A Spatio-Temporal Model of House Prices in the US

By S. Holly, M. Hashem. 2010

317 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6da1b46b46a2ff71ffefa451a3d1a4d7879a01ed

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In Economics and Econometrics

Citations in the corpus

Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique (2017, result): “Similar results were arrived at by Holly, Pesaran, and Yamagata (2010) in their study of the United States and, using unique cross-sectional data on the majority of German counties and cities for 2005, Bischoff (2012) also identified an interdependence between real estate prices and income.”
Generalized Spatial and Spatiotemporal Autoregressive Conditional Heteroscedasticity (2016, background): “re packages such that it is simple to model spatial clusters of high and low observations. Consequently, a wide range of applications can be found in empirical research, including econometrics (e.g., Holly et al. 2010, Fingleton 2008b), biometrics (e.g., Shinkareva et al. 2006, Ho et al. 2005, MacNa”
Growth, Development and Natural Resources: New Evidence Using a Heterogeneous Panel Analysis (2011, unknown): “Given these observations, we employ the estimation procedure developed by Pesaran (2006) and applied in Holly et al. (2009) to see whether the resource curse is in fact present in our sample or whether this naive estimation procedure, as employed in the literature to support the existence of the…”
Do House Price Developments Spill Over Across Euro Area Countries? Evidence from a Global VAR (2011, background): “This appears consistent with the finding of Holly, Pesaran, and Yamagata (2006) for the US that real house prices and real income per capita at the US state level are cointegrated by estimating a model which allows for unobserved common factors that could potentially be correlated with the observed…”
Trade openness, income levels, and economic growth: The case of developing countries, 1970–2009 (2015, background): “…as the way to properly eliminate both strong and weak common factors in large cross-sectionally dependent panel data models, and is consistent even when the associated errors are weakly cross-sectionally dependent (see Pesaran 2006; Holly, Pesaran, and Yamagata 2010; Pesaran and Tosetti 2011).”
The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation (2018, unknown): “…as a misspecification test for regression models that already account for cross-section dependence has repeatedly been observed in empirical studies (see, e.g., Holly, Pesaran, and Yamagata 2010; Everaert and Pozzi 2014, Bailey, Holly, and Pesaran 2016; Eberhardt and Presbitero 2015 among others).”
Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia (2020, methodology): “They are used in epidemiology for prevalence mapping of infectious diseases [Giorgi et al., 2018], and play a key role in socio-economic research, for example, for the modeling of housing prices [Holly et al., 2010], or for election forecasting [Pav´ıa et al., 2008].”
Financial markets, innovations and cleaner energy production in OECD countries (2018, unknown): “Our consideration of the short-run and long-run CD properties is supported by Holly et al. (2010) and Mayor and Patuelli (2012).”
Testing for Panel Cointegration Using Common Correlated Effects Estimators (2017, unknown): “Holly et al. (2010) use a CCE-based procedure in their investigation without developing a formal test for cointegration.”
Natural Resources and Economic Development: New Panel Evidence (2015, methodology): “…−2.7745*** −3.7667** −1.9158** Group ADF statistic −1.5670** −2.9947*** −4.1559*** −1.9783** Kao (1999) ADF statistic 2.0561** 1.3858* 2.0442** 1.8594** Holly et al. (2010) CIPS statistic −6.113*** −9.303*** −5.383*** −5.357*** Larsson et al. (2001) Cointegration rank Cointegration…”