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Your assumption is correct. Value added is Gross Output-intermediate consumption(inputs). value-added approach is a simple measure that ignores the difficulties of dealing with inter-industry and intra-industry flows of goods and services. Intermediate inputs are simply excluded here. The value-added approach provides a simple link of industry-level MFP ...


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I would like to add on to the previous post from 1muflon1. Total agree with the post and I will try not to repeat anything said but I feel there is some additional information that is worth mentioning about both R and Python. When it comes to loops Python is faster than R, when the number of iterations is less than 1000. Below 100 steps, python is up ...


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If you already know how to code in MATLAB then python is more similar to it than R so I would say you will have easier time to transitioning there. Otherwise, both R and Python are programming languages so you will be able to do all those things in both of them as you can always program your own functions. The strength of R is that the language has a ...


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You can use the dplyr::distinct function to keep only unique values, at a particular level of aggregation. So, for example, to only keep unique combinations of country and year in df, you can use library(dplyr) new_df <- distinct(df, country, year) df should be in long format, though.


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Here are some good guides on this topic: Bayesian Inference 2019 from Ville Hyvönen & Topias Tolonen. - this is good guide to Bayesian inference in general. They have separate chapter on hierarchical Bayes and all their explanations include worked out step by step examples in R. Bayesian Data Analysis by Gelman, Carlin, Stern, Dunson, Vehtari, and ...


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I would advise you to think deeper about your research question first, as this will guide the decision to use country fixed effects. If you would like to exploit cross country variation, for example by studying how the same industry functions differently across countries, then do not use country dummies because it will absorb the variation you want. If you ...


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The price elasticity of demand is defined as: $$E_P=\frac{dQ}{dP} \frac{P}{Q}$$ Although generally elasticity depends on price there is a special type of functions (isoelastic functions) for which elasticity remains the same along the whole function. For example consider demand given by: $$P=AQ^{1/e}$$ This demand function will always have the same ...


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Note that the "cost of home ownership" depends not only on the house and the market, but also the individual. For someone who moves every year, home ownership is generally not a good option - that person incurs closing costs every year, and most of their mortgage payments at the start of the loan go to interest, rather than building a meaningful amount of ...


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A rent requires a payment every period of time but it's not charged by the interest rate required in a mortgage because you aren't asking for a loan , and also if you rent then some of the costs are paid by the owner ( renovation, for example) . If you buy a house you will be able to sell it again at a certain price when you have finished to pay for it, but ...


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Based on the clarification in your comment this is not because you use scale variables but because the scale variable that you use is self-reported. It is well known in literature that self-reporting leads to endogeneity. See for example: Lindeboom, M., & Kerkhofs, M. (2004). Subjective health measures, reporting errors and endogeneity in the ...


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A dummy variable is a binary variable, that does need to be logged. You can use them as they are. Also, if the variable is set to 1 for a given result and zero otherwise, by taking logs you will turn your variable to 0 and a missing value because the log of 0 does not exist. update: Instead of a plot, why not simply calculate pairwise correlation ...


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Hi: Here's an attempt at a heuristic solution. Dividing both sides by $(1 - \rho L)z_{t}$ ( using $\rho$ instead of B and $\epsilon_t$ instead of $u_{t}$ because that notation is easier for me ), gives $z_{t} = \frac{a}{1-\rho L} + \frac{\epsilon_t}{1 - \rho L} + \frac{\epsilon_{t-1}}{ 1 - \rho L} $ = $\frac{a}{1-\rho L} + \sum_{i=0}^{\infty} \rho^{i} \...


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This site https://www.statisticshowto.com/granger-causality/ shows about conducting the F-test for granger-causality. It also talks about alternative Tests. "If you have a large number of variables and lags, your F-test can lose power. An alternative would be to run a chi-square test, constructed with likelihood ratio or Wald tests. Although both ...


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As I understand, the Ramsey RESET test ... is not actually a general test for omitted variable bias. Rather, it is a test for misspecification. Specifically, if the model is properly specified, "no nonlinear functions of the independent variables should be significant when added to the estimated equation" This is completely correct, the idea of the RESET ...


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The easy way to see the seasonal adjustment factor for many series is to divide the non-seasonally adjusted series by the seasonally adjusted version. This assumes that the seasonal adjustment factor is multiplicative. Different series have different patterns.


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There is a lot of literature on this question. This review looks like a good place to start although it is from 2013. It looks at a wide range of both qualitative and quantitative approaches. Among them, the most frequently used technique is the time series econometrics in order to forecast the volatility of oil prices, the second frequently used is ...


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