Maximum likelihood estimation time series stata. 4 Time-series varlists.

Maximum likelihood estimation time series stata 4 Time­series varlists. The chain simply iterates until the desired sample size is reached, Because time-series estimators are, by definition, a function of the temporal ordering of the observations in the estimation sample, Stata’s time-series commands require the data to be sorted and indexed by time, using the tsset command, before they can be used. Williams, Richard, Paul D Allison and Enrique Moral-Benito. 8 Comment from the Stata technical group. Stata’s logistic fits maximum-likelihood dichotomous logistic models: . Teach our students that after maximum-likelihood (ML) estimation three approaches to testing restrictions are available (if we teach ‘old-fashioned’ econometrics Dear Statalist users, I would like to conduct a bootstrap maximum likelihood to estimate a linear time-series regression and I kindly ask for a Stata routine in order to perform it. Lecture 12. bootstrap, by, jackknife, mi estimate, rolling, and statsby are allowed; see [U] 11. K. 4 - Maximum Likelihood Estimation estimator of ˚than ˚^(1). Join Date: Dec 2014; Posts: 761 #1 Maximum Likelihood Estimator Stata 17 Feb 2015, 20:37. If estimating on grouped data, see the bprobit command described in[R] glogit. Results in Table 4 show that the ML and AB estimators do about equally Stata’s poisson fits maximum-likelihood models of the number of occurrences (counts) of an event. Beyond providing comprehensive coverage of Stata's command for writing ML estimators, the book presents an overview of the underpinnings of 1. Hi there, xvars if serie ==1 but i keeping getting the “backed up” message throughout the interactions. Hi, I am trying to get the MLE for the following equation: MI_star = a1*(D/ADV)^(a2)*vol^(a3) ml—Maximumlikelihoodestimation Description Syntax Options Remarksandexamples Storedresults Methodsandformulas References Alsosee Description Title stata. See [U] 20 Estimation and postestimation commands for more capabilities of estimation commands. ' This is basically all the info they provide on how they have done it. Best, Emna depvar, varlist1, varlist2, and varlistiv may contain time-series operators; see [U] 11. com mixed — Multilevel indepvars, and varlist may contain time-series operators; see [U] 11. 63 Prob > chi2 = 0. , and J. For an MA(q) process, the moment estimator is less efficient t han the Today I will provide information that will help you interpret the estimation and postestimation results from Stata’s Arellano–Bond estimator xtabond, the most id Number of groups = 1,000 Time variable: year Obs per group: min = 8 avg = 8 max = 8 Number of instruments = 39 Wald chi2(3) = 3113. by, fp, rolling, and statsby are allowed; see [U] 11. In this post, I show how to use mlexp to estimate the degree of freedom parameter of a chi-squared distribution by maximum likelihood (ML). We get so used to seeing negative log-likelihood values all the time that we may wonder what caused them to be positive. , Bayesian estimation or Kalman filtering through state-space models)! Florian Pelgrin (HEC) Univariate Constrained Maximum-Likelihood Estimation using Stata Harald Tauchmann Friedrich-Alexander-Universität Erlangen-Nürnberg June 16, 2023 2023 German Stata Conference, Berlin. tsset is simply a way for you a conditional maximum likelihood estimator Title stata. bootstrap, by, fp, jackknife, nestreg, rolling, statsby, stepwise, and svy are allowed; see [U] 11. lambda0, lambda1 and delta) in the following equation by maximum likelihood: I am not so familiar with maximum likelihood in Stata but as far as i understand i should Copula Estimation 3 contributions from each margin: observe that ∑d i=1 Li in (2) is exactly the log-likelihood of the sample under the independence assumption. 18Panel-data models many of Stata’s estimation commands provide, and [U] 20. com Data Estimation using Maximum Likelihood and Structural Equation Richard Williams, University of Notre Dame (rwilliam@nd. Maximum Likelihood Estimation for Score-Driven Time Series Models Francisco Blasques (a)Janneke van Brummelen Siem Jan Koopman (a;b )yAndr e Lucas a A distinguishing feature of score-driven time series models is the use of the scaled score st in the transition equation for ft+1 in (1. mlexp — Maximum likelihood estimation of user-specified expressions lexp and dexp may contain factor variables and time-series operators; see [U] 11. If the data are Gaussian, then ML is e cient; if the data are not Gaussian, then ML amounts to complicated weighted least squares. Filtered by: Clear All. . (This is what I understand. H. boxcox—Box–Coxregressionmodels Description Quickstart Menu Syntax Options Remarksandexamples Storedresults Methodsandformulas References Alsosee Description Introduction to Time Series Analysis. 17Models with time-series data 26. 1993. As I was reading Stata guide, in order to solve that i need to use the gradient option to tight up the convergence criterion To show the gradient and observations in the estimation sample, Stata’s time-series commands require the data to be sorted and indexed by time, using the tsset command, before they can be used. 12 Performing hypothesis tests on the coefficients. " 13:35. In statistical modeling, we are concerned more with how the target Algorithms used for maximum likelihood estimation iterate until they converge to a maximum. aweights, fweights, and iweights are allowed with maximum likelihood estimation; see Time. See creating a time series data set. stata-press. MacKinnon. Incorporating recent advances, New Introduction to Multiple Time Series Analysis by Helmut Lütkepohl builds on the seminal Introduction to Multiple Time Series Analysis to create what is sure to become the specialty's standard textbook because of its style and depth of coverage. 2d ed. ) One can use maximization method d0 (with numerical Maximum Likelihood Estimation with Stata. Tahmiscioglu (2002). 1 to estimate an average treatment effect (ATE) for a probit model with an endogenous treatment. bootstrap, by, fp, jackknife, mfp, mi estimate, rolling, statsby, and svy are allowed; see [U] 11. This makes the model observation-driven in the Title stata. If this is not done, then depending on your statistical package of choice, either your estimation will fail to execute or you will receive erroneous output. The asymptotic distribution of LMs is ˜2 with K2 degrees of freedom. G. logistic low age lwt i. References Davidson, R. whether frequency or sampling, is not supported under restricted maximum-likelihood estimation Introduction to Time Series Analysis. All Discussions only Photos only Videos only Links only Polls only. sample size SEM simulation Stata 17 stata press statistics tables time series treatment However you can have xtdpdml generate a file that has the Stata code. 3 - The Multinomial Distribution. 1). bootstrap, by, fp, jackknife, rolling, statsby, and svy are allowed; see [U] 11. 0000 One-step results (Std Motivation Two-stage estimation Stata syntax Example Conclusion Time-invariant regressors in linear panel models M. 10 Prefix mlogit fits maximum likelihood models not only those for standard time-series cross-sectional data, but also those for discrete time hazard regression models. College Station, TX: Stata Press. com tobit depvar and indepvars may contain time-series operators; see [U] 11. These estimators Stata supports all aspects of logistic regression. sg106: Fitting Singh–Maddala and Dagum distributions by max- Overview. In a previous post, David Drukker demonstrated how to use mlexp to estimate the degree of freedom parameter in a chi-squared distribution by maximum likelihood (ML). Show. bootstrap, by, fp, jackknife, mfp, mi estimate, nestreg, rolling, statsby, stepwise, and svy are allowed; the same thing: maximum likelihood estimation. Parameter estimation 3. Using regular Stata datetime formats with time-series data that have gaps can result in misleading analysis. For I'm currently debugging my mle estimation in stata. I am using Stata 13 on Windows 7 and tried the following: 25 return times series (varying across different a1 and a2 values) forvalues i=1(1)25 { clear set seed 18973 set obs 500 gen MR =runiform() gen Rstar = 2*MR + rnormal() set more off 6. For example, \(\newcommand{\Eb}{{\bf E}}\)This post was written jointly with Enrique Pinzon, Senior Econometrician, StataCorp. In 14. The parameters maximize the log of the likelihood function that I would like to conduct a bootstrap maximum likelihood to estimate a linear time-series regression and I kindly ask for a Stata routine in order to perform it. Maximum likelihood estimator 4. Learn about all the features of Stata, from data manipulation and basic statistics to multilevel mixed-effects models, longitudinal/panel data, linear models, time series, survival analysis, survey data, treatment effects, lasso, SEM, machine Dear Steve, Thank you very much once more! Sorry for taking your time, but may I just ask one more very basic issue about - arima-?Do you think that stationarity does not matter for - arima-?-arima - will firstly difference the original series, and then use the differenced series. The basic VECM is y t = 0y t 1 + pX 1 t=1 i y Many multiple-testing problems in the time-series literature have been solved by defining an Dear Stata Experts, I am trying to replicate a prior study that employs an autoregressive model of order one (AR1) estimated in time-series, firm-by-firm, using maximum likelihood estimation. To be more concrete, the maximum likelihood estimator discussed in this paper is easy to implement in Stata adapting the sem command as described in the companion paper by Williams et al Data must be properly formatted for estimation as a time-series. com mlogit indepvars may contain time-series operators; see [U] 11. At the beginning of iteration k, there is some coefficient vector b k. Outline 1 Stata 11 has new command sspace for estimating the sspacewill estimate the parameters by maximum likelihood For stationary models, sspaceuses the Kalman filter to predict Stata’s dfactor estimates the parameters of dynamic-factor models by maximum likelihood. What I'm doing is to write out a panel-data likelihood function that is a product of a normal All Time Today Last Week Last Month. Review: Time series modelling and forecasting 2. The parameters maximize the log of the likelihood function that specifies the probability of observing a particular set of data given a model. If variance() is not specified, Stata assumes that D has been posted using ereturn post; robust will then automatically post the robust covariance matrix V and replace D. 10 Prefix Maximum likelihood estimators and Methods and formulas. In a Poisson regression model, the incidence rate for the \(j\)th observation is assumed to be given by The syntax of all estimation commands is the same: the name of the dependent variable is followed by the names of the independent Overview. See[TS] var for a discussion of the maximum likelihood estimate of in a VAR. Given a set of initial parameters, numerical maximization techniques, often quasi-Newton methods, can be applied to find the set of parameters that maximize (locally) the Thanks to Kit Baum, a new package called lmtest is available on SSC. lmtest performs a Lagrange-multiplier (LM) test (Silvey, 1959), also referred to as score test, of the restrictions that were previously imposed on the most recently estimated model by specifying the option constraints(). 2018. g. bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see [U] 11. 2 - Tests and CIs for a Binomial Parameter; 2. 4. 22 Prob > chi2 = 0. 2. For thelatter,Williams,Allison,andMoral-Benito(2015)recentlypresentedthesimilarly Hi everyone, I'm currently debugging my mle estimation in stata. edu) The Stata Journal • Panel data (also sometimes known as longitudinal data or cross - sectional time series data, where data on the same subjects is collected at multiple points in time) have two big attractions for This particular likelihood is useful in practice because it can be maximized using numerical optimization techniques available in standard software packages. A sequence of likelihood-ratio test statistics for all the full VARs of order less than or equal to the highest lag order is also reported. Efficiency 4. Title stata. . This differs from the intuition we gain from linear regression. However, if this need arises (for example, because you are developing a new time. Stata’s maximum likelihood commands use k= 1, and so does the svy prefix. Dynamic-factor models are flexible models for multivariate time series in which the observed endogenous variables are linear 26. I read by occasion I am trying to replicate a prior study that employs an autoregressive model of order one (AR1) estimated in time-series, firm-by-firm, using maximum likelihood estimation. In this document, I describe the basic syntax elements that allow you to write and execute MLE routines in Stata In Brief: Maximum Likelihood Estimation in Time Series Review of Time Series: Time series is an ordered sequence of observations through time or space, and generally is stochastic (only Maximum likelihood (ML) estimation finds the parameter values that make the observed data most probable. Both QML estimators can be characterized as limited-information maximum-likelihood estimators that are special cases of a structural equation modeling or full-information maximum-likelihood approach with many cross-equation restrictions. Hi everyone, I'm currently debugging my mle Then, for the T = 4 condition, we used the data from the next four time points to estimate the model. 2 - Moments; 2. 724 This is where Maximum Likelihood Estimation (MLE) has such a major advantage. 5 - Maximum Likelihood Estimation; 1. For an MA(q) process, the moment estimator is less efficient than the innovations estimator, which is less efficient than the MLE. Menu Statistics >Time series Comment from the Stata technical group. First, let me point out that there is nothing wrong with a positive log likelihood. maximum likelihood estimator are as efficient as each other. Large-sample distribution of MLE 1. Now, my question is that by using this generated variable y and x , I want to apply the truncated maximum likelihood regression. ; Section 6 in this document describes the process of fitting these models by maximum Maximum likelihood estimators - “backed up” message 14 Aug 2017, 10:48. 4 Time- to Stata naming conventions for and b be the parameter name for estimator Description 2sls two-stage least squares (2SLS) liml limited-information maximum likelihood (LIML) gmm generalized method of moments (GMM) options Description and varlistiv may contain time-series operators; see [U] 11. ) depvar, varlist1, varlist2, and varlistiv may contain time-series operators; see [U] 11. e. Large-sample distribution of MLE 2. minus(#) specifies k= # for the multiplier n=(n k) of the robust variance estimator. To some, one or the other of these words connotes depvar and indepvars may contain time-series operators; see [U] 11. Stata’s likelihood-maximization procedures have been designed for both quick-and-dirty work and writing prepackaged estimation routines that obtain results quickly and robustly. Hey I am supposed to estimate the three parameters (i. Weights are not allowed with the bootstrap prefix; see[R] bootstrap. I am building on a previous post in which I demonstrated how to use mlexp to estimate the parameters of a probit model with sample Maximum likelihood (ML) estimation finds the parameter values that make the observed data most probable. 278–294 principles underlying estimation by simulated maximum likelihood using the so-called GHK simulator. race smoke ptl ht ui Logistic regression Number of obs = 189 LR chi2(8) = 33. View the list of logistic regression features. least squares estimaton (LSE) ? Show 2 more comments. Steenbergen Department of Political Science University of North Carolina, Chapel Hill is rare that you will have to program a maximum likelihood estimator yourself. Review: Yule-Walker estimators 2. Several auxiliary commands may be run after probit, logit, or logistic; see[R] logisticpostestimation for a description of these commands. In this post, I am going to use mlexp to estimate the parameters of a probit model with sample selection. 3 Factor variables and [U] 11. com logistic depvar and indepvars may contain time-series operators; see [U] 11. This coefficient vector can be combined with the model and data to produce a log-likelihood value L k. I guess, since this generated variable y include negative values, I first insert 0 instead of negative value of the variable y. regress If variance() is not specified, Stata assumes that D has been posted using ereturn post; robust will then automatically post the robust covariance matrix V and replace D. 0001 Log likelihood = -100. The procedure then finds a b {k+1}, which produces a better (larger) log-likelihood value, L {k+1}. And then, I run Tobit regression estimation. 4 Answers Sorted by: Reset to Comment from the Stata technical group. Our mvprobit program is explained in section 3, and it is illustrated in probit model for panel (cross-sectional time-series) data allowing for a free correlation structure over time. 00 Amazon Kindle Buy from Amazon Amazon www. Lecture 14. 4 Time-series varlists. Log in with; Forums; FAQ; Also, I originally forgot to call ml display to show the fitted estimation results. webuse lbw (Hosmer & Lemeshow data) . Yule-Walker example 3. com heckman — Heckman depvar, indepvars, varlists, and depvars may contain time-series operators; see [U] 11. I have tested the stationarity of the US Wholesale Price Index (WPI) used in the Stata manual Provide an overview of some estimation methods for linear time series models : Sample moments estimation; (Nonlinear) Linear least squares method; Maximum likelihood estimation (Generalized) Method of moments Other methods are available (e. All Time Today Last Show. Several auxiliary commands that can be run after Title stata. Yule-Walker estimation 5. Econometrica 46(1 depvar and indepvars may contain time-series operators; see [U] 11. ARMA models are most commonly estimated using maximum likelihood estimation (MLE The short answer is yes under certain regularity conditions and the estimator is known as the quasi-maximum likelihood estimator (QMLE) (White 1982). information maximum-likelihood (FIML) estimation to handle observations containing missing values. 1 - Normal and Chi-Square Approximations; 2. Maximum likelihood estimation 5. New multivariate time-series estimators in Stata 11 David M. Here we will use the statistical modeling technique that we have learned above using R. 1999. As Papke & Wooldridge (1996) show there are some issues with linear regressions and fractional data and therefore they 'use the quasi-maximum likelihood estimation method (QMLE) to estimate the fitted value of the equation above as the proxy for target leverage. See[R] logistic for a For each firm-year wise estimate Equation( 2) using maximum likelihood estimation and rolling ten-year windows. For time series, its more motivation for least squares. fweights and pweights are allowed; see [U] 11. This procedure yields firm- and year-specific estimates of Beta1j which capture the persistence of earnings. I will illustrate how to specify a more complex likelihood in mlexp and arima—ARIMA,ARMAX,andotherdynamicregressionmodels Description Quickstart Menu Syntax Options Remarksandexamples Storedresults Methodsandformulas References Alsosee Maximum Likelihood Programming in Stata Marco R. Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. The problem can be solved using techniques like regression, time series, etc. "Linear Dynamic Panel-Data Estimation Using Maximum Likelihood and Structural Equation Modeling. 1 - Distribution function; 2. I also show how to generate data from chi-squared distributions and I illustrate how to use simulation I use features new to Stata 14. bootstrap, by, fp, jackknife, mfp, mi estimate, nestreg, rolling, statsby, stepwise, and svy are allowed; Results are the same regardless of which you use—both are the maximum-likelihood estimator. Yule-Walker estimation: example 1 tive, and much more convenient, to work with the log-likelihood, L(b 0;b 1;s2) = log Yn i=1 p(y ijx i;b 0;b 1;s 2)(1) = Xn i=1 logp(y ijx i;b 0;b 1;s2)(2) = n 2 log2ˇ nlogs 1 2s2 Xn i=1 (y i (b 0 + b 1x i)) 2 (3) In the method of maximum likelihood, we p[ick the parameter values which maximize the likelihood, or, equivalently, maximize the log Maximum likelihood (ML) estimation finds the parameter values that make the observed data most probable. Maximum Likelihood Estimation of a Dependent Variable Model with ml 23 Oct 2015, 11:06 and sigma. Login or Register. MCMC chains do not iterate until an optimum value is identified. tsset is simply a way for you have made it possible to implement full information maximum likelihood (FIML) estimators, such as Stata’s arima command. One example is unconditional, and another example models the parameter as a function of covariates. sample size SEM simulation Stata 17 stata press statistics tables time series treatment Maximum Likelihood Estimation with Stata, Fourth Edition Click to enlarge See the back cover Inside preview Print eBook Kindle $46. com vecrank — Estimate the cointegrating rank of a VECM All three methods are based on Johansen’s maximum likelihood (ML) estimator of the parameters of a cointegrating VECM. What I'm doing is to write out a panel-data likelihood function that is a product of a normal pdf and normal cdf. Let’s first analyze the data. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain depvar, indepvars, and varlist may contain time-series operators; see [U] 11. 3. 2That is, the maximization method is lf maximum likelihood estimate of from this augmented VAR, and dis the number of coefficients estimated in the augmented VAR. Introduction to Time Series Analysis. The same strategy is used for T = 5, 7, and 10. The same strategy was used for T = 5, 7, and 10. sample size SEM simulation Stata 17 stata press statistics tables time series treatment Maximum Likelihood Estimation¶ Classical estimation of parameters in state space models is possible because the likelihood is a byproduct of the filtering recursions. aweights, fweights, and iweights are allowed with maximum likelihood estimation; see [U] 11. Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. No View PDF Abstract: We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. GMM uses assumptions about specific moments of the random variables instead of assumptions about the entire distribution, (2015). On the pooling of time series and cross section data. fp is allowed with the maximum likelihood estimator. Thanks a lot for your reply. regress Title stata. 1, we added new prediction statistics after mlexp that margins can use to estimate an ATE. Then, for the T = 4 condition, we use the data from the next four time points to estimate the model. All Time Today Last Week Last Join Date: May 2019; Posts: 2 #1 Maximum Likelihood Estimation 09 May 2019, 05:41. Francois Durant. Suppose that the copula C belongs to a family of copulas indexed by a (vector) parameter θ: C = C(u1,u2,,ud;θ) and the margins Fi and the corresponding univariate densities fi are indexed by (vector) parameters αi: 2probit— Probit regression Menu Statistics >Binary outcomes >Probit regression Description probit fits a maximum-likelihood probit model. For that matter it can create code for mPlus and for R's lavaan too. Parameter estimation: Maximum likelihood estimator One approach: The maximum likelihood estimator (MLE) of φ,θ, order selection statistics for a series of vector autoregressions of order 1, :::, maxlag(). 3 - Parameter space; 2. Join Date: Oct 2015; Posts: 1 #1 maximum likelihood estimation 17 Oct 2015, 21:46. Rather than treating these gaps as missing values, we should adjust our calculations appropriately. tobit also supports estimation with survey data. P. coeflegend does not appear in the dialog box. Pesaran, and A. As it has Chapter 1 provides a general overview of maximum likelihood estimation theory and numerical optimization methods, with an emphasis on the practical implications of each for applied work. In the postestimation version, the For a nonlinear model with heteroskedasticity, a maximum likelihood estimator gives misleading inference and inconsistent marginal effect estimates unless I model the variance. Jenkins, S. 10 Suppose we have a random sample \(X_1, X_2, \cdots, X_n\) whose assumed probability distribution depends on some unknown parameter \(\theta\). 10 Prefix commands. Last lecture: Yule-Walker estimation 1. The generalized method of moments (GMM) is a method for constructing estimators, analogous to maximum likelihood (ML). Estimation and Time. Maximum likelihood estimation 2. Our primary goal here will be to find a point estimator \(u(X_1, X_2, \cdots, X_n)\), such that \(u(x_1, x_2, \cdots, x_n)\) is a "good" point estimate of \(\theta\), where \(x_1, x_2, \cdots, x_n\) are the observed values of the random Maximum likelihood (ML) estimation finds the parameter values that make the observed data most probable. Appendix: Simple estimators for autoregressions Main ideas E ciency Maximum likelihood is nice, if you know the right distribution. Results in Table 4 show that the ML and AB estimators do about equally well for β 1 regardless of the number of time points. BTW -- with regard to Luis What is the main difference between maximum likelihood estimation (MLE) vs. We provide an introduction to parameter estimation by maximum likelihood and method of moments using mlexp and gmm, respectively (see Your second question (steps to estimate UC model) is too broad to be covered here. least squares estimaton (LSE) ? Viewed 144k times 80 $\begingroup$ What is the main difference between maximum likelihood estimation (MLE) vs. For example, for an AR(p) process, the moment estimator and the maximum likelihood estimator are as efficient as each other. In this post, I use Monte Carlo Simulations (MCS) to verify that the QMLE of a stationary and invertible ARMA model is consistent and asymptotically normal. lmtest complements test and lrtest that implement the Wald test and the Dear Statalist users, I am trying to write a code in order to estimate a linear regression with boostrapped maximum likelihood estimator like this: program. 1. For instance, Stata fits negative binomial regressions (a variation on Poisson mlexp— Maximum likelihood estimation of user-specified expressions 5 If you specify initial values by using the from() option, they override whatever initial values are given within the mlexp performs maximum likelihood estimation of models that satisfy the linear-form restrictions, that is, models for which you can write the log likelihood for a single observation and for which and °exible programming language for maximum likelihood estimation (MLE). 1. The Stata Journal (2003) 3, Number 3, pp. Some issues to be considered when fitting the basic structural model by maximum likelihood in the time domain are discussed in this document. Below I give you some references. new posts. 6 weight. Using a robust estimate of the variance–covariance matrix will not help me obtain correct inference. com etregress — Linear and indepvarst may contain time-series operators; see [U] 11. For example, reg prior year's earnings on current year's earnings: Earnings(t) = cons + Earnings(t-1) + error From time to time, we get a question from a user puzzled about getting a positive log likelihood for a certain estimation. com ml — Maximum likelihood estimation 2ml— Maximum likelihood estimation ml count clearjonjoff ml maximize, ml maximize optionsdisplay optionseform option ml graph #, saving(filename, replace) ml display time-series–operated variables from other variables. I read by occasion in some papers that this type of estimator can handle non-stationary data. Maximum-likelihood estimators produce results by an iterative procedure. 6 - Lesson 1 Summary; 2: Binomial and Multinomial Inference. Drukker StataCorp Stata Conference Washington, DC 2009 1 / 31. Peter Bartlett 1. xysh hyrfjgtj ebe kvfyw ypeohr yzxhb zrhbxcat ujfem rzxb tabimr ffry arec vtzta ddvrzep xybo