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Books on the topic 'Time series regression'

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Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Konstantinos, Fokianos, ed. Regression models for time series analysis. Hoboken, N.J: Wiley-Interscience, 2002.

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2

Ostrom, Charles W. Time series analysis: Regression techniques. 2nd ed. Newberry Park, Calif: Sage Publications, 1990.

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3

Hylleberg, Svend. Seasonality in regression. Orlando: Academic Press, 1986.

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4

Chih-Ling, Tsai, ed. Regression and time series model selection. Singapore: World Scientific, 1998.

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5

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471266981.

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6

Štulajter, František. Predictions in time series using regression models. New York: Springer, 2002.

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7

Seasonality in regression. Orlando, Fla: Academic Press, 1986.

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8

Fuller, Wayne A. Introduction to statistical time series. 2nd ed. New York: Wiley, 1996.

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9

Fuller, Wayne A. Introduction to statistical time series. 2nd ed. New York: Wiley, 1996.

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10

Robinson, P. M. Time series regression with long range dependence. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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11

Štulajter, František. Predictions in Time Series Using Regression Models. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3629-8.

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12

Forecasting with dynamic regression models. New York: Wiley, 1991.

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13

T, O'Connell Richard, Koehler Anne B, and Bowerman Bruce L, eds. Forecasting, time series, and regression: An applied approach. 4th ed. Belmont, CA: Thomson Brooks/Cole, 2005.

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14

Uribe, Jorge M., and Montserrat Guillen. Quantile Regression for Cross-Sectional and Time Series Data. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44504-1.

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15

Lewis, Peter A. W. Nonlinear modeling of time series using Multivariate Adaptive Regression Splines (MARS). Monterey, Calif: Naval Postgraduate School, 1990.

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16

Bowerman, Bruce L. Forecasting and time series: An applied approach. 3rd ed. Belmont, Calif: Duxbury Press, 1993.

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17

Vroomen, Harry. Forecasting retail fertilizer prices: A combined time-series regression analysis approach. [Washington, D.C.?]: U.S. Dept. of Agriculture, Economic Research Service, 1991.

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18

Wooldridge, Jeffrey M. Regression-based inference in linear time series models with incomplete dynamics. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1990.

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19

Marinucci, D. Band spectrum regression for cointegrated time series with long memory innovations. London: Suntory Centre, 1998.

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20

Semenov, Nikolaĭ Aleksandrovich. Programmy regressionnogo analiza i prognozirovanii͡a︡ vremennykh ri͡a︡dov. Moskva: "Finansy i statistika", 1990.

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21

Ot͡s︡enka parametrov i struktury ėkonomicheskikh prot͡s︡essov. Moskva: "Ėkonomika", 1985.

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22

Klein, Judy L. Statistical visions in time: A history of time series analysis, 1662-1938. Cambridge: Cambridge University Press, 1997.

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23

RATS handbook for econometric time series. New York: Wiley, 1996.

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24

1951-, Christensen Ronald, ed. Advanced linear modeling: Multivariate, time series, and spatial data; nonparametric regression and response surface maximization. 2nd ed. New York: Springer, 2001.

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25

Urga, Giovanni. Panel data vs. time series regression analysis: An aggregation issue : a comparison using labour demand functions. London: London University, Queen Mary and Westfield College, Department of Economics, 1993.

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26

Hastie, Trevor. Varying-coefficient models. Toronto: University of Toronto, Dept. of Statistics, 1991.

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27

Farnum, Nicholas R. Quantitative forecasting methods. Boston: PWS-Kent Pub. Co., 1989.

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28

Data Analysis Using Regression Models: The Business Perspective. Englewood Cliffs, N.J: Prentice Hall, 1995.

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29

Silvapulle, Paramsothy. Testing stationary nonnested short memory against long memory processes. Bundoora, Vic., Australia: La Trobe University, Schools of Economics and Commerce, 1996.

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30

Bizer, David S. Testing the positive theory of government finance. Cambridge, MA: National Bureau of Economic Research, 1990.

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31

Stevens, James G. An investigation of multivariate adaptive regression splines for modeling and analysis of univariate and semi-multivariate time series systems. Monterey, Calif: Naval Postgraduate School, 1991.

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32

John, DiNardo, ed. Econometric methods. 4th ed. New Yorki: McGraw Hil, 2007.

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33

Donaldson, Janet R. STARPAC: The Standard Times Series and Regression Package. Boulder, Colo: U.S. Dept. of Commerce, National Bureau of Standards, 1987.

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34

Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".

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35

Skalin, Joakim. Modelling macroeconomic time series with smooth transition auto regressions. Stockholm: Stockholm School of Economics, 1999.

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36

Ishiguro, M. ARdock, an auto-regressive model analyzer. Tokyo: Institute of Statistical Mathematics, 1999.

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37

Ishiguro, M. ARdock, an auto-regressive model analyzer. Tokyo: Institute of Statistical Mathematics, 1999.

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38

Econometric methods. 4th ed. New York: McGraw-Hill, 1997.

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39

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."

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40

Koehler, Anne, Richard O'Connell, and Bruce L. Bowerman. Forecasting, Time Series, and Regression (with CD-ROM) (Forecasting, Time Series, & Regression). 4th ed. Duxbury Press, 2004.

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41

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Wiley & Sons, Incorporated, John, 2008.

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42

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Wiley & Sons Australia, Limited, John, 2003.

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43

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. Wiley & Sons, Incorporated, John, 2005.

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44

Hylleberg, Svend, and Karl Shell. Seasonality in Regression. Elsevier Science & Technology Books, 2014.

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45

Pinola, Rudy. Applied regression and time series analysis. Economic Research Service, 1997.

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46

Stulajter, Frantisek. Predictions in Time Series Using Regression Models. Springer London, Limited, 2013.

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47

Stulajter, Frantisek. Predictions in Time Series Using Regression Models. Springer, 2011.

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48

Fuller, Wayne A. Introduction to Statistical Time Series. Wiley & Sons, Incorporated, John, 2009.

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49

Stulajter, Frantisek. Predictions in Time Series Using Regression Models. Springer, 2002.

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50

Pankratz, Alan. Forecasting with Dynamic Regression Models. Wiley & Sons, Incorporated, John, 2012.

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