Econometric modeling | Spatial analysis
Spatial econometrics is the field where spatial analysis and econometrics intersect. The term “spatial econometrics” was introduced for the first time by the Belgian economist Jean Paelinck (universally recognised as the father of the discipline) in the general address he delivered to the annual meeting of the Dutch Statistical Association in May 1974 (Paelinck and Klaassen, 1979).In general, econometrics differs from other branches of statistics in focusing on theoretical models, whose parameters are estimated using regression analysis. Spatial econometrics is a refinement of this, where either the theoretical model involves interactions between different entities, or the data observations are not truly independent. Thus, models incorporating spatial auto-correlation or neighborhood effects can be estimated using spatial econometric methods. Such models are common in regional science, real estate economics, education economics, housing market and many others. Adopting a more general view, in the by-law of the [1], the discipline is defined as the set of “models and theoretical instruments of spatial statistics and spatial data analysis to analyse various economic effects such as externalities, interactions, spatial concentration and many others” (Spatial Econometrics Association, 2006). Recent developments tend to include also methods and models from social network econometrics. (Wikipedia).
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
21b Spatial Data Analytics: Dispersion Variance
Subsurface modeling course lecture on dispersion variance.
From playlist Spatial Data Analytics and Modeling
10c Data Analytics: Variogram Introduction
Lecture on the variogram as a measure to quantify spatial continuity.
From playlist Data Analytics and Geostatistics
01c Spatial Data Analytics: Modeling Goals
A lecture on subsurface modeling goals.
From playlist Spatial Data Analytics and Modeling
21 Spatial Data Analytics: Spatial Scale
Subsurface modeling course lecture on scale.
From playlist Spatial Data Analytics and Modeling
01d Spatial Data Analytics: Modeling Strategies
A lecture on spatial, subsurface modeling strategies and workflows.
From playlist Spatial Data Analytics and Modeling
01b Spatial Data Analytics: Subsurface Data
Lecture of the data available for subsurface modeling.
From playlist Spatial Data Analytics and Modeling
22 Spatial Data Analytics: Decision Making
Spatial data analytics course lecture on optimum decision making in the presence of uncertainty.
From playlist Spatial Data Analytics and Modeling
Tao Zou - Network Influence Analysis
Dr Tao Zou (ANU) presents "Network Influence Analysis”, 20 August 2020. Seminar organised by the Australian National University.
From playlist Statistics Across Campuses
Regional Climate Projections and High-Resolution Economic Modeling
Tony Smith, Yale, delivers a lecture entitled, "Regional Climate Projections and High-Resolution Economic Modeling", during the YCEI conference, "Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists". More at yale.edu
From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists
Impact of interannual climate variability... - Groth - Workshop 3 - CEB T3 2019
Groth (UCLA, ENS) / 04.12.2019 Impact of interannual climate variability on the agricultural sector in the Sahel region ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHen
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Data Science - Part XVI - Fourier Analysis
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of the Fourier Analysis and the Fourier Transform as applied in Machine Learnin
From playlist Data Science
40 - Poisson model: crime count example introduction
This video provides an introduction to a use of Bayesian inference with a Poisson likelihood function, which we will use for the next few videos to examine the posterior distribution, as well as the prior and posterior predictive distributions, when a gamma prior is assumed. If you are in
From playlist Bayesian statistics: a comprehensive course
Jean-Pierre Florens: Inverse problems in econometrics - Lecture 1/4
Recording during the thematic month on statistics - Week 2 : "Mathematical statistics and inverse problems" the 9 February, 2016 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide
From playlist Probability and Statistics
01 Spatial Data Analytics: Subsurface Modeling
Lecture discussing the concept of subsurface modeling, integrating information sources, quantification over volume and properties of interest for decision support.
From playlist Spatial Data Analytics and Modeling
Threshold Switching Models | Switching Models in Econometrics, Part 2
This is the second video in a two-part series that shows how to model time series data in the presence of regime shifts in MATLAB in this video, we use Threshold Switching Models from the Econometrics Toolbox to model inflation data across different inflationary regimes. Download the code
From playlist Switching Models in Econometrics
Markov Switching Models | Switching Models in Econometrics, Part 1
This is the first video in a two-part series that shows how to model time series data in the presence of regime shifts in MATLAB. In this video, William Mueller uses Markov switching models from the Econometrics Toolbox to model unemployment data across different economic regimes. Downloa
From playlist Switching Models in Econometrics
Introduction to Econometrics Toolbox in MATLAB
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Create a predictive time-series model of a stock index. For more videos, visit http://www.mathworks.com/products/econometrics/examples.html
From playlist Computational Finance
Sylvia Frühwirth-Schnatter: Bayesian econometrics in the Big Data Era
Abstract: Data mining methods based on finite mixture models are quite common in many areas of applied science, such as marketing, to segment data and to identify subgroups with specific features. Recent work shows that these methods are also useful in micro econometrics to analyze the beh
From playlist Probability and Statistics
14 Data Analytics: Indicator Methods
Lecture on the use of indicators for spatial estimation and simulation.
From playlist Data Analytics and Geostatistics