7
votes
Accepted
Did William Nordhaus exclude 87% of economic industries from his climate change analysis?
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 ...
- 57.7k
4
votes
Predicted recessions that never happened
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 ...
- 17k
3
votes
Why did economic forecasters get inflations predictions so wrong?
If one could make such long comments, I would write this as a comment, not an answer. I neither forecast weather, nor inflation. However, I am a risk manager in banking, and forecasting is part of my ...
- 4,121
3
votes
Accepted
Gap in between GDP & 1 quarter forecast
You are not comparing apples with apples. According to the official documentation (your link), in the forecast dataset, NGDP1 is the real-time quarterly historical value for the previous quarter. The ...
- 4,121
3
votes
Accepted
What is economic nowcasting?
How about
"Nowcasting" by Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia (2010).
Main body is 22 pages long. From the introduction of the paper:
Economists have imperfect knowledge ...
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3
votes
Resources to derive economic forecasts
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. ...
- 2,772
3
votes
Resources to derive economic forecasts
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 ...
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2
votes
Oil prices forecast methods
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.
...
- 436
2
votes
Accepted
Should the seed be set in forecasting returns in a MCMC
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 ...
- 16.3k
2
votes
Building a time-series demand forecasting model
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 ...
- 466
2
votes
How good is a forecast? (Many outputs)
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 $\hat{\mathbf{y}}$ ...
- 15.8k
2
votes
What are good (i.e. non-random walk) time series to perform forecasting on?
I think you nailed it by requesting series that exhibit particularly strong seasonality. Two macroeconomic series you might want to consider testing on:
U.S. Unemployment rate (not seasonally ...
- 239
2
votes
Why, according to the OECD, should inflation decrease in the coming months?
It's best to read their official documentation in the link you provided. That way you hear the explanation of the OECD, as opposed to someone else's opinion /research.
The direct link is here.
P.35 ...
- 354
2
votes
Write equations for $E[Y_{t+k}|X_t,Y_t]$ and $E[X_{t+k}|X_t,Y_t]$
You need to know the properties of conditional expectations. Learn them if you don't.
Write the expressions for $Y_{t+k}$ and for $X_{t+k}$.
Apply the conditional expectation in the expressions in 2. ...
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1
vote
Machine learning and Inflation forecasting
I am far from being an expert, but would say it is not the model but the data that can help you gain forecast accuracy in this case. Modern machine learning applications in macroeconomic forecasting ...
- 2,385
1
vote
Procedure to aggregate bottom up expenditure projections into a GDP figure
When no price indices or nominal series are available to replicate chain-linking, just summing over the estimated components will be good enough. This holds especially over short forecast horizons ...
- 1,662
1
vote
Accepted
The effect of stationarity of forecasting
Problem is that in case the data are non-stationary your out of sample forecasts will usually be poor.
Even if it by random chance performs well in your training out of sample set, it is very likely ...
- 57.7k
1
vote
How to assess the quality of a forecast?
Forecasting is a very interesting topic for economists. There are many techniques, but basically the key is to compare your forecast against a base model, which could be validation data (test data), a ...
1
vote
Which variable keep/get rid in a time series model?
I don't know a lot about VAR, but model selection is well-developed in the context of OLS and should be analogously applicable to VAR.
LASSO is the most commonly used method of model selection. The ...
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1
vote
The economics of a "no deal" Brexit
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 ...
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1
vote
The economics of a "no deal" Brexit
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 ...
1
vote
Predicted recessions that never happened
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 ...
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