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Dive into the research topics where Chaitra H. Nagaraja is active.

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Featured researches published by Chaitra H. Nagaraja.


The Annals of Applied Statistics | 2011

An autoregressive approach to house price modeling

Chaitra H. Nagaraja; Lawrence D. Brown; Linda H. Zhao

A statistical model for predicting individual house prices is proposed utilizing only information regarding sale price, time of sale, and location (ZIP code). This model is composed of a xed time eect and a random ZIP (postal) code eect combined with an autoregressive component. The latter piece is applied only to homes sold repeatedly while the former two components are applied to all of the data. In addition, the autoregressive component incorporates heteroscedasticity in the errors. To evaluate the proposed model, single-family home sales for twenty U.S. metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case-Shiller model, which is a repeat sales model, and a conventional mixed eects model. It is also shown that the time eect in the proposed model can be converted into a house price index. Finally, the special case of Los Angeles, CA is discussed as an example of history repeating itself in regards to the current housing market meltdown.


European Journal of Operational Research | 2015

Measuring the bullwhip effect for supply chains with seasonal demand components

Chaitra H. Nagaraja; A. Thavaneswaran; S.S. Appadoo

A bullwhip measure for a two-stage supply chain with an order-up-to inventory policy is derived for a general, stationary SARMA(p, q) × (P, Q)s demand process. Explicit expressions for several SARMA models are obtained to illustrate the key relationship between lead-time and seasonal lag. It is found that the bullwhip effect can be reduced considerably by shortening the lead-time in relation to the seasonal lag value.


Journal of Real Estate Literature | 2014

Repeat Sales House Price Index Methodology

Chaitra H. Nagaraja; Lawrence D. Brown; Susan M. Wachter

We compare four traditional repeat sales indices to a recently developed autoregressive index that makes use of the repeat sales methodology but includes single sales and a location effect. Qualitative comparisons on statistical issues including the effect of gap time on sales, use of hedonic information, and treatment of single and repeat sales are addressed. Furthermore, predictive ability is incorporated as a quantitative metric into the analysis using data from US home sales in twenty metropolitan areas. The indices tend to track each other over time; however, the differences are substantial enough to be of interest, and we find that the autoregressive index performs best overall.


Statistical journal of the IAOS | 2015

On the Interpretation of Multi-Year Estimates of the American Community Survey as Period Estimates

Chaitra H. Nagaraja; Tucker McElroy

The rolling sample methodology of the American Community Survey leads to Multi-Year Estimates that measure aggregate activity over one, three, or five years. This paper introduces a novel, non-model-based method for quantifying the impact of viewing multi-year estimates as functions of single-year estimates belonging to the same time span. The method is based on examining the changes to confidence interval coverage. The interpretation of a multi-year estimate as the simple average of single-year estimates is a viewpoint that underpins the published estimates of sampling variability. Therefore, it is vital to ascertain the extent to which this viewpoint is valid. We apply our new methodology to data from the U.S. Census Bureaus Multi-Year Estimates Study and demonstrate that viewing a multi-year estimate as the simple average of single-year estimates typically results in substantial distortions to coverage; therefore, multi-year estimates should not be interpreted as averages, but merely as period estimates.


European Journal of Operational Research | 2017

The Multivariate Bullwhip Effect

Chaitra H. Nagaraja; Tucker McElroy

A multivariate bullwhip expression for m products with an order-up-to inventory policy is developed. The demand models under consideration are differenced stationary vector time series with a Wold representation for which general forecasting formulas are available, resulting in a large class of possible models (including nonstationary ones). Examples are provided for common demand models and implemented on sales data. It is found that the multivariate approach gives rise to mechanisms for understanding and reducing the bullwhip effect through horizontal information sharing, particularly for the nonstationary demand case. In the stationary setting, a more nuanced approach to bullwhip reduction can be achieved by managing the relationship between cross-correlations and lead-times. A method of determining whether a multivariate or univariate approach generates a lower bullwhip effect is proposed.


Communications in Statistics-theory and Methods | 2017

Theory and methods for partitioned Gini coefficients computed on post-stratified data

Chaitra H. Nagaraja

ABSTRACT The Gini coefficient is used to measure inequality in populations. However, shifts in the population distribution may affect subgroups differently. Consequently, it can be informative to examine inequality separately for these segments. Consider an independently and identically distributed sample split based on ranking and compute the Gini coefficient for each partition. These coefficients, calculated from post-stratified data, are not functions of U-statistics. Therefore, previous theoretical and methodological results cannot be applied. In this article, the asymptotic joint distribution is derived for the partitioned coefficients and bootstrap methods for inference are developed. Finally, an application to per capita income across census tracts is examined.


Handbook of Statistics | 2014

Introduction to R

Chaitra H. Nagaraja

Abstract The basics of R programming are discussed from syntax, conditional statements, and control structures, to writing functions. Fundamentals such as data cleaning, exploratory data analysis, hypothesis testing, and regression are introduced. Simulation and random generation and numerical methods are considered as well. Finally, a list of add-on packages and additional resources are listed.


ICSA Applied Statistics Symposium Proceedings | 2013

Constructing and Evaluating an Autoregressive House Price Index

Chaitra H. Nagaraja; Lawrence D. Brown

We examine house price indices, focusing on an S&P/Case-Shiller-based index and an autoregressive method. Issues including the effect of gap time on sales and the use of hedonic information are addressed. Furthermore, predictive ability is incorporated as a quantitative metric into the analysis using data from home sales in Columbus, Ohio. When comparing the two indices, the autoregressive method is found to have the best predictive capabilities while accounting for changes in the market due to both single and repeat sales homes in a statistical model.


Archive | 2010

House Price Index Methodology

Chaitra H. Nagaraja; Lawrence D. Brown; Susan M. Wachter


Journal of Statistical Planning and Inference | 2016

Tail index estimation with a fixed tuning parameter fraction

Tucker McElroy; Chaitra H. Nagaraja

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Lawrence D. Brown

University of Pennsylvania

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Tucker McElroy

United States Census Bureau

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Hon Keung Tony Ng

Southern Methodist University

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Pankaj K. Choudhary

University of Texas at Dallas

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Susan M. Wachter

University of Pennsylvania

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Linda H. Zhao

University of Pennsylvania

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