Vector autoregression stata interpretation. ty. Structural analysis begins with the structural vector autoregression (SVAR). Handle all the statistical challenges inherent to time-series data—autocorrelations, common factors, autoregressive conditional heteroskedasticity, unit roots, cointegration, and much more. VAR is a type of stochastic process model. Economic theory typically motivates the constraints, allowing a causal interpretation of the IRFs to be made. Princeton, NJ: Princeton Jul 17, 2012 · Johannes. dta. VAR models differ from univariate autoregressive models because they allow feedback to occur between the variables in the model. However, in the ARDL framework, the outcome variable Dec 27, 2018 · 1. Title stata. Apr 15, 2021 · The vector autoregressive (VAR) model is a workhouse multivariate time series model that relates current observations of a variable with past observations of itself and past observations of other variables in the system. Although there are standard formulas for all Oct 21, 2021 · The quantile autoregressive distributed lag (QARDL) model combines two important time series analysis tools, quantile regression and autoregressive distributed lag (ARDL) models. When we have only one lagged value of each variable on the right-hand side, it would be a VAR (1) model; if two-lagged terms, it would be a VAR (2) model; and so on. While programs specifically designed to fit time-series VAR models are often included as standard features in most statistical packages, panel VAR model estimation and inference are often implemented with general-use routines that require some Nov 16, 2022 · See all features. VAR models (vector autoregressive models) are used for multivariate time series. trivial. Nov 27, 2014 · 1. Next, perform the Granger processes display a type of long-run dependence. Bayesian vector autoregression MCMC iterations = 12,500 Gibbs sampling Burn-in = 2,500 MCMC sample size = 10,000 Sample: 1956q3 thru 2005q1 Number of obs = 195 Acceptance rate = 1 Efficiency: min = . 2005. Combining key statistical concepts with detailed technical advice, Software for Bayesian estimation of structural vector autoregressions. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. t+1, only depend on the last Lvalues of the vector of observables but are otherwise independent of time. SVARs make explicit identifying assumptions to isolate estimates of policy and/or private agents’ behavior and its effects on the economy while 6var— Vector autoregressive models The output has two parts: a header and the standard Stata output table for the coefficients, standard errors, and confidence intervals. That is, the current unemployment rate would be modeled using unemployment and inflation rates at previous times. To understand what this means, let us first look at a simple univariate (i. Such Markov models are called dynamic models. svar fits a vector autoregressive model subject to short- or long-run constraints you place on the resulting impulse–response functions (IRFs). Schneider ardl: Stata module to estimate autoregressive distributed lag models 18/20. That is, the relationship between the time series involved is bi-directional. Vector autoregressive (VAR) model. It has p lagged values of each variable on the right-hand side. Panel vector autoregression (VAR) models have been increasingly used in applied research. Alternatively, use the following STATA command for VAR: VAR pfcegfcfgdp, lags(1/2) The below results will appear. com varstable — Check the stability condition of VAR or SVAR estimates DescriptionQuick startMenuSyntax OptionsRemarks and examplesStored resultsMethods and formulas ReferencesAlso see Description varstable checks the eigenvalue stability condition after estimating the parameters of a vector autoregression using var or svar. The VAR models become popular for economic analysis when Sims (1980) advocated them as Traditional approaches to structural vector autoregressions can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. If y t is a K 1 vector of I(1) variables and there exists a vector , such that y t is a vector of I(0) variables, then y t is said to be cointegrating of order (1,0) with cointegrating vector . Nov 7, 2017 · Hey guys, I've been trying for a while to replicate the vector autoregression analysis that I used to do in Stata. While programs specifically designed to fit time-series VAR models are often included as Jan 14, 2020 · Whereas, Vector Auto Regression (VAR) is bi-directional. where. Vector Autoregressions • VAR: Vector AutoRegression – Nothing to do with VaR: Value at Risk (finance) • Multivariate autoregression • Multiple equation model for joint determination of two or more variables • One of the most commonly used models for applied macroeconometric analysis and 2var intro— Introduction to vector autoregressive models Remarks and examples stata. Stata: Data Analysis and Statistical Software . Firstly, the term ‘auto-regression’ is used due to the appearance of the lagged value of dependent variables on the right side. 1 ARCH/GARCH Models ›. However, there are many cases where the reverse should also be allowed for — where all variables affect each other. 1 ARCH/GARCH Models. Here, we covers all aspects of panel Sep 1, 2016 · The pvar package of programs, a set of programs to conveniently execute the model selection, estimation, and inference of homogeneous panel VAR models in a generalized method of moments framework, is presented. VAR models are generally defined as alternatives to structural Oct 27, 2016 · Introduction. We use this Oct 16, 2018 · Choose the dependent variables in the box, i. 1993. In order to do so, we will Oct 17, 2022 · This is a Research HUB original playlist on Panel Data Models featuring Dr. It is a natural extension of the univariate autoregressive model to dynamic mul-tivariate time series. The application of Bayesian VAR models, however, has been more limited, mostly because of the difficulties in Jul 7, 2019 · Vector Autoregression (VAR) is a forecasting algorithm that can be used when two or more time series influence each other. Markov-switching models are not limited to two regimes, although two-regime models are common. Estimation of panel vector autoregression in Stata: A package of programs Michael R. dev. As we discuss below, identification in the multivariate case is more involved. inference, interpretation, and specification testing in linear regression models. normal over The MLE is a general way of coming up with an estimator: equivalent to least squares in the temporal case, but better in the spatial case. first null hypothesis = no cointegrating relationship. Next to a set of intercept parameters, the VAR(1) model is comprised by autoregressive effects, which indicate how much a variable is predicted by Bayesian VAR models. A choice had to be made about the computer package that would be used to perform the quantitative work and EViews was eventually selected because of its popularity among IMF sta and central bankers more generally. Abrigo*1 and Inessa Love2 (February 2015) 1. (1988), is a new model based on panel data that adapted from the VAR model. VAR is a regression model that can be considered as a kind of hybrid among univariate time series models. As Christoph Hanck correctly notes, the other equations of the model do not affect its estimation if you do equation-by-equation OLS (and that is a preferred method for an unrestricted VAR). Also see [TS] varbasic postestimation — Postestimation tools for varbasic [TS] tsset — Declare data to be time-series data [TS] var — Vector autoregressive models [TS] var intro — Introduction to vector autoregressive models [TS] var svar — Structural vector Nov 13, 2019 · Modeling Time-series Stochastic Data. Section2. The sequence ΔXt. Individual economies in the global economy are interlinked through many different channels in a complex way. Let Ω = E(wtw′ t) be the observed variance covariance matrix. Things are easiest in the Gaussian setting, but Gibbs sampling and coding can be used with non Aug 31, 2018 · Univariate autoregression. It provides an interpretation of historical fluctuations in the modelled time series through the lens of the identified structural shocks. Figure 3: Lag selection for VAR in STATA. 2. (see ?irf) imp<-irf(YOUR_MODEL) plot(imp) by the way: Maybe you cant estimate a valid model with such a low number of observations. It starts by de ning the model in section2. irf— Create and analyze IRFs, dynamic-multiplier functions, and FEVDs 3 To analyze IRFs and FEVDs in Stata, you first fit a model, then use irf create to estimate the IRFs and FEVDs and save them in a file, and finally use irf graph or any of the other irf analysis Aug 13, 2021 · by Yugesh Verma. Vector autoregression ( VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. In the self-exciting threshold model, the lagged dependent variable is used as the threshold variable. This parameterization nests the autoregressive moving-average (ARMA) model, which is widely used for short-memory processes. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. “A Vector autoregressive (VAR) model is useful when one is interested in predicting multiple Oct 1, 2016 · I am using the vecrank command in STATA to identify the number of cointegrating relationships for 2 dependent price series (a and b). See[TS] var intro for a list of commands that are used in conjunction with svar. With probability 0. Time Series Analysis. 32529 + . 3) for an introduction to linear regression using Stata. The normal way to interpret a VAR Model would be to calculate the impulse response functions and plot them. Quick Objectives. 83 2 5. . Simultaneously model multiple variables in terms of past lags of themselves and one another. In the first-order Vector Autoregressive (VAR(1)) model, each variable at time point t is predicted by all variables (including itself) at time point t − 1. This paper briefly discusses model selection, estimation and inference of homogeneous panel VAR models in a generalized method of moments (GMM) framework, and presents a set of Stata programs to conveniently execute them. Sep 26, 2018 · In multivariate time series, the prominent method of regression analysis is Vector Auto-Regression (VAR). The VAR model is advantageous because it can explain past and causal relationships among multiple variables over time, as well as predict future observations. 0498443*socst+. Oct 23, 2018 · 3. New Introduction to Multiple Time Series Analysis. Handle: RePEc:boc:bocode:s457944 Note: This module should be installed from within Stata by typing "ssc install xtvar". Thu, 26 Jan 2012 12:26:49 -0500. Identify and interpret ARCH models. The suite includes several commands for estimating and interpreting impulse–response functions (IRFs), dynamic-multiplier functions, and forecast Aug 2, 2021 · The first step to understanding restrictions in structural vector autoregressive (SVAR) models is understanding the differences and links between the SVAR model and reduced form VAR model. If we let Φ ( B) = 1 − ϕ 1 B − ϕ 2 B 2 − ⋯, then we can write the AR model for the errors as. 9969 Log marginal-likelihood = -684. ¨ Introduction to Multiple Time Series Analysis. A two-variable VAR with one lag looks like \begin{align} Nov 17, 2021 · If you are only interested in one dependent variable, you may look at its equation alone. "XTVAR: Stata module to compute panel vector autoregression," Statistical Software Components S457944, Boston College Department of Economics, revised 02 Apr 2015. . 2nd ed. dta spshape2dta also linked the two files. See full list on medium. Compared with a univariate autoregression, VARs The linear VAR in (1) con- tains a built-in notion of time invariance: conditional forecasts as of time t, such as E. 3893102*math + -2. By using menu in Stata we can perform estimation of a VAR model by following the sequence. Secondly, the term ‘vector’ refers to dealing with A simple linear regression model with autoregressive errors can be written as. Vector autoregressive (VAR) models have played an important role in macroeconomic analysis since Sims (1980). The header contains summary statistics for each equation in the VAR and statistics used in selecting the lag order of the VAR. Also see [TS] var — Vector autoregressive models [TS] var intro — Introduction to vector autoregressive models [TS] var svar — Structural vector autoregressive models [TS] varbasic — Fit a simple VAR and graph regression models include the threshold autoregression model and self-exciting threshold model. The structure is that each variable is a linear function of past lags of itself and past lags of the other variables. Vector autoregressive models (VARs) have been widely used in macroeconomics to summarize data interdependencies, test generic theories, and conduct policy analyses (Canova 2007). Vector autoregressions are flexible statistical Structural Vector Autoregressions (SVARs hereafter) are a multivariate, linear represen-tation of a vector of observables on its own lags and (possibly) other variables as a trend or a constant. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. i. Oct 3, 2019 · Let's take a look at the basics of the vector auto regression model in time series analysis!---Like, Subscribe, and Hit that Bell to get all the latest video May 12, 2017 · Estimating VAR model with Stata (time series) Vector autoregressive (VAR) models have a long tradition as tools for multiple time series analysis (Quenouille, 1957). The autoregressive fractionally integrated moving-average (ARFIMA) model provides a parsimonious parameterization of long-memory processes. In previous posts, I have identified the parameters of a structural VAR model by imposing restrictions on how Vector Autoregressive Models for Multivariate Time Series 11. Let’s understand this be one example. Probably more informative to look at the impulse response function of the VAR. 4) for a more advanced discussion along the same lines. This process feeds forward indefinitely. All variables in a VAR system are Mar 23, 2016 · An IRF indicates what is the impact of an upward unanticipated one-unit change in the "impulse" variable on the "response" variable over the next several periods (typically 10). We Nov 16, 2022 · In the spotlight: Bayesian vector autoregressive models. Software for alternative to Hodrick-Prescott Filter. Is anyone aware of a template or a video that will teach me to write a VAR? I tried looking at documentation but for some reason its hard for me to apply what I read. These include sharing scarce resources (such as oil and other commodities), political and technological developments, labour and capital movements across countries, cross-border trade in financial assets as well as trade in goods and services. 68843 max = 1 Schenck (Stata) Bayesian Econometrics August 25, 2021 16 / 63 May 6, 2021 · More valuable insights come from structural analysis. Share. 9556 avg = . dta The other is a Stata dataset containing the other data that were in the shapefile bundle: tl_2016_us_county. Structural Form VAR III 1. The estimation of ARDL / EC models has become increasingly popular over the last decades. M. Dec 14, 2023 · A VECM models the difference of a vector of time series by imposing structure that is implied by the assumed number of stochastic trends. From graphing and filtering to fitting complex multivariate models, let Stata reveal the structure in your time-series data. Introduction Time-series vector autoregression (VAR) models originated in the macroeconometrics literature as an alternative to multivariate simultaneous equation models (Sims, 1980). In this post, we will see the concepts, intuition behind VAR models and see a comprehensive and correct method to train and forecast VAR … Vector Autoregression (VAR) – Comprehensive Guide In this article, we briefly discuss model selection, estimation, and inference of homogeneous panel VAR models in a generalized method of moments framework, and we present a set of programs to conveniently execute them. Lutkepohl, H. com 11. I want to do the following: Identifying supply (inflation) and demand (gdp) shocks. gdp, gfcf, pfce. Expressed in terms of the variables used in this example, the regression equation is. Notice: On April 23, 2014, st: bootstrap vector autoregression (var)? Date Fri, 27 Jan 2012 15:45:45 +0000: Apr 2, 2024 · Estimation Of Panel Vector Autoregression In Stata A Applied Statistics Using Stata 2022-04-26 Mehmet Mehmetoglu Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. After successfully completing this lesson, you should be able to: Model the variance of a time series. t has mean 0, has covariance matrix , and is i. That is, the variables influence each other. The goal of structural VAR analysis is to obtain B, which is not Dec 23, 2023 · Among them, the panel vector autoregression (PVAR) model, proposed by Holtz-Eakin et al. The original regressions as you specified them have the coefficients. We have our own analysis data for these counties in texas_ue. So, -var- cannot operate as when it tries to operate it will find some times repeated, which Title stata. 2) and discusses the implications of the law of iterated quantiles (section 2. 009765*female+. V ECTOR auto-regressive (VAR) integrated model comprises multiple time series and is quite a useful tool for forecasting. ) Then in t + 2, the changes to Xt, Yt, Xt + 1, and Yt + 1 will affect the values in t + 2. The reduced form VAR model considers each variable to be a function of: Its own past values. 3352998*read. The proliferation of nonlinear VAR models naturally leads to extending the historical decomposition into nonlinear settings. interval] dln_inv dln_inv Oct 26, 2023 · A quantile vector autoregressive (VAR) model, unlike standard VAR, traces the interaction among the endogenous random variables at any quantile. While programs specifically designed to estimate time-series VAR models are often In t + 1, the changes to Xt and Yt will affect Xt + 1 and Yt. SeeWooldridge(2010, chap. However the residual diagnostics fail when looking at that equation. In the example above, we described the switching as being abrupt; the probability instantly changed. Step 2: Prepare the data for analysis. answer by a prof from my department (University of Leicester): "Yes you can interpret the coefficients in exactly the same way". X1, βY1, γX1, and γY1. The bootstrap methods for dependent data, such as VAR, are not. 1995. As an example suppose that we measure three different time series variables, denoted by \ (x_ {t,1}\), \ (x_ {t,2}\), and \ (x_ {t,3}\). In the threshold autoregression model, proposed byTong(1983), the dependent variable is a function of its own lags; seeTong(1990) for details. If rejected, first null hypothesis = 1 cointegrating relationship, etc. Oxford: Oxford University Press. Re: st: bootstrap vector autoregression (var)? Date. RE: st: bootstrap vector autoregression (var)? The original -tsset- is not the issue here. Although there are standard formulas for all necessary identification restriction on the cointegrating vector. More strongly, the conditional one-step-ahead variance is fully independent of time: E. VAR models are different from univariate autoregressive models because they allow analysis and make predictions on multivariate time Description. Jan 11, 2013 at 12:01. 4introduces the concept of quantile impulse response func-tions. The autoregressive distributed lag model uses two components to explain the behavior of a dependent variable: Sep 6, 2019 · In Stata, the command estimates a vector autoregressive model. Sep 1, 2016 · For the analysis, the newly developed panel vector autoregression (PVAR) in the generalised method of moments (GMM) estimation approach was applied. Jul 30, 2012 at 13:06. A line graph showing supply Jan 23, 2021 · What is the var model? In this video, I show you How to estimate and interpret VAR models in Eviews - Vector Autoregression model. 75, the processes revert from state 2 to state 1 in the next time period. One limitation of the models that we have considered so far is that they impose a unidirectional relationship — the forecast variable is influenced by the predictor variables, but not vice versa. Tobias Cagala & Ulrich Glogowsky, 2014. Then by chance some observations will be selected twice, or even more; and some not at all. 9962 Log marginal-likelihood = 467. contiguous blocks (say with four lags, just to be on the safe side in. VAR model involves multiple independent variables and therefore has more than one equations. Software to reproduce examples from the book Time Series Analysis. It allows for the presence of unobservable individual heterogeneity and time effects. A VAR is a multiequation, multivariable linear model where each variable is in turn explained by its own lagged values as well as current and past values of the remaining variables. VECM is used to specify and estimate these models. 7) andCameron and Trivedi(2022, chap. BVAR differs with standard VAR models in that the model parameters are treated as random variables, with prior probabilities, rather than fixed values. In structural analysis, we apply the VAR relationship to understand the dynamic relationship between the variables in our model. Mar 17, 2017 · Based on the criteria (lowest AIC, lowest BIC, etc. Stephen Zamore from the University of Agder. Select the number of lags, i. Also see [TS] var — Vector autoregressive models [TS] var svar — Structural vector autoregressive models Nov 16, 2022 · Time series. It follows that BB′ = Ω 2. IRFs do not have coefficients. There are two similar, but di erent formulations of spatial autoregression: simultaneous and conditional. SeeHamilton(2013, chap. C. Keywords: st0455, pvar, pvarfevd, pvargranger Nov 18, 2018 · Stata commands used to specify and estimate a Vector Auto-regression model and generate Impulse Response Functions given a specific Cholesky Ordering. New York: Springer. 75286 max = 1 Equal-tailed Mean Std. Our findings suggest a long-run equilibrium focuses on that case. – Johannes. They show you how one variable reacts when a shock hits the system. In statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. 11. The vector autoregression (VAR) model is one of the most commonly employed multivariate regression time series analytic techniques. VAR stands for vector autoregression. with ϵ t = ϕ 1 ϵ t − 1 + ϕ 2 ϵ t − 2 + ⋯ + w t, and w t ∼ iid N ( 0, σ 2). S. To illustrate the basic VAR model in Stata, suppose that we are estimating the relationship between aggregate consumption (ln_agg_cons) and income (ln_gdp_mkt) by using National Accounts Nov 16, 2022 · Incorporating more than six chapters of new material, New Introduction to Multiple Time Series Analysis also provides extensive coverage of the vector error-correction model (VECM) for cointegrated processes, structural VARs, structural VECMs, cointegrated VARMA processes, and multivariate models for conditionally heteroskedastic processes 2 Quantile vector autoregression This section introduces and studies the properties of QVAR. Then click on ‘OK’. One is a Stata-format shapefile: tl_2016_us_county_shp. MCSE Median [95% cred. sciencePredicted = 12. Δ y t = Π y t − 1 + Γ 1 Δ y t − 1 + … + Γ k a r − 1 Δ y t − k a r + 1 + u t. only one dependent or endogenous variable) autoregressive (AR) model of the form yt = a1yt−1 +et y t = a 1 y t − 1 + e t. Apr 23, 2020 · 2. 2 Vector Autoregressive models VAR (p) models. according to the coefficients β. Vector autoregressive (VAR) models study relationships between multiple time series, such as unemployment and inflation rates, by including lags of outcome variables as model predictors. com Remarks are presented under the following headings: Introduction to VARs Introduction to SVARs Short-run SVAR models Long-run restrictions IRFs and FEVDs Introduction to VARs A VAR is a model in which K variables are specified as linear functions of p of Subject. Dohoo, Martin, and Stryhn(2012,2010) discuss linear regression Bayesian vector autoregression MCMC iterations = 12,500 Gibbs sampling Burn-in = 2,500 MCMC sample size = 10,000 Sample: 1961q2 thru 1978q4 Number of obs = 71 Acceptance rate = 1 Efficiency: min = . 1, it shows how to construct forecasts (section 2. (We assume that all u terms are zero after t. Sep 1, 2016 · Abstract. 1. e. Framework. The past values of other variables in the model. Introduction. Other data and programs The historical decomposition is standard within the vector autogression (VAR) toolkit. 1. Dec 21, 2023 · Compared with a system-based Johansen (1995) cointegration analysis, which is implemented in Stata’s vec command suite, the single-equation approach can be more efficient if the focus is on one outcome variable, in addition to the aforementioned flexibility regarding the integration orders. From such a model you will not be able to obtain impulse response functions, while variance decomposition will be trivial (none of the variables explains the variance). d. com varwle Same as above, but use vector autoregressive (VAR), structural autoregressive (SVAR), Time Series Analysis. Imagine the first bootstrap sample, which is taken with replacement. Figure 4: Results of VAR in STATA. Vector autoregression (VAR) is a statistical model for multivariate time series analysis, especially in a time series where the variables have a relationship that affects each other to time. Summary: the new ardl package for Stata. Vector autoregressions. Feb 18, 2021 · The aim of this study is to explain vector autoregressive (VAR) models and Granger causality. Software for Markov-switching models. ). 3). VAR is an econometric model that generalizes univariate autoregressive (AR) models. Johansen, S. 2. Stata has a suite of commands for fitting, forecasting, interpreting, and performing inference on vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models. Kripfganz and D. A VECM ( k a r − 1) has the following form. 9655 avg = . The Johansen trace test uses a recursive method (i. Consider a vector autoregressive (VAR) model with plags y t = v + A 1y t 1 + A 2y t 2 + + A py t p + t (7) where y t is a K 1 vector of variables, v is a K 1 vector of parameters, A 1–A p are K K matrices of parameters, and t is a K 1 vector of disturbances. Φ ( B) ϵ t = w t. Federal funds rate and monetary policy Affine term structure and commodity futures models. As an example suppose that we measure three different time series variables, denoted by x t, 1, x 6var— Vector autoregressive models The output has two parts: a header and the standard Stata output table for the coefficients, standard errors, and confidence intervals. Princeton: Princeton University Press. In general univariate forecasting algorithms (AR, ARMA, ARIMA), we predict only one time-dependent variable. Here ‘Money’ is dependent on time. The bivariate VAR system given above is known as a VAR ( p) model. It is important to understand VAR for more clarity. Aug 25, 2022 · Vector AutoRegressive (VAR) is a multivariate forecasting algorithm that is used when two or more time series influence each other. 4. Introduction ARDL model Bounds testing Stata syntax Example Conclusion. In this blog post, I describe Stata’s capabilities for estimating and analyzing vector autoregression (VAR) models with long-run restrictions by replicating some of the results of Blanchard and Quah (1989). These estimates tell you about the relationship between the independent variables and the dependent variable. When one analyzes multiple time series, the natural extension to the autoregressive model is the vector autoregression, or VAR, in which a vector of variables is modeled as depending on their own lags and on the lags of every other variable in the vector. case your model did not specify enough lags) of Vector autoregression. ARDL Models. Being linear model, they are relatively easy to work with both in theory and practice. ), zero lag is preferred, which means a model with just an intercept but no lags. It can be considered an extension of the auto-regressive (AR part of ARIMA) model. We illustrate the pvar package of programs by using standard Stata datasets. t[(y. y t = β 0 + β 1 x t + ϵ t. The IRFs has three main outputs: the expected level of the shock in the analysis of quantitative macroeconomic systems with the Structural Vector Autoregression approach. Typically, block bootstrap schemes are used where short. 1 Introduction The vector autoregression (VAR) model is one of the most successful, flexi-ble, and easy to use models for the analysis of multivariate time series. – Jase. Quantile forecasts are obtained by factorizing the joint distribution in a recursive structure but cannot be obtained from reduced form estimation.
qo zl nk jz vg ak ni he ph ya