Maxwell L. King

3.8k total citations
109 papers, 2.4k citations indexed

About

Maxwell L. King is a scholar working on Statistics and Probability, General Economics, Econometrics and Finance and Finance. According to data from OpenAlex, Maxwell L. King has authored 109 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Statistics and Probability, 27 papers in General Economics, Econometrics and Finance and 25 papers in Finance. Recurrent topics in Maxwell L. King's work include Statistical Methods and Inference (47 papers), Advanced Statistical Methods and Models (40 papers) and Monetary Policy and Economic Impact (26 papers). Maxwell L. King is often cited by papers focused on Statistical Methods and Inference (47 papers), Advanced Statistical Methods and Models (40 papers) and Monetary Policy and Economic Impact (26 papers). Maxwell L. King collaborates with scholars based in Australia, United States and Malaysia. Maxwell L. King's co-authors include Nada Kulendran, Xibin Zhang, Merran Evans, Baki Billah, John H. H. Lee, Rob J. Hyndman, Ralph D. Snyder, Anne B. Koehler, David E. A. Giles and Jean-Marie Dufour and has published in prestigious journals such as Journal of the American Statistical Association, Econometrica and The Review of Economics and Statistics.

In The Last Decade

Maxwell L. King

107 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxwell L. King Australia 26 1.0k 835 673 623 361 109 2.4k
James B. McDonald United States 27 921 0.9× 1.2k 1.5× 857 1.3× 418 0.7× 434 1.2× 96 3.0k
Yanqin Fan United States 27 1.2k 1.1× 1.2k 1.4× 1.2k 1.7× 630 1.0× 383 1.1× 86 2.7k
Herman J. Bierens United States 23 798 0.8× 1.2k 1.5× 765 1.1× 1.1k 1.7× 195 0.5× 64 2.3k
Jean‐Marie Dufour Canada 24 763 0.8× 1.2k 1.4× 718 1.1× 1.1k 1.8× 212 0.6× 106 2.3k
Herman K. van Dijk Netherlands 26 612 0.6× 1.1k 1.3× 737 1.1× 1.0k 1.6× 471 1.3× 153 2.3k
Jean‐Pierre Florens France 21 550 0.5× 970 1.2× 388 0.6× 446 0.7× 762 2.1× 103 2.1k
Zongwu Cai United States 27 1.7k 1.7× 1.0k 1.2× 937 1.4× 599 1.0× 465 1.3× 102 3.0k
Oliver Linton United Kingdom 32 1.6k 1.5× 2.0k 2.4× 1.5k 2.2× 928 1.5× 373 1.0× 133 3.9k
Zudi Lu China 27 874 0.9× 998 1.2× 551 0.8× 258 0.4× 174 0.5× 84 2.3k

Countries citing papers authored by Maxwell L. King

Since Specialization
Citations

This map shows the geographic impact of Maxwell L. King'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 Maxwell L. King with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxwell L. King more than expected).

Fields of papers citing papers by Maxwell L. King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maxwell L. King. 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 Maxwell L. King. The network helps show where Maxwell L. King may publish in the future.

Co-authorship network of co-authors of Maxwell L. King

This figure shows the co-authorship network connecting the top 25 collaborators of Maxwell L. King. A scholar is included among the top collaborators of Maxwell L. King 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 Maxwell L. King. Maxwell L. King 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.
King, Maxwell L., et al.. (2015). Exponential smoothing with regressors: Estimation and initialization. Model Assisted Statistics and Applications. 10(3). 253–263. 1 indexed citations
2.
King, Maxwell L., et al.. (2015). Applications of information measures to assess convergence in the central limit theorem. Model Assisted Statistics and Applications. 10(3). 265–276. 1 indexed citations
3.
King, Maxwell L., et al.. (2008). An alternative Wald type test for two linear restrictions with applications to non-linear models. Journal of Statistical Computation and Simulation. 78(11). 1017–1031. 1 indexed citations
4.
Goh, Kim‐Leng & Maxwell L. King. (2005). Pre-checking for non-monotonicity of the Wald statistic. Journal of Statistical Computation and Simulation. 75(9). 751–759. 2 indexed citations
5.
Begum, Nelufa & Maxwell L. King. (2005). Most mean powerful test for testing heteroscedastic disturbances in the linear regression model. Model Assisted Statistics and Applications. 1(1). 9–16. 1 indexed citations
6.
Billah, Baki, Maxwell L. King, Ralph D. Snyder, & Anne B. Koehler. (2005). Exponential smoothing model selection for forecasting. International Journal of Forecasting. 22(2). 239–247. 193 indexed citations
7.
Hyndman, Rob J., Xibin Zhang, & Maxwell L. King. (2004). Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC. RePEc: Research Papers in Economics. 20 indexed citations
8.
King, Maxwell L., et al.. (2004). A Wald-type test of quadratic parametric restrictions. Economics Letters. 83(3). 359–364. 2 indexed citations
9.
Goh, Kim‐Leng & Maxwell L. King. (1999). A Correction for Local Biasedness of the Wald and Null Wald Tests. Oxford Bulletin of Economics and Statistics. 61(3). 435–450. 4 indexed citations
10.
Granger, Clive W. J., Maxwell L. King, & Halbert White. (1995). Comments on testing economic theories and the use of model selection criteria. Journal of Econometrics. 67(1). 173–187. 113 indexed citations
11.
King, Maxwell L., et al.. (1993). Pre-Test Strategies for Time-Series Forecasting in the Linear Regression Model. AgEcon Search (University of Minnesota, USA). 1 indexed citations
12.
Silvapulle, Paramsothy & Maxwell L. King. (1993). Nonnested testing for autocorrelation in the linear regression model. Journal of Econometrics. 58(3). 295–314. 7 indexed citations
13.
Grose, Simone D. & Maxwell L. King. (1991). The locally unbiased two-sided Durbin—Watson test. Economics Letters. 35(4). 401–407. 5 indexed citations
14.
Dufour, Jean-Marie & Maxwell L. King. (1991). Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR(1) errors. Journal of Econometrics. 47(1). 115–143. 93 indexed citations
15.
King, Maxwell L., et al.. (1991). Locally Optimal Testing When a Nuisance Parameter is Present Only Under the Alternative. The Review of Economics and Statistics. 75(1). 1–7. 28 indexed citations
16.
King, Maxwell L.. (1987). Towards a theory of point optimal testing. Econometric Reviews. 6(2). 169–218. 90 indexed citations
17.
King, Maxwell L. & Merran Evans. (1986). Testing for Block Effects in Regression Models Based on Survey Data. Journal of the American Statistical Association. 81(395). 677–679. 14 indexed citations
18.
King, Maxwell L. & Merran Evans. (1986). Testing for Block Effects in Regression Models Based on Survey Data. Journal of the American Statistical Association. 81(395). 677–677. 7 indexed citations
19.
King, Maxwell L.. (1985). A point optimal test for autoregressive disturbances. Journal of Econometrics. 27(1). 21–37. 52 indexed citations
20.
King, Maxwell L.. (1981). The Durbin‐Watson Bounds Test and Regressions Without an Intercept*. Australian Economic Papers. 20(36). 161–170. 7 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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