# Tag Info

## Hot answers tagged forecasting

4

Did William Nordhaus exclude 87% of economic industries from his climate change analysis? No, Nordhaus did not do that but Nordhaus did exclude $\approx 87\%$ economic activity as measured by national income and Keen either confuses economic activity with industry or does not use common terminology in economics (or given that he is known for sensationalism ...

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One example is this UK Government analysis: "Britain’s economy would be tipped into a year-long recession, with at least 500,000 jobs lost and GDP around 3.6% lower, following a vote to leave the EU, new Treasury analysis launched today by the Prime Minister and Chancellor shows…Speaking at B&Q in Eastleigh, Hampshire, the Prime Minister and ...

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For forecasting with VAR in R there are some good tutorials on econometrics with R. This tutorial from Justin Eloriaga also helped me when we had to make VAR for our quantitative macro assignment. PenState also has good sources for econometrics, here are their sources for VAR.

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You can find excellent examples of codes for DSGE models as well as VAR on QuantEcon. For example, here is an example of VAR model in Python, and here is an example of some simple DSGE model. The above are just examples, you can find different models on the site that might suit you better. In addition, you should note that in every case you will have to do ...

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The MSE is essentially a squared Euclidean distance between two vectors, say $\mathbf y$ and $\hat{\mathbf y}$, where $\mathbf y$ is the actual economic data over $T$ periods and $\hat{\mathbf{y}}$ the predicted values. A natural extension of this to matrices $\mathbf Y=(y_{it})$ and $\widehat{\mathbf Y}=(\hat y_{it})$ where $i=1,\dots,n$ and $t=1,\dots,T$ (\$...

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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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My understanding is that the standard advice is to randomly select a seed value, and then keep that seed for your entire analysis. This allows the same computer code to give identical results each time. See, for example, the Stata manual: Stata’s random-number generation functions, such as runiform() and rnormal(), do not really produce random ...

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If it's just knowing about the models, I'd suggest starting with Hamilton's "Time Series Analysis" but any book like it will do. You can also explore Google Scholar for inspiration (people likely had built similar models). It seems you have a VAR model, check this wikipedia link to see if it's what you have in mind: https://en.wikipedia.org/wiki/...

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Brexit economic forecasts usually assume that Brexit will happen and nothing more than Brexit. In other words forecasters will not assume other changes which may be pro-growth. For example they will not likely assume the "Singapore on the Thames" outcome because we do not know if it is politically feasible or realistic. All else equal, countries tend to ...

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Maybe someone can get into more details, but generally speaking The range of estimates is large, from a loss of GDP of nearly ten percentage points (in the least attractive trade and inward investment scenarios modelled by the Treasury, NIESR and the Centre for Economic Performance at LSE) 1 to a gain of four points (Minford, for Economists for ...

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In a question here someone provided us with another example. Some Moody analysts predicted in mid-2016 a Trump recession starting in 2018 of varying intensities depending on how much of Trump's policies got implemented: the economy will be significantly weaker if Mr. Trump’s economic proposals are adopted. Under the scenario in which all his stated ...

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