What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of study are Econometrics, Spatial analysis, Economic geography, Data mining and Data science.
His study in Econometrics focuses on Specification in particular.
His Spatial analysis research is multidisciplinary, incorporating perspectives in Spatial econometrics and Spatial ecology.
His study in Economic geography is interdisciplinary in nature, drawing from both Economic growth and International trade.
His Data mining study incorporates themes from Variogram, Kriging, Model selection, Information retrieval and Subject.
Manfred M. Fischer studied Data science and Geographic information system that intersect with Urban planning.
His most cited work include:
- Knowledge interactions between universities and industry in Austria: sectoral patterns and determinants (513 citations)
- Spatial Growth Regressions: Model Specification, Estimation and Interpretation (270 citations)
- Spatial Knowledge Spillovers and University Research: Evidence from Austria (240 citations)
What are the main themes of his work throughout his whole career to date?
Manfred M. Fischer focuses on Econometrics, Spatial analysis, Artificial intelligence, Artificial neural network and Spatial econometrics.
Manfred M. Fischer focuses mostly in the field of Econometrics, narrowing it down to matters related to Spatial dependence and, in some cases, Poisson distribution.
His Spatial analysis research includes elements of Spatial filter and Data mining.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
As a part of the same scientific family, he mostly works in the field of Artificial neural network, focusing on Benchmark and, on occasion, Mathematical optimization and Estimation theory.
His Panel data study combines topics in areas such as Total factor productivity and Productivity.
He most often published in these fields:
- Econometrics (28.93%)
- Spatial analysis (13.45%)
- Artificial intelligence (11.68%)
What were the highlights of his more recent work (between 2013-2021)?
- Econometrics (28.93%)
- Autoregressive model (6.35%)
- Monetary policy (2.28%)
In recent papers he was focusing on the following fields of study:
Manfred M. Fischer mainly investigates Econometrics, Autoregressive model, Monetary policy, Bayes estimator and Spatial dependence.
In the field of Econometrics, his study on Panel data and Specification overlaps with subjects such as Partial derivative and Flow.
His Autoregressive model research focuses on Prior probability and how it connects with Diversity, Asset, Context, Sample and Stochastic process.
The concepts of his Spatial dependence study are interwoven with issues in Spatial filter and Spatial analysis.
His research integrates issues of Cartography, Geographic information system, Shift-share analysis and Natural resource in his study of Spatial analysis.
As part of one scientific family, Manfred M. Fischer deals mainly with the area of Location theory, narrowing it down to issues related to the Spatial econometrics, and often Economic geography.
Between 2013 and 2021, his most popular works were:
- Handbook of Regional Science (63 citations)
- The Gravity Model for International Trade: Specification and Estimation Issues (24 citations)
- A Bayesian space‐time approach to identifying and interpreting regional convergence clubs in Europe (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Manfred M. Fischer mainly focuses on Econometrics, Spatial econometrics, Regional science, Autoregressive model and Spatial dependence.
His work on Panel data as part of general Econometrics research is often related to Partial derivative, thus linking different fields of science.
His work deals with themes such as Spatial analysis and Economic geography, which intersect with Spatial econometrics.
His work on Spatial descriptive statistics as part of general Spatial analysis research is frequently linked to Agricultural geography, thereby connecting diverse disciplines of science.
His Autoregressive model study combines topics from a wide range of disciplines, such as Bayes estimator and Inference.
He interconnects Spatial filter and Estimation in the investigation of issues within Spatial dependence.
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