Hac standard errors stata. st: Re: "White" standard errors. L. Aug 30, 2015. Specifying wmatrix(hac kernel opt) requests an HAC weight matrix using the specified kernel, 14. , breaks, time-varying parameters/volatility, regime We would like to show you a description here but the site won’t allow us. But as I said, s. when you assume observations are independent and they aren't, you can massively understate standard The HAC coefficients and standard errors can be calculated by the Real Statistics array formula =HACCoeff (B4:C23,D4:D23, 3), described as follows. dta" reg y x, robust reg y x, vce (cluster firm) R: The R function lm generates OLS estimates. I found some examples on this forum, but these don't exactly match my situation. We obtain coefficient estimates (e. , firms or villages) over time. Standard approach: Newey-West Standard Errors. E. $\endgroup$ Jun 16, 2010 · 0, than this estimate is equivelent to White standard errors (with spatial correlations accounted for). But in the presence of heteroskedasticity and autocorrelation, all we know is that the OLS standard errors are biased and inconsistent. ) Eg. The regression object, reg, is then an argument when computing standard errors using vcovCL. Nonlinear least-squares regression. They do not change the estimation of the coefficients. 75T. Once you run -xtreg, fe-, Stata will automatically cluster on your panel variable. Any advice will be greatly appreciated. glm can also calculate Heteroskedasticity and Serial. model in which the errors are serially correlated. Dear Stata Users, By using Stata's glm command, we can fit models that are fit by other. 055 1. It gives a gentle introduction to Subject. i. for standard errors – In pure trend or seasonality models set m=1. May 31, 2015 · Some robust covariance matrices make additional assumptions about the data without checking. Watson (2015). See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series. Although Newey{West standard errors have initially been proposed for use with time-series data only, panel versions are available. s, also being robust to serial correlation and not just heteroskedasticity. This has several benefits, including being much more efficient since you don’t have to re-estimate your whole model. Check in to the kind available to you in the commands you’re using. Implementation The spcgmm Command Syntax for spcgmm In that case, -reg y x, vce (hac nw opt)- (for example) would be a very convenient way to estimate OLS with HAC standard errors (with the bandwidth "optimally" chosen). , 1996) for the residuals with several embedded dimensions Jan 25, 2017 · 25 Jan 2017, 21:46. The coefficient estimates are found using the fitlm command. We’ll try to highlight Sep 17, 2020 · So we can use robust standard errors to still obtain consistent estimation of standard errors when they are not homoscedastic. The DK-HAC estimator extends the Newey-West’s (1987, 1994) and Andrews’ (1991) HAC estimators to flexibly account for nonstationarity (e. you must not reuse the id of a dropped out unit for another unit in a subsequent period. time() -ptm user system elapsed 1. Cách dùng mô hình sai số chuẩn mạnh (Robust Standard Errors) Trước khi thực hiện tải về bộ dữ liệu này để tiện thực hành nhé! DỮ LIỆU MOSL. Stata has a built-in function called xtreg that can estimate fixed effects models with HAC standard errors. For example heteroscedasticity and autocorrelation robust standard errors or Newey-West, HAC, standard errors assume a sequential time series structure. M. Newey-West estimator: declining average of sample autocovariances. Newey-West tends to be used as "catch-all" HAC standard errors, whereas Hansen-Hodrick comes up frequently in the context of overlapping data points (e. VN Tải ngay. nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions and their SEs, residuals, etc. And what does it suggest about the Because of autocorrelation there is the need to update the summary with newey west standard errors. Specifically, the errors are assumed to follow a first-order autoregressive process. While we’re using the time series datasets, often we’re highly likely to find serial correlation and heteroskedasticity in our data. Hence, I wonder which regression type and which standard errors are most applicable as they Standard errors. s only robust to heteroskedasticity seem more sensible in a VAR, as remaining serial correlation should, in my view, be captured through extra lags in the VAR. But, the results are different. Sep 8, 2015 · Hansen and Hodrick wrote their paper before GMM was developed, so they focused on the homoscedastic case. From the Stata manual, for. Dec 14, 2010 · You have two options: (1) you xtset your data in stata and use the xtreg option with the fe modifier or (2) you use plm with the pooling option and one dummy per ENTITY. The tables also include the accompanying asterisks beside the parentheses/brackets indicating statistical significance. Heteroskedasticity-Autocorrelation Robust Standard Errors Using the Bartlett Kernel Without Truncation Nicholas M. If # is not specified, N 2 is Jan 31, 2019 · HAC s. 837 proc. Robust, cluster–robust, bootstrap, jackknife, and HAC standard errors. " You seem to have a small-T panel (5 years as you say), so the Arellano option would be possible. vcovCL sandwich vcovCL. Date. Feb 1, 2012 · The last four columns of Panel A report H A C S C robust standard errors for a range of bandwidth choices using the Bartlett kernel. There is one final important difference. However, I find large BDS test statistics (Broock et al. The syntax used with vce(hac kernel :::) is identical to that used with wmatrix(hac kernel :::); see wmatrix() above. g. stats. Apr 3, 2015 · And if there is no evidence for autocorrelation (or heteroscedasticity) then the OLS standard errors are probably fine. Naturally, the standard errors of my second stage regression do Nov 14, 2016 · I have a panel data set with N=9 and T= 72. Vogelsang†‡ September, 2000; Revised February, 2001 Abstract In this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. MULLER¨ Department of Economics, Princeton University, Princeton, NJ 08544 (umueller@princeton. * Load in auto data sysuse auto. The bandwidth of a kernel is equal to the number of lags plus one. This video provides a demonstration of how to generate robust standard errors and test statistics for regression estimates when performing linear regression Nov 3, 2021 · 2. Also see [SEM] Intro 7 — Postestimation Apr 23, 2021 · 1) under -xtreg- (I assume you're using this -xt- command) both -robust- and -cluster- options do the very same job (as they tell Stata to adopt a cluster-robust standard error); 2) running regressions with different specifications and obtaining different resulst comes with no wonder at all. s are a type of robust s. For instance, -reg- is robust to heteroscedasticity—but results in unclustered standard errors. 2. Since the usual R2 depends on the coefficients but not on the SEs, you could get the R2 from -regress-. Impulse{response functions can be computed after many estimation commands with the irf create command. identical to the White estimator. -ivreg2- requires Stata 9. May 5, 2023 · Posts: 7. HAC refers to the general form for combining weighted matrices to form the variance estimate. HAC standard errors, ctd. I recommend that you start at the beginning. With HAC standard errors you can select the Bartlett, Parzen, or quadratic spectral kernel, and you can specify the number of lags or request that Newey and West’s optimal lag-selection algorithm be vce(oim) standard errors. The membership to a time series of an individual or group can be either specified by group indicators or by increasing time periods. Mô hình sai số chuẩn mạnh thường được đưa vào để khắc phục hiện tượng PSTĐ cụ thể: Chỉ cần bỏ thêm lệnh Apr 27, 2022 · Heteroskedasticity and autocorrelation consistent standard errors (HAC) have become a staple in time series econometrics since their development by Newey and Nov 16, 2022 · ivregress can provide robust, cluster robust, jackknife, bootstrap, and heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. edu) Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. The VAR (p)-model assumes that the residuals are Gaussian white noise. tsa. using ) and then use. Is there any impact on the f-statistic by using newey-west standard errors? j<b) under exogeneity and normal errors e(a,b) E(y jja <y j<b) under exogeneity and normal errors ystar(a,b) E(y) j, y j = maxfa;min(y j;b)gunder exogeneity and normal errors stdp not allowed with margins stdf not allowed with margins residuals not allowed with margins Statistics not allowed with margins are functions of stochastic quantities We would like to show you a description here but the site won’t allow us. Sep 12, 2017 · As you can see from the results, point estimates are roughly the same but their robust HAC standard errors are quite different because of different bandwidth choice (8 vs. A cookie is a small piece of data our website stores on a site visitor's hard drive and accesses each time you visit so we can improve your access to our site, better understand how you use our site, and serve you content that may be of interest to you. So the command line would look like: xtset country time. ) Williams, R. Stata commands. first requests that the first-stage regression results be displayed. The first two estimation results are identical, but the last one is slightly different from others. Jun 4, 2020 · How to use HAC errors in VAR model. 2000. Real Statistics Function: The following array function is available in the Real Statistics Resource Pack where R1 is an n × k array with X data elements and R2 is an n × 1 array with Y data Oct 6, 2022 · For continuous covariates, the conclusions are different. n • Use reg. Matching Stata to R: xtset entity year. from the package to compute the standard errors. Easily specify panel-style instruments. Fortunately, the calculation of robust standard errors can help to mitigate this problem. 4T. tl;dr: Fast computation of standard errors that allows for serial and spatial auto-correlation. 1/3 – In dynamic regression set m=. cov_hac module, but I don't see how to make it work with OLS. Kiefer∗ TimothyJ. The same may be true in your application. In principle, they are different things. 650 0. 3, 88–94. It is also expected that the fixed-effect estimator Nov 14, 2011 · HAC procedures are just about providing consistent estimates of the standard errors. st: Re: Newey-West robust errors. If I'm not mistaken, -newey- will report the same regression coefficients as -regress- but with HAC-robust standard errors. On Thursday, Apr 24, 2003, at 02:33 US/Eastern, Nerissa wrote: How do I calculate in Stata, White's heteroscedastic-consistent standard errors for each parameter estimate? I have found only the command for calculating the White's chi-square test statistic. It seems like the name stuck, so now people say Hansen-Hodrick standard errors when they use GMM standard errors with a truncated, equal-weight, kernel. vector_ar ). This is anticonservative as Stata 5. Hello everyone, Since my error terms are heteroscedastic, I am trying to figure out how I can obtain heteroskedasticity-robust standard errors in Stata. ado in your personal ado folder and install them using the stata command : >install *. dta, clear * Just add robust to the options of the regression * This will give you HC1 regress price mpg gear_ratio foreign, robust * For other kinds of robust standard errors use vce In the one-dimensional regression problem a value of β–β 0 chosen so that β is contained in the (true, not size-distorted) 90% HAC confidence interval with probability 0. There are three kernels built into glm. vce(hac kernel #) specifies that a heteroskedasticity- and autocorrelation-consistent (HAC) variance estimate be used. With HAC standard errors you can select the Bartlett, Parzen, or quadratic spectral kernel, and you can specify the number of lags or request that Newey and West’s optimal lag-selection algorithm be The approach to computing clustered standard errors is identical in all cases we consider. lm. Reporting level(#); see[R] Estimation options. Quick start Prais–Winsten regression of y on x estimating the autocorrelation parameter by a single-lag OLS regression of residuals using tsset data prais y x 5 days ago · ‘hac-panel’: heteroscedasticity and autocorrelation robust standard errors in panel data. I am trying to replicate the ivreg output of a regression performing manually the first stage, predicting the instrument after the first stage and running the second stage regression with the instrument in place of the endogenous regressor in the structural model. For the individual level data, I am trying to use HAC standard error, for the group level data I am trying to use Driscoll and Kraay standard errors, for the . Specifying wmatrix(hac kernel #) requests a heteroskedasticity- and autocorrelation-consistent (HAC) weight matrix using the specified kernel (see below) with # lags. First, taking sample size on its own as a criterion is not enough to obtain accurate standard errors in the presence of heteroskedasticity. Nov 16, 2022 · Linear and nonlinear models. The most common method (by far) for computing HAC/HAR SEs is to use the Newey- West (1987) estimator. This script stores estimation results in standard Stata formats, so most "ereturn" commands should work properly. 19). The most common method (by far) for computing HAC/HAR SEs is to use the Newey-West (1987) estimator. On Oct 26, 2010, at 2:33 AM, Leon wrote: > Hi, I am new to Stata and try to measure herd behavior as deviations in the return dispersion of a large panel dataset. Re: st: Robust Standard Errors in Paneldatasets. Stata. In Stata, Newey{West standard errors for panel datasets are obtained by choosing option force of the neweycommand. . You run summary() on an lm. Thu, 24 Apr 2003 10:55:29 -0400. If you have strict exogeneity with serial correlation, your coefficients are unbiased, but the standard errors are incorrect. This may have an easy answer. A note on robust variance estimation for cluster-correlated data. 895 CDD 1. Single- and multiple-equation models. These cases increase the chances to obtain serially correlated errors with non-constant variance. This means that the usual OLS standard errors (usual STATA printout) are wrong! We need to use, instead, SEs that are robust to autocorrelation as well as to heteroskedasticity This is easy to do using STATA and most (but not all) other statistical software. 386 This matches the standard errors from the Stata output. Not least due to the "robust" option in Jan 22, 2021 · HAC robust standard errors. Results are stored in a le with the irf set command. Nov 16, 2022 · ivregress can provide robust, cluster robust, jackknife, bootstrap, and heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. <>. summary(lm. HAC Corrections for Strongly Autocorrelated Time Series Ulrich K. We propose a Stata command, lrcov, to compute long-run covariance with a prewhitening strat-egy and various kernel functions. Correlation (HAC) adjusted errors. example, the following newey and glm produces the same results. 0 includes an F test in the header of the output that is the Wald test based on the robust variance estimate. robust standard errors, as does the option in Stata. In the context of the Stata* command -xtreg, fe- (and, if I remember correctly, only in that context), however, vce (robust) is automatically changed by Stata to vce (cluster xtpanelvar). 786 0. for forecast intervals, or iterated forecasts with forecast Long-run covariance plays a major role in much of time-series inference, such as heteroskedasticity- and autocorrelation-consistent standard errors, gener-alized method of moments estimation, and cointegration regression. Dec 10, 2021 · To avoid errors, make sure to assign each unit a unique id. d. Feb 28, 2017 · How to implement heteroscedasticity-robust standard errors on regressions in Stata using the robust option and how to calculate them manually. By default, computes. If we’re purely interested in statistical inferences, we should go for the HAC to obtain standard errors robust to spatial^ Jeanty (Rice) Spatial HAC in Stata July 26-27, 2012 10 / 29. Results can be displayed in graphical form with irf graph or in tabular form with irf table. Biometrics 56: 645–646. What matters is the number of observations per regressor. Jordan caught the paper of Field and Zhou (2003), HAC standard errors for M-estimators with known scale were studied. If you believe or know that you do not have remaining autocorrelation you could use PCSE instead, allowing for some cross unit correlation. The hreg command used n-1 as the degrees of freedom for the t tests of the coefficients. st: HAC Errors and glm. Such a command would supercede (and thus deprecate) both -newey (official Stata) and -newey2- (which Kit's message indicated has already been deprecated by its author). Aug 21, 2017 · OLS Spatial Spatial_HAC HDD 0. Cross-sectional, time-series, and panel models. ˆ 1 ˆ. 10. It gives you robust standard errors without having to do additional calculations. Newey-West estimator: declining average of sample autocovariances ˆ 1 ˆ m NW j jm j m where ˆ Jun 22, 2018 · Beck and Katz call the associated standard errors “Panel-Corrected Standard Errors” (PCSE). (Based on an asymptotic calculation) (ii) Robustness to volatility breaks Since standard model testing methods rely on the assumption that there is no correlation between the independent variables and the variance of the dependent variable, the usual standard errors are not very reliable in the presence of heteroskedasticity. ado About Spatial and serial correlation adjustment for regressions with high dimensional fixed effects. May 29, 2020 · Note, -robust- handles uncertainty differently depending upon whether you're estimating your model using -reg- or -xtreg, fe-. #1. Hence one important aspect of my question is, is there anything about Hansen-Hodrick that makes it more suited to deal with overlapping data than Newey-West? Feb 18, 2021 · About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket Press Copyright May 14, 2024 · Here, we will provide examples of how to estimate fixed effects models with HAC standard errors using Stata, R, and Python. xtreg y v1, fe robust. Robust standard errors (the “r” option in Stata) are the most commonly used in current ap-plied practice. Thanks. 619 0. My data is not really a panel (Each Loosely, when estimating standard errors: If you assume something is true and it isn't true, you generally lose consistency. One-step, two-step, and iterative estimators. ado and ols_spatial_HAC. Please, how can I obtain the R-squared. 0 now uses g-1 as the degrees of freedom. e. The topic of heteroskedasticity-consistent ( HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis. In addition to the standard White covariance estimators from above, EViews supports the bias-correcting HC2, pseudo-jackknife HC3 (MacKinnon and White, 1985), and the leverage weighting HC4, HC4m, and HC5 (Cribari-Neto, 2004; Cribaro-Neto and da Silva, 2011; Cribari-Neto, Souza, and Vasconcellos, 2007 and 2008). The Jan 9, 2017 · Jan 10, 2017 at 23:48. Wooldridge, J. For example, if the treatment is at the village level or state level, we often cluster errors at that level. May 2, 2014 · I get the coefficients and associated with them standard errors. Feb 23, 2016 · This is the twenty-second post in the series Programming an estimation command in Stata. However, more often than not robust standard errors means the HC0 standard errors, originally developed by Eicker and Huber, and later popularized by White. There are three kernels available for nl: nwest jgallant janderson # specifies the number of lags. 608 0. Both use the Bartlette kernel function. With lag_cutoff you can set the cutoff along the time dimension. 23. Thanks for all your work, Esben. This software implements the DK-HAC covariance matrix estimators and robust standard errors from Casini (2021) and Casini and Perron (2021b). predictnl point estimates, standard errors, testing, and inference for generalized predictions pwcompare pairwise comparisons of estimates test Wald tests of simple and composite linear Nov 16, 2022 · Title : Comparison of standard errors for robust, cluster, and standard estimators: Author: William Sribney, StataCorp I found an R function that does exactly what you are looking for. sandwich_covariance. The value of β is contained in the KVB 90% CI with probability 0. (A PDF of this article can be found here. However, I have each survey cluster's latitude/longitude, so I was hoping to utilise this to use Conley standard errors. Reprinted in Stata Technical Bulletin Reprints, vol. I see statsmodels. kernel is a user-written program or one of nwestjgallantjanderson # specifies the number of Nov 16, 2022 · This website uses cookies to provide you with a better user experience. As the number of observations rises, your estimate need not converge in probability to the true value. A pragmatic approach could also be to simply try whether using Newey-West standard errors makes a relevant difference. Since I have large T and -nl allows for HAC standard errors, I wanted to use the Newey-west type. I tried ols_cluster_hac but I don't think it is appropriate. These are also known as heteroskedasticity-robust standard errors (or simply robust standard errors ), Eicker–Huber–White standard errors (also Huber–White standard errors or Aug 27, 2021 · These clusters change for each survey round, so I'm not sure if I can use standard errors clustered at the survey cluster level. Correcting for Spatial and Temporal Auto-Correlation in Panel Data. Interactive and programmable versions. Now, if we use robust standard errors and that's it- have we restored 'BLUE"? i. HAC/HAR SEs are generically needed in time series regression. Beginners with little background in statistics and econometrics often have a hard time understanding the benefits of having programming skills for learning and applying Econometrics. How to obtain heteroscedasticity robust standard errors? 05 May 2023, 11:54. When these options are combined with either the gmm2s or cue options (see below), the parameter estimators reported are also efficient in the presence of the same violation of i. The default is 0, meaning that standard errors are only adjusted cross-sectionally. Nov 16, 2022 · Note also that Stata 5. errors. Dec 10, 2006 · To analyse data, we applied robust standard errors for fixed-effects panel regression (FE) (Hoechle, 2007) on the one side, and regression with Driscoll and Kraay (1998) standard errors (D-K FE Oct 4, 2015 · Instead you're going to estimate the robust standard errors separately like in the following little piece of code using hac. , weather, internet, crime), there is often another problem that arises requiring clustering standard Sep 2, 2015 · OLS Spatial Spatial_HAC 0. vce(oim) standard errors are unambiguously best when the standard assumptions of homoskedasticity and independence are met. vce(hac kernel #jopt #) specifies that an HAC covariance matrix be used. I use Stata. Dec 14, 2022 · EViews allows you to estimate your covariances using several choices for . Stata has robust standard errors built into most regression commands, and they generally work the same way for all commands. When working spatial or remote sensing data (e. Stata provides an irf suite of commands to estimate, manage, and display impulse{response functions. The standard errors were computed in Stata using the xtscc procedure developed by Hoechle (2007). The hypothesis of known scale, however, is unrealistic in practice and eliminates the quickly move beyond classical standard errors. HAC standard errors address the latter point. For some point estimates, the H A C S C robust standard errors systematically decrease as the bandwidth increases Sep 11, 2022 · In practice, it is common to cluster standard errors at the level of the treatment. 493 4. I want to ask first of all if there exists any difference between robust or cluster standard errors. Economists and political scientists often employ panel data that track units (e. Stata Technical Bulletin 13: 19–23. ivreg2 irx t, robust bw(9) Notice that it reports the presence of gaps but does not choke on them. python The above-mentioned thread seems to claim that "ivregress 2sls" is used to calculate HAC standard errors and if we choose "nw" option, it should calculate the same standard errors as "newey" does. If the autocorrelations in the residuals are small, then Newey-West should lead to very similar results anyway. object, robust=T) Despite its name it is happy to estimate OLS models without any instruments, and it can estimate Newey-West standard errors as well as a variety of other HAC models. Darin Christensen, Thiemo Fetzer. 3. From the literature, two practical considerations arise. 886 0. @mugen The term robust standard errors is sometimes used as an umbrella term for HC, HAC, and other sandwich standard errors. ‘Introduction to Econometrics with R’ is an interactive companion to the well-received textbook ‘Introduction to Econometrics’ by James H. 065 4. I am using Nonlinear Least Squares to estimate y_it = G (X_it, Beta) where X is a vector. 2002. The syntax for estimating a country-year fixed effects model with HAC standard errors in Stata is: Found. I. They are appropriate for time-series models which are dynamically well-modeled, including autoregressive and ADL models. A poisson command with options for a robust or a cluster–robust VCE vce(hac kernel #) specifies that a heteroskedasticity- and autocorrelation-consistent (HAC) variance estimate be used. Estimating the model and computing the standard errors requires just over 1 second, making it over 20 times faster than the comparable STATA routine. The document has moved here. Alternatively, CGM provide the cgmreg command, downloadable from. does this just give us consistent estimation of the standard errors, or does it also restore the 'Best', minimum variance property? Feb 7, 2019 · Here are examples producing identical estimates and roughly the same output in Stata and R. see this question or this question ). The data needs to be sorted in this case, the time series for each panel unit or cluster need to be stacked. Some panel data robust standard errors also assume stacking of the time series by individuals. Less efficient means that for a given sample size, the standard errors jump around more from sample to sample than would the vce(oim) standard errors. Panel Dimension and optional HAC standard errors Allows for sampling weights (pweights) Allows for ‘if’ and ‘in’ statements Allows for partialling out up to 2 high-order xed e ects Produces output similar to Stata’s native commands Allows for storing distance matrix and weights matrix Stores main results in e() – If the errors are serially uncorrelated, use r option – If the errors are correlated • Use newey. Aug 13, 2018 · I often see regression tables in publications where the plain standard errors are reported (in parentheses), together with the robust standard errors (in brackets) below the plain standard errors. Regression standard errors in clustered samples. HAC standard errors (Newey-West, the “Newey” command in Stata Aug 30, 2015 · Contact. There are multiple kinds of cluster-robust standard errors, for example CR0, CR1, and CR2. After updateting the t-statistics changed dramatically, but the f-statistic (required to determine the significance level of r^2) didnt change. Standard approach: Newey-West Standard Errors HAC/HAR SEs are generically needed in time series regression. Matching plm to Stata: The logic to compute the ^ matrix, as CGM show, is a generalization of the formula for two-way clustering, and may be implemented using only one-way cluster-robust estimates available from many Stata estimation commands. Also Consider Cluster Bootstrap Standard Errors, which are another way of performing cluster-robust inference that will work even outside of a standard regression context. Corrected Code. object and if you set the parameter robust=T it gives you back Stata-like heteroscedasticity consistent standard errors. Tue, 26 Oct 2010 07:56:06 -0400. Mon, 9 Jan 2012 11:16:33 +0200. 844 . Stata: use "data/petersen. 1/3. It is the case that if the only departure from iid errors is positive AR (1), the OLS standard errors will be downward biased relative to the correct standard errors. and predict sf, stdf. If lagcutoff = infinity, than this treatment is equivelent to the "cluster" command in Stata at the panel variable level. To use this code, copy reg2hdfespatial. I would like to use the Newey-West method for the standard errors in a VAR (p)-model (I use statsmodels. That is, it is dominated by the time dimension. Stock and Mark W. The standard errors and test statistics reported by ivreg2 can be made consistent to a variety of violations of the assumption of i. While you can specify standard errors inside the original fixest model call (just like Stata), a unique feature of R is that you can adjust errors for an existing model on-the-fly. (This is bad. aa tz iq cw vz im le ew qz xi