(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Nadia Hassan writes:
2016 may be strange with Trump. Do you have any thoughts on how people might go about modeling a strange election? When I asked you about predictability and updating election forecasts, you stated that models that rely on polls at different points should be designed to allow for surprises. You have touted the power of weakly informative priors. Could those be a good tool for this situation?
I received this message on 4 Apr and I’m typing this on 9 Apr but it’s 17 Aug in blog time. So you’re actually reading a response that’s 4 months old.
What is it that they say: History is journalism plus time? I guess political science is political journalism plus time.
Anyway . . . whenever people asked me about the primary elections, I’d point them to my 2011 NYT article, Why Are Primaries Hard to Predict? Here’s the key bit:
Presidential general election campaigns have several distinct features that distinguish them from most other elections:
1. Two major candidates;
2. The candidates clearly differ in their political ideologies and in their positions on economic issues;
3. The two sides have roughly equal financial and organizational resources;
4. The current election is the latest in a long series of similar contests (every four years);
5. A long campaign, giving candidates a long time to present their case and giving voters a long time to make up their minds.
OK, now to Hassan’s question. I don’t really have a good answer! I guess I’d take as a starting point the prediction from a Hibbs-like model predicting the election based on economic conditions during the past year, presidential popularity, and party balancing. Right now the economy seems to be going OK though not great, Obama is reasonably popular, and party balancing favors the Democrats because the Republicans control both houses of Congress. So I’m inclined to give the Democratic candidate (Hillary Clinton, I assume) the edge. But that’s just my guess, I haven’t run the numbers. There’s also evidence from various sources that more extreme candidates don’t do so well, so if Sanders is the nominee, I’d assume he’d get a couple percentage points less than Clinton would. Trump . . . it’s hard to say. He’s not ideologically extreme, on the other hand he is so unpopular (even more so than Clinton), it’s hard to know what to say. So I find this a difficult election to predict. And once August rolls around, it’s likely there will be some completely different factors that I haven’t even thought about! From a statistical point of view, I guess I’d just add an error term which would increase my posterior uncertainty.
It’s not so satisfying to say this, but I don’t have much to offer as an election forecast beyond what you could read in any newspaper. I’m guessing that statistical tools will be more relevant in modeling what will happen in individual states, relative to the national average. As Kari Lock and I wrote a few years ago, it can be helpful to decompose national trends and the positions of the states. So maybe by the time this post appears here, I’ll have more to say.
P.S. This seems like a natural for the sister blog but I’m afraid the Washington Post readers would get so annoyed at me for saying I can’t make a good forecast! So I’m posting it here.
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