arXiv subjects

Emil Mendoza

Publications and source records attributed to Emil Mendoza.

3 recordsLinked to original sources

arXiv2023

Changes in Risk Appreciation, and Short Memory of House Buyers When the Market is Hot, a Case Study of Christchurch, New Zealand

In this paper house prices in Christchurch are analyzed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown using the well-established hedonic price model. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, value the risk of potential earthquake damage to a property differently from buyers soon after the earthquake. We find that there are observable shifts in hedonic prices across the different time periods, specifically for section size pre and post COVID-19 lockdown.

q-fin.GN↗

arXiv2021

Regularized Maximum Likelihood Estimation for the Random Coefficients Model

The random coefficients model $Y_i={β_0}_i+{β_1}_i {X_1}_i+{β_2}_i {X_2}_i+\ldots+{β_d}_i {X_d}_i$, with $\mathbf{X}_i$, $Y_i$, $\mathbfβ_i$ i.i.d, and $\mathbfβ_i$ independent of $X_i$ is often used to capture unobserved heterogeneity in a population. We propose a quasi-maximum likelihood method to estimate the joint density distribution of the random coefficient model. This method implicitly involves the inversion of the Radon transformation in order to reconstruct the joint distribution, and hence is an inverse problem. Nonparametric estimation for the joint density of $\mathbfβ_i=({β_0}_i,\ldots, {β_d}_i)$ based on kernel methods or Fourier inversion have been proposed in recent years. Most of these methods assume a heavy tailed design density $f_\mathbf{X}$. To add stability to the solution, we apply regularization methods. We analyze the convergence of the method without assuming heavy tails for $f_\mathbf{X}$ and illustrate performance by applying the method on simulated and real data. To add stability to the solution, we apply a Tikhonov-type regularization method.

stat.ME↗

arXiv2021

Nonparametric Estimation of the Random Coefficients Model in Python

We present $\textbf{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\textbf{PyRMLE}$ is simple to use and readily works with data formats that are typical to Random Coefficient problems. The module makes use of Python's scientific libraries $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features.

stat.CO↗