scholarly journals The Spatial Structure of Farmland Values: A Semiparametric Approach

2018 ◽  
Vol 47 (3) ◽  
pp. 568-591 ◽  
Author(s):  
Haoying Wang
Keyword(s):  
Panel Data ◽  
Spatial Structure ◽  
Spatial Competition ◽  
Statistical Tests ◽  
Competition Theory ◽  
Semiparametric Approach ◽  
Out Of Sample ◽  
Spatial Weights Matrix ◽  
Spatial Autoregressive ◽  
Farmland Values

Controlling for spatial heterogeneity and spatial dependence in farmland valuation models has gained substantial attention in recent literature. This paper proposes to derive the spatial structure of farmland values endogenously and semiparametrically based on the spatial competition theory. The paper assembles panel data of Pennsylvania county level farmland values between 1982 and 2012. A spatial autoregressive panel data model with spatial weights matrix endogenously incorporated is estimated. Out of sample predictions and non-nested statistical tests for model selection suggest that the fit and the predictability of hedonic farmland valuation models can be greatly improved.

Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

2020 ◽  
Vol 05 (1&2) ◽  
pp. 1-13
Author(s):  
Mohamed R Abonazel ◽  
Keyword(s):  
Panel Data ◽  
Personal Income ◽  
Spatial Weights ◽  
Spatial Panel Data Models ◽  
Spatial Weights Matrix ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
Per Capita ◽  
Capita Personal Income ◽  
Inverse Distance

Over the last decades, the Per Capita Personal Income (PCPI) variable was a common measure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by using spatial panel data models for 48 U.S. states during the period from 2009 to 2017. We utilize the three following models: spatial autoregressive (SAR) model, Spatial Error (SEM) Model, and Spatial Autoregressive Combined (SAC) model, with individual (or spatial) fixe deffects according to three different known methods for constructing spatial weights matrices: binary contiguity, inverse distance, and Gaussian transformation spatial weights matrix. Additionally, we pay attention for direct and indirect effects estimates of the explanatory variables for SAR, SEM, and SAC models. The second objective of this paper is to show how to select the appropriate model to fit our data. The results indicate that the three used spatial weights matrices provide the same result based on goodness of fit criteria, and the SAC model is the most appropriate model among the models presented. However, the SAC model with spatial weights matrix based on inverse distance is better compared to other used models. Also, the results indicate that percentage of individuals with graduate or professional degree, real Gross Domestic Product (GDP) per capita,and number of nonfarm jobs have a positive impact on the PCPI, while the percentage of individuals without degree or bachelor’s degree have a negative impact on the PCPI.

scholarly journals Predicting the future development of mild cognitive impairment in the cognitively healthy elderly

2020 ◽  
Author(s):  
Bryan Strange ◽  
Linda Zhang ◽  
Alba Sierra-Marcos ◽  
Eva Alfayate ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Memory ◽  
Statistical Tests ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Healthy Individuals ◽  
Healthy Elderly ◽  
Out Of Sample

Identifying measures that predict future cognitive impairment in healthy individuals is necessary to inform treatment strategies for candidate dementia-preventative and modifying interventions. Here, we derive such measures by studying converters who transitioned from cognitively normal at baseline to mild-cognitive impairment (MCI) in a longitudinal study of 1213 elderly participants. We first establish reduced grey matter density (GMD) in left entorhinal cortex (EC) as a biomarker for impending cognitive decline in healthy individuals, employing a matched sampling control for several dementia risk-factors, thereby mitigating the potential effects of bias on our statistical tests. Next, we determine the predictive performance of baseline demographic, genetic, neuropsychological and MRI measures by entering these variables into an elastic net-regularized classifier. Our trained statistical model classified converters and controls with validation Area-Under-the-Curve>0.9, identifying only delayed verbal memory and left EC GMD as relevant predictors for classification. This performance was maintained on test classification of out-of-sample converters and controls. Our results suggest a parsimonious but powerful predictive model for MCI development in the cognitively healthy elderly.

scholarly journals Lack of Spatial Approach in Urban Density Policies: The Case of the Master Plan of Tehran

2020 ◽  
Vol 12 (18) ◽  
pp. 7285
Author(s):  
Mostafa Ghadami ◽  
Andreas Dittmann ◽  
Taher Safarrad
Keyword(s):  
Spatial Structure ◽  
Statistical Tests ◽  
Area Ratio ◽  
Master Plan ◽  
Urban Spatial Structure ◽  
Floor Area Ratio ◽  
Spatial Approach ◽  
Arc Gis ◽  
Geographic Weighted Regression ◽  
The City

This paper aims to investigate the approach of density policies in the Tehran Master Plan and the consequences of ignoring the macro spatial scale in density policymaking. In this study, the floor area ratio (FAR) regulations of the Master Plan of Tehran (which are defined by specific land use zones) are used as one of the main densification tools. Then, employing the Getis–Ord Local G and geographic weighted regression (GWR) statistical tests, Arc GIS 10.3 software, and population and employment variables, the spatial outcomes of the Master Plan density policies were modeled. In this research, both population and employment (job) variables and their relationship were utilized to depict the urban spatial structure of the city. The model will show the resulting spatial structure of Tehran if the densification policies of the plan are realized. The findings of the research are surprising, as they indicate that the Master Plan’s densification policies would worsen the current spatial structure by disrupting the current population and employment spatial structure and neglecting their logical relationships. In fact, the Master Plan would change the current polycentric structure into a highly dispersed structure due to its densification approach, which is mainly based on the neighborhood micro scale.

scholarly journals Bitcoin Returns and the Frequency of Daily Abnormal Returns

Ledger ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Alex Plastun ◽  
Viktor Oliinyk
Keyword(s):  
Linear Models ◽  
Statistical Tests ◽  
Piecewise Linear ◽  
Weighted Least Squares ◽  
Abnormal Returns ◽  
Future Price ◽  
Consensus Forecasts ◽  
Out Of Sample ◽  
Regression Techniques ◽  
Linear Regressions

This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection  criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).

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