Sergio G. Koreisha

681 total citations
33 papers, 481 citations indexed

About

Sergio G. Koreisha is a scholar working on General Economics, Econometrics and Finance, Statistics and Probability and Control and Systems Engineering. According to data from OpenAlex, Sergio G. Koreisha has authored 33 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in General Economics, Econometrics and Finance, 14 papers in Statistics and Probability and 10 papers in Control and Systems Engineering. Recurrent topics in Sergio G. Koreisha's work include Monetary Policy and Economic Impact (16 papers), Advanced Statistical Methods and Models (12 papers) and Control Systems and Identification (9 papers). Sergio G. Koreisha is often cited by papers focused on Monetary Policy and Economic Impact (16 papers), Advanced Statistical Methods and Models (12 papers) and Control Systems and Identification (9 papers). Sergio G. Koreisha collaborates with scholars based in United States, Finland and China. Sergio G. Koreisha's co-authors include Tarmo Pukkila, M. Megan Partch, Yue Fang, Christopher M. James, Christopher J. James, Greg Hundley, Qi-Man Shao, Yongli Zhang and Simon F. Giszter and has published in prestigious journals such as The Journal of Finance, Biometrika and Energy Economics.

In The Last Decade

Sergio G. Koreisha

30 papers receiving 421 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sergio G. Koreisha United States 13 228 194 181 167 98 33 481
R. Deane Terrell Australia 11 147 0.6× 166 0.9× 94 0.5× 193 1.2× 84 0.9× 41 423
Victoria Zinde‐Walsh Canada 12 105 0.5× 150 0.8× 218 1.2× 154 0.9× 46 0.5× 37 421
Giorgio Calzolari Italy 13 248 1.1× 350 1.8× 161 0.9× 299 1.8× 62 0.6× 52 565
Degui Li United Kingdom 16 235 1.0× 252 1.3× 420 2.3× 433 2.6× 111 1.1× 79 904
Carsten Jentsch Germany 11 140 0.6× 137 0.7× 150 0.8× 220 1.3× 36 0.4× 48 458
Rolf Tschernig Germany 9 98 0.4× 134 0.7× 76 0.4× 149 0.9× 47 0.5× 18 333
Gael M. Martin Australia 12 203 0.9× 285 1.5× 131 0.7× 231 1.4× 96 1.0× 37 517
Xiaohong Chen United States 13 216 0.9× 265 1.4× 301 1.7× 347 2.1× 60 0.6× 30 713
W. K. Li Hong Kong 9 170 0.7× 420 2.2× 230 1.3× 304 1.8× 75 0.8× 16 712

Countries citing papers authored by Sergio G. Koreisha

Since Specialization
Citations

This map shows the geographic impact of Sergio G. Koreisha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sergio G. Koreisha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergio G. Koreisha more than expected).

Fields of papers citing papers by Sergio G. Koreisha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sergio G. Koreisha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sergio G. Koreisha. The network helps show where Sergio G. Koreisha may publish in the future.

Co-authorship network of co-authors of Sergio G. Koreisha

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio G. Koreisha. A scholar is included among the top collaborators of Sergio G. Koreisha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sergio G. Koreisha. Sergio G. Koreisha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fang, Yue, Sergio G. Koreisha, & Qi-Man Shao. (2023). Revisiting the Use of Generalized Least Squares in Time Series Regression Models. Journal of Data Science. 486–504. 3 indexed citations
2.
Koreisha, Sergio G. & Tarmo Pukkila. (2004). The specification of vector autoregressive moving average models. Journal of Statistical Computation and Simulation. 74(8). 547–565. 8 indexed citations
3.
Fang, Yue & Sergio G. Koreisha. (2004). Forecasting with serially correlated regression models. Journal of Statistical Computation and Simulation. 74(9). 625–649. 5 indexed citations
4.
Koreisha, Sergio G. & Yue Fang. (2001). Generalized Least Squares with Misspecified Serial Correlation Structures. Journal of the Royal Statistical Society Series B (Statistical Methodology). 63(3). 515–531. 28 indexed citations
5.
Koreisha, Sergio G. & Tarmo Pukkila. (2000). Using the Residual White Noise Autoregressive Order Determination Criterion to Identify Unit Roots in Arima Models. Communications in Statistics - Simulation and Computation. 29(1). 259–293.
6.
Koreisha, Sergio G. & Yue Fang. (1999). The impact of measurement errors on ARMA prediction. Journal of Forecasting. 18(2). 95–109. 1 indexed citations
7.
Koreisha, Sergio G. & Tarmo Pukkila. (1999). The selection of the order and identification of nonzero elements in the polynomial matrices of vector autoregressive processes. Journal of Statistical Computation and Simulation. 62(3). 207–235. 5 indexed citations
8.
Koreisha, Sergio G. & Tarmo Pukkila. (1995). A Comparison between Different Order-Determination Criteria for Identification of ARIMA Models. Journal of Business and Economic Statistics. 13(1). 127–127. 3 indexed citations
9.
Koreisha, Sergio G. & Tarmo Pukkila. (1995). A Comparison Between Different Order-Determination Criteria for Identification of ARIMA Models. Journal of Business and Economic Statistics. 13(1). 127–131. 10 indexed citations
10.
Koreisha, Sergio G. & Tarmo Pukkila. (1993). New approaches for determining the degree of differencing necessary to induce stationarity in ARIMA models. Journal of Statistical Planning and Inference. 36(2-3). 399–412. 8 indexed citations
11.
Koreisha, Sergio G., et al.. (1991). A Comparison among Identification Procedures for Autoregressive Moving Average Models. International Statistical Review. 59(1). 37–37. 19 indexed citations
12.
Koreisha, Sergio G. & Tarmo Pukkila. (1990). Linear Methods for Estimating Arma and Regression Models with Serial Correlation. Communications in Statistics - Simulation and Computation. 19(1). 71–102. 23 indexed citations
13.
Koreisha, Sergio G. & Tarmo Pukkila. (1990). A GENERALIZED LEAST‐SQUARES APPROACH FOR ESTIMATION OF AUTOREGRESSIVE MOVING‐AVERAGE MODELS. Journal of Time Series Analysis. 11(2). 139–151. 53 indexed citations
14.
Koreisha, Sergio G. & Tarmo Pukkila. (1989). FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING‐AVERAGE MODELS. Journal of Time Series Analysis. 10(4). 325–339. 27 indexed citations
15.
Koreisha, Sergio G. & Tarmo Pukkila. (1987). Identification of Nonzero Elements in the Polynomial Matrices of Mixed Varma Processes. Journal of the Royal Statistical Society Series B (Statistical Methodology). 49(1). 112–126. 23 indexed citations
16.
Hundley, Greg & Sergio G. Koreisha. (1987). The specification of econometric strike models: A VARMA approach. Applied Economics. 19(4). 511–530. 9 indexed citations
17.
Koreisha, Sergio G., et al.. (1985). Identification of iransfer function models: an asymptoiic test of significance for the corner method. Communication in Statistics- Theory and Methods. 14(1). 159–173.
18.
James, Christopher M., Sergio G. Koreisha, & M. Megan Partch. (1985). A VARMA Analysis of the Causal Relations Among Stock Returns, Real Output, and Nominal Interest Rates. The Journal of Finance. 40(5). 1375–1384. 96 indexed citations
19.
Giszter, Simon F., et al.. (1984). A vector autoregressive moving average time series approach for describing asymmetries of antennal control of two millipede species. Journal of Mathematical Biology. 19(3). 281–302. 2 indexed citations
20.
Koreisha, Sergio G.. (1980). The limitations of energy policy models. Energy Economics. 2(2). 96–110. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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