to perform the estimation of (2). Here we reject the null and conclude that random effects is the appropriate model because there is evidence of significant differences across women. , the difference. Keywords: Difference in differences, causal inference, kernel propensity score, quantile treatment effects, quasi-experiments. * Monte Carlo Simulation for Panel Data Model clear set more off capture log close log using I:\411\411log. 05 to show a statistically significant relationship between X and Y. 5s, and the new version of reghdfe takes 0. New features for stpm2 include improvement in the way time-dependent covariates are modeled, with these effects far less likely to be over parameterized; the ability to incorporate expected mortality and thus fit relative survival models; and a superior predict command that enables simple quantification of differences between any two covariate. One answer is that it is a necessary ingredient in calculating random-effects results: the random-effects results are a weighted average of the xtreg, be and the xtreg, fe results. dta}, which is distributed with Stata. ˘ ˇ ˆ˙˝˛˝ ˛ ˘ ˇ ˘ ˘ ˛ ˘ • ˚ ˛˜ ˇ ˘ ˆ ˘ˆ˙ ˆˇ ˆ ˝ ˘˛ ˘. Difference-in-differences estimation in Stata Nicholas Poggioli research methods , stata May 23, 2017 May 23, 2017 1 Minute I created a short. In an economic situation, y might be purchases of some item and x income; a change in average income should have more effect than a transitory change”3. In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the ``parallel trends assumption" holds potentially only after conditioning on observed covariates. type: xtset country year. 4507 Obs per group: min 4 between 0. 025; Instead of working at α = 0. We begin with a fairly typical OLS regression analysis regressing api04 on meals, el, avg_ed and emer. In Stata, xtoverid is used on a test of overidentifying restrictions (orthogonality conditions) for a panel data estimation after xtreg, xtivreg, xtivreg2, or xthtaylor. , 10-year differences) in order to control for time-constant unobserved heterogeneity. With T > 2, we could do T – 1 differences across pairs of time periods, allowing n(T – 1) observations in the differenced sample (and n ( T – 1) – k degrees of freedom because there is no constant term). The Hausman test looks to see whether the estimates from the fixed and random effects models are significantly different from each other. Hofstede’s work provided researchers with a consistent quantification of cultural differences between countries, causing a surge in empirical studies about the impact of culture on the activities and performance of multinational firms (Kirkman et al. dat is the version of the data posted at the JBES website * Note that in chapter 22 we instead use MOMprecise. Test: Ho: difference in coefficients not systematic. Keyword-suggest-tool. For TA individual items Mann–Whitney rank-sum test was employed, while a difference of proportion test was used for TR items to examine the differences. * Instead should get cluster-robust errors after xtreg * See Section 21. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. 85 corr(u_i, Xb) -0. It automatically conducts an F-test, testing the null hypothesis that nothing is going on here (in other words, that all of the coefficients on your independent variables are equal to. Water supplied to households by competing private companies Sometimes different companies supplied households in same street In south London two main companies: Slideshow 791190 by. 5s, and the new version of reghdfe takes 0. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:Ho:difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13. In this FAQ we will try to explain the differences between xtreg, re and xtreg, fe with an example that is taken from analysis of variance. All the data are real, except for the treatment variable. Fixed-Effects Model & Difference-in-Difference xtreg health retired female i. The reason is they use slightly different degrees of freedom adjustments, because they are making different assumptions about what indices are going to infinity. The command in Stata is xttest0. 978064 (with the accurate p =. When thinking about situations in which a difference-in-differences design can be used, one usually tries to find an instance where a consequential treatment was given to some people or units but denied to others “haphazardly. We have the standard regression model (here with only one x):. The Grand Experiment. ** STATA Sample Program by Colin Cameron ** Program stpanel. The difference between subject-specific coefficients and population-averaged coefficients, and why it matters. is different from 0. 52 Prob>chi2 = 0. Specialized on Data processing, Data management Implementation plan, Data Collection tools - electronic and paper base, Data cleaning specifications, Data extraction, Data transformation, Data load, Analytical Datasets, and Data analysis. smcl", replace use "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2 - schools. 99 chi2 model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test. The difference is real in that we are making different assumptions with the two approaches. 0 overall 0. For the fixed-effects model,. In such settings, default standard errors can greatly overstate estimator precision. 请教是不是第一个检验结果是用固定效应模型，第二个用随机效应？. * - panel robust standard errors. The difference between subject-specific coefficients and population-averaged coefficients, and why it matters. New features for stpm2 include improvement in the way time-dependent covariates are modeled, with these effects far less likely to be over parameterized; the ability to incorporate expected mortality and thus fit relative survival models; and a superior predict command that enables simple quantification of differences between any two covariate. Xtreg Difference In Difference. There are multiple ways of implementing a fixed effects regression in Stata -- make your own dummy variables, use the prefix xi, use the commands areg or xtreg, or employ techniques such as demeaning or first differences. The point above explains why you get different standard errors. xtreg, re provides the random-effectsestimator and is a. sum LNEXP12M AGE SEX HHSIZE FARM EDUC HHEXP LNHHEXP COMMUNE Variable | Obs Mean Std. What I mean by that is: For example, one could hypothetically imagine a firm in the US applying different accounting standards (e. This method produces the same results but rather than creating dummy variables for each entity and time, it relaxes the assumption of one intercept term and allows each entity. School-specific difference between female and male average scores We do see quite a bit of heterogeneity in the gender differences across the different school. Would these be "correct" procedures in the DiD setting? If yes, how would you interpret the results of these other procedures wrt the former? 2. but in the last situation (4th, i. * random effect estimation. In this FAQ we will try to explain the differences between xtreg, re and xtreg, fe with an example that is taken from analysis of variance. The log-transformed values were converted back to normal values for presenta-. I am using SAS PROC TSCSREG & STATA XTREG to run a fixed-effect model on the same panel data. The calculation of the R2 is different. The key difference in running regressions with panel data (with both cross-sectional and time-series variations) from a usual OLS regression (with only cross-sectional variation) is that one needs to control for the common effect for all individuals in a particular time point, and also the idiosyncratic individual effect that is common across. We realized after the paper's publication that the Stata procedure used to calculate standard errors with the xtreg command - used throughout the paper - was changed between Stata 9 and Stata 10. Each is fitted via xtreg , fe xtreg , be xtreg , re. This handout is designed to explain the STATA readout you get when doing regression. What I mean by that is: For example, one could hypothetically imagine a firm in the US applying different accounting standards (e. Adkins and R. ***** PANEL DATA SUMMARY * Read in data set use mus08psidextract. > At first, I estimate the following model: > y b0+b1Time+b2Treatment+b3Time*Treatment+u > > using the -reg command: > > -reg y time treatment time*treatment, cluster (h1) > > while y is the outcome variable that is between 0 and 1 and h1 is. 0276 avg = 7. Keywords: Difference in differences, causal inference, kernel propensity score, quantile treatment effects, quasi-experiments. smcl", replace use "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2 - schools. xtreg with its various options performs regression analysis on panel datasets. option instead of. This is modeling the between variation - Averaging across individuals (collapsing over time), is there a difference per values of subject-varying variables? re or “random effects”. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. The uptake is > non-random. 025; Instead of working at α = 0. If within-country variation is very small, you can experiment with RE or taking long differences (e. some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. xtreg, be provides what is known as the between estimatorand amounts to using OLS to perform the estimation of (2). You can use it to run fixed effects We will illustrate how to run a difference-in-differences regression to explain the effect of a treatment intervention on progression to secondary school. B = inconsistent under Ha, efficient under Ho; obtained from xtreg. * (which for RE simplify by assuming lamda_hat is known not estimated). Patient(s): African American and Caucasian women identified by random. In the xtreg, fe approach, the effects of the groups are fixed and unestimated quantities are subtracted out of the model before the fit is performed. inside sport events and I also 'd like to know if this effect would be moderated. These options are all equivalent in terms of the coefficient estimates. Before using xtregyou need to set Stata to handle panel data by using the command xtset. 0 for both treatment and control grouop in the baseline period and 1 for the treatment group in the followup while 0 for the control group in the followup. The effect of the terrorist attack on ED inflow is given by the Treatment group x After coefficients (−0. * setup version 11. option instead of. txt) or read online for free. , areg takes 2 seconds. Stata Xtreg. For example, instead of working at α = 0. xtset fcode year. You obtain that the difference-in-difference estimator is -1. 025; Instead of working at α = 0. Python Xtreg Python Xtreg. With T > 2, we could do T – 1 differences across pairs of time periods, allowing n(T – 1) observations in the differenced sample (and n ( T – 1) – k degrees of freedom because there is no constant term). diff difference-in-difference built-in Stata command r eg s io nd c tu y xtabond xtdpdsys dynamic panel estimator teffects psmatch p ro e ns ity cma h g synth e ic or la oaxaca user-written ssc install ivreg2 for Stata 13: ci mpg price, level (99). Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. The major differences. Introduction. Hofstede (1980) was the first researcher to reduce cross-national cultural diversity to country scores on a limited number of dimensions. com Difference in differences (DID) Estimation step‐by‐step * Estimating the DID estimator reg y time treated did, r * The coefficient for ‘did’ is the differences-in-differences estimator. do) *trajectory. xtregar An Introduction to “Difference” and “System” GMM in Stata. In the areg procedure, you are estimating coefficients for each of your covariates plus each dummy variable for your groups. Hallo Tom! Vielen Dank fuer den Text, es hat mich sehr geholfen. xtreg wage experience education , fe. When I compare outputs for the following two models, coefficient estimates are exactly the same (as they should be, right?). The estimation method was maximum likelihood using Stata’s XTNBREG (negative binomial) and XTREG (linear) commands. , xtreg_fe takes 2. > Now, I want to estimate the impact in a difference in difference design. We begin with a fairly typical OLS regression analysis regressing api04 on meals, el, avg_ed and emer. The difference is real in that we are making different assumptions with the two approaches. * - panel bootstrap standard errors. do file for Stata 6. xtreg command fits various panel data models, including fixed- and random-effects models. txt drop in 1 d su * setup panel data xtset co year, yearly * panel data analysis xtdes xtsum * between (or group means) estimator * xtreg i f c, be by co. The activity-based fund allocation for radiology providers was reduced from approximately 50% to 40%, which was compensated by an increased basic grant. ------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section5\mma21p1panfeandre. We want to caution against using these values as measures of model fit (see discussion below). Difference- in-Differences We will illustrate how to run a difference-in-differences regression to explain the effect of a treatment intervention on progression to secondary school. AMMBR from xtreg to xtmixed(+checking for normality, and random slopes, it might converge to different estimates for different algorithms in the iterative process. The unit of “benefit” is 1,000 points (10,000 JPY or around 100 USD. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:Ho:difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13. The example (below) has 32 observations taken on eight subjects, that is, each subject is observed four times. We have the standard regression model (here with only one x):. Stata can store estimates from multiple models, save all estimates in a single table, and export the table to an external file, such as rtf, csv, html, tex, and others. BASICS ** Open "neighborhood" data *1 xtsum schid, i(schid) *2 ** Sort by level-2 unit identifier sort schid *3 browse schid attain *4 tab schid *5 *Download. states over 30 years 1963-92 * mus08cigarwide. differences between races in missing hormone data, and these missing data are assumed to be random. These differences were correlated with the corresponding patient’s clinical-judgement scores (deteriorated, stable or improved) through random-effects linear regression analyses using the model “xtreg” in STATA, an approach comparable with ordinary regression analyses, but taking into account the associations caused by the longitudinal. Marianne Bertrand’s 2004 article “How much should we trust differences-in-differences estimates?” (appeared in QJE) outlines several tests that can be done to assess the robustness of difference-in-differences estimates given concerns of false positives. German accounting standards), which leads to different values of reported earnings of the same firm in the same period. November 2018 at 1:48. xtreg ln_consumo1 ln_precio1 ln_pib_pc, fe Fixed-effects (within) regression Number of obs = 317. pdf), Text File (. 双重差分模型 （Difference-Differences, DID）是政策评估的非实验方法中最为常用的一种方法，其中 交互项是DID的灵魂 。 交互项形式拥有各种形式，包括（1）传统DID；（2）经典DID；（3）异时DID；（4）广义DID；以及（5）异质性DID。下面分别介绍这几种。 1. When thinking about situations in which a difference-in-differences design can be used, one usually tries to find an instance where a consequential treatment was given to some people or units but denied to others “haphazardly. I got same coefficient estimates but rather different R-squared. ***** PANEL DATA SUMMARY * Read in data set use mus08psidextract. One answer is that it is a necessary ingredient in calculating random-effects results: the random-effects results are a weighted average of the xtreg, be and the xtreg, fe results. When you use -xtreg, fe- Stata has one particular strategy for dealing with the colinearity. smcl", replace use "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2 - schools. The difference between running a one or two tailed F test is that the alpha level needs to be halved for two tailed F tests. 请教是不是第一个检验结果是用固定效应模型，第二个用随机效应？. The objective is to explore what problems can be solved with such “two dimensional” data that. so can you please guide me that what’s the reason for such strange behaviour in my. , the difference. * random effect estimation. 26 Prob>chi2 = 0. * - usual standard errors. The only apparent difference I found is the year effect, which is caused by contrast (xtreg sets the first year as reference, while plm directly estimates the effect for each year). After you let STATA know how the data is organized you can use the xtreg command. Dependent i. * (which should equal panel robust from. Marianne Bertrand's 2004 article "How much should we trust differences-in-differences estimates?" (appeared in QJE) outlines several tests that can be done to assess the robustness of difference-in-differences estimates given concerns of false positives. The unit of “benefit” is 1,000 points (10,000 JPY or around 100 USD. Lets run the regression: regress. The difference between subject-specific coefficients and population-averaged coefficients, and why it matters. It automatically conducts an F-test, testing the null hypothesis that nothing is going on here (in other words, that all of the coefficients on your independent variables are equal to. Year, fe J’obtiens des résultats différents, donc je dois faire quelque chose de mal avec l’un ou l’autre xtreg ou , ou les plm deux. Adkins and R. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. 0574 max 4 F(4,1148) 121. Background The variation in the impact of the 2008 reimbursement change for Norwegian radiology providers, depending on the travel times to private and public providers in different municipalities, was examined. dat * which is the same data set but with more significant digits ***** READ DATA ***** * The data are in ascii file MOM. /* ** Panel Data (Cornwell and Rupert, 1988) ** Greene , Chap. 9 ** Data is stacked in long form, 595 individuals 7 years ** lwage = exp exp2 wks edu. Obviously, one could have also construcet a treatment dummy that varies between the time periods, i. * random effect estimation. The difference between running a one or two tailed F test is that the alpha level needs to be halved for two tailed F tests. How can method 3 be wrong? Because it fails to account for the fact that the means we removed are *ESTIMATES*. 025; Instead of working at α = 0. some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. Additional features include:. 01, which is also well measured since the p-value is basically 0. xtreg emis_ratio_trans i. Type II ANOVA , also known as random-effect ANOVA , assumes that you have randomly selected groups from an infinite (or at least large) number of possible groups, and that you want to reach conclusions about differences among ALL the. Keywords: Difference in differences, causal inference, kernel propensity score, quantile treatment effects, quasi-experiments. do May 2001 (began October 1999) * To run you need file * patr7079. Background Targeted temperature management is recommended after out-of-hospital cardiac arrest and may be achieved using a variety of cooling devices. In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the ``parallel trends assumption" holds potentially only after conditioning on observed covariates. • Bivariate data can be stored in a table with two columns: X Y Obs. > At first, I estimate the following model: > y b0+b1Time+b2Treatment+b3Time*Treatment+u > > using the -reg command: > > -reg y time treatment time*treatment, cluster (h1) > > while y is the outcome variable that is between 0 and 1 and h1 is. Adkins and R. Second, we determined the duration of an acquisition/merger deal as the time between the public announcement and the deal completion. 978064 (with the accurate p =. 99 chi2 model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test. For example, instead of working at α = 0. The difference is that pooling cross sections means different elements are sampled in each period, whereas panel data follows the same elements through time. Test of the Difference Between Two Non-Zero Coefficients We first convert r to Fisher’s Z statistics: We then assume a normal distribution for Z 1-Z 2 and use the. Panel Data 4: Fixed Effects vs Random Effects Models Page 4 Mixed Effects Model. * 595 individuals for years 1976-82 * mus08cigar. It automatically conducts an F-test, testing the null hypothesis that nothing is going on here (in other words, that all of the coefficients on your independent variables are equal to. Comentario: xtreg, be rara vez se utiliza, pero entre las estimaciones son un ingrediente en la estimación de efectos aleatorios. some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. In this handout we will focus on the major differences between fixed effects and random effects models. dat is the version of the data posted at the JBES website * Note that in chapter 22 we instead use MOMprecise. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. Hallo Tom! Vielen Dank fuer den Text, es hat mich sehr geholfen. The command xtreg is equivalent to the reg command, but takes into account the panel nature of the data. The difference is real in that we are making different assumptions with the two approaches. Well, you are right. You obtain that the difference-in-difference estimator is -1. Water supplied to households by competing private companies Sometimes different companies supplied households in same street In south London two main companies: Slideshow 791190 by. chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 96. This dataset has complete data on 4,702 schools. StataCorp LLC, Texas, USA) and the command xtreg. Introduction. Marianne Bertrand’s 2004 article “How much should we trust differences-in-differences estimates?” (appeared in QJE) outlines several tests that can be done to assess the robustness of difference-in-differences estimates given concerns of false positives. The key difference in running regressions with panel data (with both cross-sectional and time-series variations) from a usual OLS regression (with only cross-sectional variation) is that one needs to control for the common effect for all individuals in a particular time point, and also the idiosyncratic individual effect that is common across. xtreg lpassen lfare y98 y99 y00, fe cluster(id) Fixed-effects (within) regression Number of obs 4596 Group variable: id Number of groups 1149 R-sq: within 0. Participants were two cohorts of in total 8806 Norwegian twins born 1948 to 1960 (older cohort, mean age at questionnaire = 40. xtreg iq mIQ if treatment == 1, fe *Adding fixed effects for individual participants by specifying ", fe" at the end of the xtreg command gets us the accurate p-value (0. Patient(s): African American and Caucasian women identified by random. Panel data allows you to control for variables you cannot observe or measure like cultural factors or difference in business practices across companies; or variables that change over time but not across entities (i. xtreg (with assumption checking). In general, differencing removes all time constant variables (such as gender). some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. delta: 1 unit time variable. Lets run the regression: regress. /*program that runs interrupted time series (segmented) linear regression - problem is that effects are fixed*/ program ITSfixed local txt1 = "P:\Evan\Quip. When you use -xtreg, fe- Stata has one particular strategy for dealing with the colinearity. national policies, federal regulations, international agreements, etc. The Hausman test looks to see whether the estimates from the fixed and random effects models are significantly different from each other. The difference is real in that we are making different assumptions with the two approaches. Free essays, homework help, flashcards, research papers, book reports, term papers, history, science, politics. See full list on econometricstutorial. ------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section5\mma21p1panfeandre. The difference between running a one or two tailed F test is that the alpha level needs to be halved for two tailed F tests. , 10-year differences) in order to control for time-constant unobserved heterogeneity. Table 3 Difference-in-Differences estimation results. > At first, I estimate the following model: > y b0+b1Time+b2Treatment+b3Time*Treatment+u > > using the -reg command: > > -reg y time treatment time*treatment, cluster (h1) > > while y is the outcome variable that is between 0 and 1 and h1 is. The command in Stata is xttest0. Below is a specifically empirical problem and a case where the commands do not seem to be generating what I want. Test: Ho: difference in coefficients not systematic. 99 chi2 model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test. The uptake is > non-random. Xtreg Difference In Difference. , xtreg_fe takes 2. AMMBR from xtreg to xtmixed (+checking for normality, and random slopes, and cross-classified models, and then we are done in terms of theory ). >> >> I always thought that this setting and a setting with fixed effects >> yield exactly the same result as long as one has only two points in time >> (in my case 2010 and 2012). This study was conducted to explore the performance and outcomes for intravascular versus surface devices for targeted temperature management after out-of-hospital cardiac arrest. Lets run the regression: regress. This is possible with the. ˘ ˇ ˆ˙˝˛˝ ˛ ˘ ˇ ˘ ˘ ˛ ˘ • ˚ ˛˜ ˇ ˘ ˆ ˘ˆ˙ ˆˇ ˆ ˝ ˘˛ ˘. 1 John Snow’s Cholera Hypothesis. Stata can manipulate data, calculate statistics, and run regressions. txt) or read online for free. ABSTRACTThe impact of privatization on public service quality is an enduring issue in public policy and management. 978064 (with the accurate p =. xtreg health retired female i. Marianne Bertrand’s 2004 article “How much should we trust differences-in-differences estimates?” (appeared in QJE) outlines several tests that can be done to assess the robustness of difference-in-differences estimates given concerns of false positives. The major differences. The Grand Experiment. In Stata, this can be set up as (repeated cross section) regress y time_dummy group_dummy interaction_time_group controls or (panel) xtreg y time_dummy group_dummy interaction_time_group. Keywords: Difference in differences, causal inference, kernel propensity score, quantile treatment effects, quasi-experiments. xtreg estimates within-group variation by computing the differences between observed values and their means. 5s, and the new version of reghdfe takes 0. in the cox-proportional model. com Difference in differences (DID) Estimation step‐by‐step * Estimating the DID estimator reg y time treated did, r * The coefficient for ‘did’ is the differences-in-differences estimator. xtreg DEATH POST, fe. For the fixed-effects model,. • Heteroskedasticity can also occur if there are subpopulation differences or other interaction effects (e. * file chap15. Whites are significantly more likely to eat vegetables while blacks are not significantly different from recent Mexican immigrants. Here is the info with respect to my data set N=60 and T=47, so I have a panel data set and this is also strongly balanced. in Table 24. * and calculates. What is the difference between xtreg, re and xtreg, fe Stats. Prob>chi2 = 0. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:Ho:difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13. diff difference-in-difference built-in Stata command r eg s io nd c tu y xtabond xtdpdsys dynamic panel estimator teffects psmatch p ro e ns ity cma h g synth e ic or la oaxaca user-written ssc install ivreg2 for Stata 13: ci mpg price, level (99). xtregar An Introduction to “Difference” and “System” GMM in Stata. For example: xtset id xtreg y1 y2, fe runs about 5 seconds per million observations whereas the undocumented command. txt) or read online for free. Represents the change in outcome due to natural trend and all other events, and the program c) The impact of the. chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 148. com and simply omit x1 from the test. • Bivariate data can be stored in a table with two columns: X Y Obs. do file comparing several ways of estimating a two-period difference-in-differences model in Stata. Posts about simulation written by Jens Förderer. The difference is real in that we are making different assumptions with the two approaches. See full list on stats. Finally, run the regression using the ﬁrst-differened data, called ﬁrst difference equation: ∆yi= d0+ b1∆xi+ ∆ei(8) Notice that bothaiand b0disappear. xtreg, be provides what is known as the between estimatorand amounts to using OLS to perform the estimation of (2). b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg. Prob > chi2 = 0. Below is a specifically empirical problem and a case where the commands do not seem to be generating what I want. ------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section5\mma21p1panfeandre. The activity-based fund allocation for radiology providers was reduced from approximately 50% to 40%, which was compensated by an increased basic grant. (2001) * Panel on 46 U. Give or take a few decimal places, a mixed-effects model (aka multilevel. (Again, the problem arises from violation of the assumption that no such differences exist or have already been incorporated into the model. We begin with a fairly typical OLS regression analysis regressing api04 on meals, el, avg_ed and emer. How to do a placebo simulation in difference-in-differences designs (part 1) January 27, 2019 January 27, 2019 Jens Förderer Leave a comment Marianne Bertrand’s 2004 article “ How much should we trust differences-in-differences estimates? ” (appeared in QJE) outlines several tests that can be done to assess the robustness of difference. xtreg Fixed-, between- and random-effects, and population-averaged linear models. I have a panel of different firms that I would like to analyze, including firm- and year fixed effects. The reason is they use slightly different degrees of freedom adjustments, because they are making different assumptions about what indices are going to infinity. Why first-order autoregressive structures are usually unsatisfactory. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. b consistent under Ho and Ha; obtained from xtreg, B inconsistent under Ha, efficient under Ho; obtained from xtreg. Water supplied to households by competing private companies Sometimes different companies supplied households in same street In south London two main companies: Slideshow 791190 by. Instead we estimate:. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg. School-specific difference between female and male average scores We do see quite a bit of heterogeneity in the gender differences across the different school. Statistical Methods The natural logarithms of plasma hormone values were used in all statistical analyses because the transformed val-ues were more normally distributed. Results The effect of the Stockholm terrorist attack on ED inflow Table 3 shows the estimation results for time windows of different lengths (days before and after the attack). We begin with a fairly typical OLS regression analysis regressing api04 on meals, el, avg_ed and emer. 0000 We have mechanically succeeded in greatly reducing the ˜2, but not by enough. Test: Ho: difference in coefficients not systematic. Carter Hill * used for "Using Stata for Principles of Econometrics, 4e" * by Lee C. The command in Stata is xttest0. xtreg and xtmixed: recap We have the standard regression model (here with only one x): but think that the data are clustered, and that the intercept (c0) might be different for different clusters … where the S-variables are dummies per cluster. 0) might be differentfor different clusters … where the S-variables are dummies per cluster. After you let STATA know how the data is organized you can use the xtreg command. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = -11. reg is the typical regression command in Stata that tells the program you are looking to linearly regress a dependent variable on other independent variable(s). The difference between running a one or two tailed F test is that the alpha level needs to be halved for two tailed F tests. The major differences. Edited to add: The difference between what -areg- and what -xtreg- are doing is that -areg- is counting all of the fixed effects against the regression's degrees of freedom, whereas -xtreg- is not. In the xtreg, fe approach, the effects of the groups are fixed and unestimated quantities are subtracted out of the model before the fit is performed. Differences-in-Differences and A Brief Introduction to Panel Data. Was there a problem with using reghdfe? Note that if you use reghdfe, you need to write cluster(ID) to get the same results as xtreg (besides any difference in the observation count due to singleton groups). These differences were correlated with the corresponding patient’s clinical-judgement scores (deteriorated, stable or improved) through random-effects linear regression analyses using the model “xtreg” in STATA, an approach comparable with ordinary regression analyses, but taking into account the associations caused by the longitudinal. ** STATA Sample Program by Colin Cameron ** Program stpanel. Test Ho difference in coefficients not systematic. mengolah data yang baik dengan stata. The example (below) has 32 observations taken on eight subjects, that is, each subject is observed four times. Notice that the -margins- results are different for the two regressions; yet these two models are not substantively different--they are just two different ways of breaking the colinearity between treat and idcode. In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DID) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the ``parallel trends assumption" holds potentially only after conditioning on observed covariates. ***** PANEL DATA SUMMARY * Read in data set use mus08psidextract. 52 Prob>chi2 = 0. Mason Michigan State University Department of Agricultural, Food, & Resource Economics 1 March 2018 Indaba Agricultural Policy Research Institute Lusaka, Zambia Recall from July/September trainings on introduction to impact evaluation (1) "An impact evaluation assesses changes in the well-. \$\begingroup\$ Hi @Davide, there is a difference between the "edit approval" process and the "reopening" process. Carter Hill * used for "Using Stata for Principles of Econometrics, 4e" * by Lee C. OLS applied to the FD regression (8) yields the so called ﬁrst-difference estimator. bys group: sum variable. xtreg data18 data128 data6 data12 if ok, re Random-effects GLS regression Number of obs = 3957 Group variable (i): firmid Number of groups = 1319. 025; Instead of working at α = 0. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test:Ho:difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13. An additional focus on corporate cultural differences may reveal the relative importance of both types of cultural difference in the intermediate merger/acquisition phase. (2001) * Panel on 46 U. While the original query wondered whether a decision between the "reg" and "xtreg" commands pivoted on whether panel data were balanced or unbalanced, a very helpful commenter quickly made clear the question's pivotal (and mistaken) assumption. The major differences. xtreg, be provides what is known as the between estimatorand amounts to using OLS to perform the estimation of (2). Fixed-Effects Model & Difference-in-Difference xtreg health retired , re // + time-constant explanatory variable. xtreg and xtmixed: recap We have the standard regression model (here with only one x): but think that the data are clustered, and that the intercept (c0) might be different for different clusters … where the S-variables are dummies per cluster. Would these be "correct" procedures in the DiD setting? If yes, how would you interpret the results of these other procedures wrt the former? 2. Hofstede’s work provided researchers with a consistent quantification of cultural differences between countries, causing a surge in empirical studies about the impact of culture on the activities and performance of multinational firms (Kirkman et al. Objective: Evaluate racial differences in reproducibility of hormone levels over time (estradiol, DHEAS, FSH, and testosterone) while adjusting for covariates previously identified as relevant in the study population. Confidence intervals are calculated by clustered robust standard errors clustered by municipality. Prob>chi2 = 0. These differences were correlated with the corresponding patient’s clinical-judgement scores (deteriorated, stable or improved) through random-effects linear regression analyses using the model “xtreg” in STATA, an approach comparable with ordinary regression analyses, but taking into account the associations caused by the longitudinal. >> >> I always thought that this setting and a setting with fixed effects >> yield exactly the same result as long as one has only two points in time >> (in my case 2010 and 2012). Xtreg Difference In Difference. We tested for heterogeneity in the relationship between fast food price and outcomes over study periods by including an interaction term for time. 4507 Obs per group: min 4 between 0. b = consistent under Ho and Ha; obtained from xtreg. For example, instead of working at α = 0. Frequently there are other more interesting tests though, and this is one I've come across often -- testing whether two coefficients are equal to one another. csat expense, robust. Fixed-Effects Model & Difference-in-Difference xtreg health retired female i. xtreg health retired female i. The calculation of the R2 is different. An additional focus on corporate cultural differences may reveal the relative importance of both types of cultural difference in the intermediate merger/acquisition phase. Then you compute the robust matrix for the two-way clustering and you name it V. smcl", replace use "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2 - schools. The real difference between -areg- and -xtreg, fe- in terms of the R2 is that -areg- includes the variance explained by the absorbed dummies in the R2 computation, whereas -xtreg, fe- does not. For example, instead of working at α = 0. Each is fitted via xtreg , fe xtreg , be xtreg , re. xtreg emis_ratio_trans i. The advantage of creating the. dat * There are 532 individuals with 10 lines. FE models attempt to do this by cutting out much of “what is going on,” leaving only a supposedly universal effect and controlling out differences at the higher level. * - usual standard errors. Panel Data 4: Fixed Effects vs Random Effects Models Page 4 Mixed Effects Model. 双重差分模型 （Difference-Differences, DID）是政策评估的非实验方法中最为常用的一种方法，其中 交互项是DID的灵魂 。 交互项形式拥有各种形式，包括（1）传统DID；（2）经典DID；（3）异时DID；（4）广义DID；以及（5）异质性DID。下面分别介绍这几种。 1. I have a panel of different firms that I would like to analyze, including firm- and year fixed effects. xtreg data18 data128 data6 data12 if ok, re Random-effects GLS regression Number of obs = 3957 Group variable (i): firmid Number of groups = 1319. Difference in differences (DID) Estimation step‐by‐step * Estimating the DID estimator reg y time treated did, r * The coefficient for ‘did’ is the differences-in-differences estimator. xtreg y x1 x2 x3, fe robust outreg2 using myreg. xtset fcode year. When you use -xtreg, fe- Stata has one particular strategy for dealing with the colinearity. Objective: Evaluate racial differences in reproducibility of hormone levels over time (estradiol, DHEAS, FSH, and testosterone) while adjusting for covariates previously identified as relevant in the study population. This kind of ANOVA tests for differences among the means of the particular groups you have collected data from. * (which for RE simplify by assuming lamda_hat is known not estimated). Fixed-Effects Model & Difference-in-Difference xtreg health retired , re // + time-constant explanatory variable. In contrast, an RE approach explicitly models this difference, leading “to a richer description of the relationship under scrutiny” (Subramanian et al. Test of the Difference Between Two Non-Zero Coefficients We first convert r to Fisher’s Z statistics: We then assume a normal distribution for Z 1-Z 2 and use the. See xtcd above for a more flexible procedure. , the difference. 0574 max 4 F(4,1148) 121. See full list on econometricstutorial. 5s, and the new version of reghdfe takes 0. xtreg ln_wage age race tenure, re. reg is the typical regression command in Stata that tells the program you are looking to linearly regress a dependent variable on other independent variable(s). R-square shows the amount of variance of Y explained by X. Reading and Using STATA Output. Stata can manipulate data, calculate statistics, and run regressions. For example, instead of working at α = 0. It automatically conducts an F-test, testing the null hypothesis that nothing is going on here (in other words, that all of the coefficients on your independent variables are equal to. Frequently there are other more interesting tests though, and this is one I've come across often -- testing whether two coefficients are equal to one another. , xtreg_fe takes 2. How can method 3 be wrong? Because it fails to account for the fact that the means we removed are *ESTIMATES*. This dataset has complete data on 4,702 schools. BASICS ** Open "neighborhood" data *1 xtsum schid, i(schid) *2 ** Sort by level-2 unit identifier sort schid *3 browse schid attain *4 tab schid *5 *Download. OLS applied to the FD regression (8) yields the so called ﬁrst-difference estimator. Same as Stata website file psidextract. 0276 avg = 7. 99 chi2 model fitted on these data fails to meet the asymptotic assumptions of the Hausman test; see suest for a generalized test. Because k can be large, this is not always feasible to estimate. 4s Without clusters, the only difference is that -areg- takes 0. xi_ areg stata, Stata is a powerful statistical software package, used by students and researchers in many fields. AMMBR from xtreg to xtmixed(+checking for normality, and random slopes, it might converge to different estimates for different algorithms in the iterative process. In a second estimation I also include some other >> covariates. * - panel bootstrap standard errors. ABSTRACTThe impact of privatization on public service quality is an enduring issue in public policy and management. I got same coefficient estimates but rather different R-squared. This is, it accounts for individual heterogeneity. In general, differencing removes all time constant variables (such as gender). You have the same problem. b = consistent under Ho and Ha; obtained from xtreg. without robust and cluster at country level) for X3 the results become significant and the Standard errors for all of the variables got lower by almost 60%. Then, you run xtreg Temperature treatment treated time. * Centrality paper * Regressions using the whole Census data - Male 20-65, all MSA's * DTA database: US_allvars. 3249 Prob F 0. Carter Hill (2011) * John Wiley and Sons, Inc. 请教是不是第一个检验结果是用固定效应模型，第二个用随机效应？. The difference between the two estimates (for the samples where Z>=0 and where Z<0) is the estimated effect of treatment. do May 2001 (began October 1999) * To run you need file * patr7079. 05, you use α = 0. Before you use xtreg you must classify the data as a panel dataset by using the xtset command (xtset entity year). How to estimate and interpret random coefficient models. Prob>chi2 = 0. Below is a specifically empirical problem and a case where the commands do not seem to be generating what I want. > At first, I estimate the following model: > y b0+b1Time+b2Treatment+b3Time*Treatment+u > > using the -reg command: > > -reg y time treatment time*treatment, cluster (h1) > > while y is the outcome variable that is between 0 and 1 and h1 is. This command operates as a post-estimation command following xtreg, fe or re. How to estimate and interpret random coefficient models. The command in Stata is xttest0. The difference is real in that we are making different assumptions with the two approaches. However, the characteristics of the two groups are different, i. dta}, which is distributed with Stata. * setup version 11. xtreg data18 data128 data6 data12 if ok, re Random-effects GLS regression Number of obs = 3957 Group variable (i): firmid Number of groups = 1319. xtreg iq mIQ if treatment == 1, fe *Adding fixed effects for individual participants by specifying ", fe" at the end of the xtreg command gets us the accurate p-value (0. some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. > Now, I want to estimate the impact in a difference in difference design. clear set mem 25m use c:/kate/manuscripts/education/revision/classtex. but in the last situation (4th, i. smcl", replace use "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2 - schools. Fixed-Effects Model & Difference-in-Difference xtreg health retired female i. Panel data allows you to control for variables you cannot observe or measure like cultural factors or difference in business practices across companies; or variables that change over time but not across entities (i. 4s Without clusters, the only difference is that -areg- takes 0. 上学期的面板数据分析课程大作业是复制一篇经典文献，我选择了一篇运用DID方法的教科书般的文献——Compulsory Licensing：Evidence from the Trading with the Enemy Act。. What is the nature of the variables that have been omitted from the model? a. This example goes through these different ways and discusses the advantages and disadvantages of each. • Heteroskedasticity can also occur if there are subpopulation differences or other interaction effects (e. In particular, xtreg, fe provides what isknown as the fixed-effects estimator—also known as the within estimator—and amounts to usingOLS to perform the estimation of (3). In the xtreg, fe procedure the R2 reported is obtained by only fitting a mean deviated model where the effects of the groups (all of the dummy variables) are assumed to be fixed. The Pesaran (2015, Econometrics Reviews) paper shows that the CD test is really a test for weak cross-section dependence rather than independence. Sector residencial. Fixedeﬀect-xtreg-YoushouldalwaysestimateFE-modelsusing-xtreg-(exceptspeciﬁcation searching). See xtcd above for a more flexible procedure. 025; Instead of working at α = 0. xtreg iq mIQ if placebo == 1, fe *The placebo effect is the familiar -. Here we reject the null and conclude that random effects is the appropriate model because there is evidence of significant differences across women. Patient(s): African American and Caucasian women identified by random. 0000 We have mechanically succeeded in greatly reducing the ˜2, but not by enough. xtreg, be provides what is known as the between estimatorand amounts to using OLS to perform the estimation of (2). xtreg estimates within-group variation by computing the differences between observed values and their means. The hypothesis was that the. Type II ANOVA , also known as random-effect ANOVA , assumes that you have randomly selected groups from an infinite (or at least large) number of possible groups, and that you want to reach conclusions about differences among ALL the. 36 Hausman test. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg. In our example, because the within- and between-effects are orthogonal, thus the re produces the same results as the individual fe and be. For the fixed-effects model,. The command xtreg is equivalent to the reg command, but takes into account the panel nature of the data. November 2018 at 1:48. verifierid, fe: Fixed effects and rank for adjusted discrepancies for the eight major verifiers with different samples. You have the same problem. , xtreg_fe takes 2. The treatment dummy is only included in the xtreg for better "comparison". 上学期的面板数据分析课程大作业是复制一篇经典文献，我选择了一篇运用DID方法的教科书般的文献——Compulsory Licensing：Evidence from the Trading with the Enemy Act。. Wang Qunyong of Nankai University (this command has been officially recognized by Stata; the third is Sun Yat-sen University Lian Yujun The teacher’s xtthres command. > At first, I estimate the following model: > y b0+b1Time+b2Treatment+b3Time*Treatment+u > > using the -reg command: > > -reg y time treatment time*treatment, cluster (h1) > > while y is the outcome variable that is between 0 and 1 and h1 is. The effect is significant at 10% with the treatment having a negative effect. Stata can manipulate data, calculate statistics, and run regressions. You obtain that the difference-in-difference estimator is -1. , areg takes 2 seconds. inside sport events and I also 'd like to know if this effect would be moderated. We knew already. In our example, because the within- and between-effects are orthogonal, thus the re produces the same results as the individual fe and be. some investigation tells me that what I need is a cox model with time-varying discrete covariates model -- but that is not making a lot of sense to me right now. , the difference. Patient(s): African American and Caucasian women identified by random. xtreg with its various options performs regression analysis on panel datasets. Frequently there are other more interesting tests though, and this is one I've come across often -- testing whether two coefficients are equal to one another. In this study, we applied this design to study the role of education and health behaviors in sickness absence, taking sex and cohort differences into account. Difference-in-differences estimation in Stata Nicholas Poggioli research methods , stata May 23, 2017 May 23, 2017 1 Minute I created a short. Fixed-Effects Model & Difference-in-Difference xtreg health retired female i. set more off cap log close log using "C:\Users\amitc\Warwick\Teaching\EC338\PS2\ps2. So basically I want to run something like: Y = D * I, where D is a binary variable equal to one if the state is treated and D is a continuous variable representing the number of states being treated. Jul 02, 2016 · reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects (including heterogeneous slopes), alternative estimators (2sls, gmm2s, liml), and additional robust standard errors (multi-way clustering, hac standard errors, etc). STATA 几个回归命令_经济学_高等教育_教育专区。stata关于回归的几个命令及对比。regression. The command in Stata is xttest0. Table 3 Difference-in-Differences estimation results. As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs. See full list on stats. _regress y1 y2, absorb(id) takes less than half a second per million observations. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. Then, you run xtreg Temperature treatment treated time. In the tird xtreg you compute the "interaction" robust matrix and you save it as V12. Difference-in-difference estimators are a special case of lagged regression Posted by Andrew on 15 February 2007, 12:31 am Jens Hainmueller has an interesting entry here about estimating the causal effects of the 2004 Madrid bombing on the subsequent Spanish elections, by comparing regular votes to absentee votes that were cast before the bombing. B = fully efficient estimates obtained from xtreg. differences between races in missing hormone data, and these missing data are assumed to be random. What is difference between Cross-sectional data and panel data? Academically there is difference between these two types of data but practically i my self do not see any difference.