# statistics

## Forecasting with logs

As an economist and all-around friend of strictly positive numbers I often use the log function. The natural logarithm of course, need I specify it? Apparently in certain spreadsheet software you do. In this note I just wanted to write down a couple of observations about how to generate mean or median forecasts of a variable $$y$$ given the model is fit in $$log(y)$$. Of course, I am going to borrow heavily from Rob Hyndman’s blog, where he coverse this.

## Lower Mortgage Rates Bolster the Housing Market

Mortgage interest rates have moved about a percentage point lower from where they were a year ago. The housing market seems to have responded favorably. On my way into D.C. the other day to do some business, I joined a Twitter exchange originally between [at]Graykimbrough and Adam Ozimek, [at]ModeledBehavior about the effects of Federal Reserve interest policy on the housing market. Seems unlikely housing market was slowed by trade war.

## What Happens in Vegas Doesn't Always Stay in Vegas

I’ve got a new working paper with Hua Kiefer (FDIC) and Diana Wei (OCC) that studies the dynamics of house prices and foreclosure rates across space and time. We estimate a model using a panel of state/quarters where nearby states influence one another. Link to paper (pdf): What Happens in Vegas Doesn’t Always Stay in Vegas Note Updated May 17, 2019 I’m giving a talk on this paper at the American Real Estate and Urban Economics National Conference later this month.

## Is the U.S. housing recovery over? Housing fluctuations across time and frequencies

The current economic expansion is set to enter its tenth year this summer. Assuming we make it to June, this will become the longest U.S. economic expansion in recorded history stretching back to the 19th century. But how is the housing market doing? After a decade of recovery housing market activity still has room for improvement, but trends in 2018 were negative. Home sales, housing construction and house price growth all declined in 2018.

## Vulnerable Housing

My recent economic and housing market talks see for example here have been titled: “Will the U.S. housing market get back on track in 2019?”. My general conclusion has been cautiously optimistic. There is enough strength in the broader economy and enough of a tailwind from demographic forces to push the U.S. housing market to modest growth next year. I still think that’s true, but as I have said in my talks, risks are weighted to the downside.

## A note on competing risks

WE ARE LATE FOR HALLOWEEN, but let’s get out our broom and purrr as we tidy some statistical results. Today I had occasion to be reminded of competing risks and a handy statistical result on competing risks from A.P. Basu and J.K. Ghosh published in the Journal of Multivariate Analysis in 1978. The paper Identifiability of the multinormal and other distributions under competing risks model showed an analytical result on the distribution of a variable Z which is the minimum of two Gaussian (Normal) random variables.

## Dynamic Model Averaging Presentation Slides

I PUT TOGETHER SOME SLIDES SUMMARIZING our recent work on dynamic model averaging. See here and here for more blah blah blah. See below for some slides. Click here for a fullscreen version here. Making the Preso Let me also share with you the R code I used to generate these slides. The code below is the Rmarkdown I used to generate the slides (saved as .txt). The slides were put together using the xaringan package.

## A closer look at forecasting recessions with dynamic model averaging

BACK WE GO INTO THE VASTY DEEP. LAST TIME we introduced the idea of using dynamic model averaging to forecast recessions. I was so excited about the new approach that I didn’t take the time to break down what was going on with it. In this post we’ll look more closely at what’s happening with the dma packaged when we try to forecast recessions. Per usual we’ll do it with R and I’ll include code so you can follow along.

## Forecasting recessions with dynamic model averaging

HERE THE LITERATURE IS VASTY DEEP. In this post we’ll dip our toes, every so slightly, into the dark waters of macroeconometric forecasting. I’ve been studying some techniques and want to try them out. I’m still at the learning and exploring stage, but let’s do it together. In this post we’ll conduct an exercise in forecasting U.S. recessions using several approaches. Per usual we’ll do it with R and I’ll include code so you can follow along.

## Of kernels and beeswarms: Comparing the distribution of house values to household income

R statistics dataviz housing mortgage data

## Resampling

R statistics dataviz housing mortgage data