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Who can run ARCH/GARCH in STATA for me?

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I am a certified data analyst with 6 years of experience in statistical research and analysis. I have worked with statistical packages such as SAS, SPSS, STATA, EViews, Minitab, and R, and can run STATA for you on ARCH and GARCH models. Sure, here’s how I will run ARCH/GARCH in STATA: For a given dataset, you should prepare the following structure: 1. First, generate an ARCH matrix for the dependent variable. This matrix represents

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GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is a statistical model for heteroskedastic time-series, such as financial and business time series. It’s also called time-varying cointegration model because the relationship between two series can vary over time. However, running ARCH in Stata isn’t very simple, especially when there are no standard functions for this. Most Stata users write ARCH(model) command by themselves. However, a professional can run ARCH in Stata using the built-

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The STATA is a widely used statistical software in the research field. The popular ARCH/GARCH models are ARCH (autoregressive with cross-sectional dependence) and GARCH (generally accepted return model) which have wide applications in different fields. ARCH: ARCH models are used in the time-series analysis of long-term series. ARCH model has a different distribution compared to traditional linear models. It does not have a constant error term (constant intercept). The residuals for an ARCH model can be used for forecasting

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Today, I want to write about a question that comes to my mind frequently when I am faced with data analysis for statistics problems. This is the question of who can run ARCH/GARCH in STATA. I have written about ARCH in multiple posts in the past, as you can find at [my blog](https://pavanrajab.github.io). The process of finding ARCH/GARCH in STATA is quite simple. Step 1: Identify the error or discrepancy Firstly, we

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Can someone do GDP & CPI time-series modeling?

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GDP (Gross Domestic Product) and CPI (Consumer Price Index) are two economic metrics that are vital for many countries. In my country, for instance, we have both GDP and CPI data. I have been studying the relationship between GDP and CPI for years. In 2014, I wrote a paper on the topic which is one of my major contributions to the field. The paper, titled “Inflation and GDP: an Intercorrelation Analysis,” appeared in the Journal of Economic Studies (volume 36,

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A statistical software called Stata comes with many statistical functions that allow you to perform time-series analysis. The following is an example of a time-series analysis in STATA. The data set includes daily time series of the stock prices for 20 stocks in 2005. The original data is available on www.sec.gov. We will first clean the data and resample the data to monthly and annual frequency. The following are the steps to clean and resample data. 1. Clean data: First, we clean the data by

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A quick example demonstrates the feasibility of creating time-series datasets in the SPSS program using the “tseries” command. I then used it to create a time-series dataset with 100 points spanning 5 years. It took about 30 minutes to complete. The dataset contains 100 points in the y variable, the first 25 are for the period 2012-2017, and the last 75 are for the period 2013-2017.

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ACF (autocorrelation function) and PACF (partial autocorrelation function) are some useful tools for statistical analysis of time series data. The idea behind these functions is to examine the underlying trend and the variation over time. It is common to plot these functions as smooth lines in the form of plots called Autocorrelations/Partial Autocorrelations Functions (ACF, PACF). The ACF plots display the long-term, long-term autocorrelations and the PACF plots display the recent past tr

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ACF and PACF are abbreviations for Auto-Correlation Function and Partial Autocorrelation Function, respectively. These plots display the statistical properties of time-series data, with their respective axes representing a lag and a follow-up period. This tutorial will explain these plots in more detail, from the technical terms to how to interpret them. ACF (Auto-Correlation Function) ACF plots provide information about the relationship between the series over different time intervals, or lagged values. The correlation of an observed time-series with itself over lagged

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ACF/PACF plots are quite popular among econometrics students and practitioners. You can use it to interpret and identify trends in time series data. I have personally used them in various research projects. The plot consists of two boxes: a box with data points representing observations and a box with estimated regression line(s). The axes in ACF plot go from left to right and from bottom to top, while the axes in PACF plot go from top to bottom and from left to right. Now I will describe in details how ACF/PAC

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Can someone explain ACF/PACF plots? Spoke like this with a sense of delight. ACF/PACF is an acronym used in technical analysis for calculating the autocorrelation and partial autocorrelation functions (ACF and PACF) for price series. If we look at the first 10 observations, we have a linear relationship. The price will increase by 1 unit in the future if the price was at the current price at the first 10 observation. And it will decrease if the price decreases by

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ACF/PACF are two very important time series analysis tools that you can use when modeling time series. ACF/PACF is a pair of plots used to display relationships between two or more time series. see this In other words, ACF/PACF plots can help identify the relationship between the two series you are analyzing. When applied correctly, ACF/PACF plots can provide valuable information about the strength and direction of the relationship. Here’s what you should know about ACF/PACF and how to make them: ACF

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ACF/PACF plots are a powerful tool for analyzing time-varying dependent variables. They are similar to regression plots but differ in several ways. Unlike regression plots, which describe the relationship between the dependent and independent variables, ACF plots show the statistical relationship between a time series and one or more lagged (preceding) values of the dependent variable. PACF plots, on the other hand, show the statistical relationship between a time series and one or more lagged (preceding) values of the lagged dependent variable. ACF

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ACF stands for auto-correlations, which is a fundamental tool to investigate the linear dynamics of a statistical time series. PACF, on the other hand, stands for partial autocorrelations, which is an extension of ACF that analyzes the nonlinear dynamics of a time series. ACF plots are visual representation of correlations. It shows the correlation between two successive values of time series. ACF plots also show the strength of the correlation. You can observe two lines drawn horizontally in the plot. One line is called the autoc

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