References

Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone. 2019. “Vulnerable Growth.” American Economic Review 109 (4): 1263–89.

Anderson, T. W. 2004. An Introduction to Multivariate Statistical Analysis. 3rd ed. New York: John Wiley; Sons.

Bahaj, Saleem, Angus Foulis, and Gabor Pinter. 2020. “Home Values and Firm Behavior.” American Economic Review 110 (7): 2225–70. https://doi.org/10.1257/aer.20180649.

Banbura, M., D. Giannone, and L. Reichlin. 2010. “Large Bayesian Vector Auto Regressions.” Journal of Applied Econometrics 25 (1): 71–92.

Blake, Andrew P. 2004. “Analytic Derivatives in Linear Rational Expectations Models.” Computational Economics 24 (1): 77–96.

Blake, Andrew P, and Haroon Mumtaz. 2017. Applied Bayesian Econometrics for Central Bankers. Revised. Technical Books. Centre for Central Banking Studies, Bank of England. https://www.bankofengland.co.uk/-/media/boe/files/ccbs/resources/applied-bayesian-econometrics-for-central-bankers-updated-2017.pdf.

Blanchard, O., and C. Kahn. 1980. “The Solution of Linear Difference Models Under Rational Expectations.” Econometrica 48 (5): 1305–11.

Boehmke, Brad, and Brandon M. Greenwell. 2019. Hands-on Machine Learning with R. The R Series. Boca Raton: Chapman & Hall/CRC. https://bradleyboehmke.github.io/HOML/.

Capistrán, Carlos, and Allan Timmermann. 2009. “Forecast Combination with Entry and Exit of Experts.” Journal of Business & Economic Statistics 27 (4): 428–40.

Carter, C. K., and R. Kohn. 1994. “On Gibbs sampling for state space models.” Biometrika 81 (3): 541–53. https://doi.org/10.1093/biomet/81.3.541.

Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven & London: Yale University Press. https://mixtape.scunning.com/.

Durbin, J., and S. J. Koopman. 2001. Time Series Analysis by State Space Methods. Oxford: Oxford University Press.

Gaglianone, W. P., and L. R. Lima. 2012. “Constructing Density Forecasts from Quantile Regressions.” Journal of Money, Credit and Banking 44 (8): 1589–1607.

Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2019. Regression and Other Stories. Cambridge: Cambridge University Press. http://www.stat.columbia.edu/~gelman/regression.

Giannone, Domenico, Michele Lenza, and Giorgio E. Primiceri. 2015. “Prior Selection for Vector Autoregressions.” The Review of Economics and Statistics 97 (2): 436–51.

Greene, William H. 1997. Econometric Analysis. Third. McGraw Hill.

Hamilton, J. D. 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.

Hamilton, James D. 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.

Harvey, Andrew C. 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.

Harvey, Andrew C., and Richard G. Pierse. 1984. “Estimating Missing Observations in Economic Time Series.” Journal of the American Statistical Association 79 (385): 125–31.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer. https://web.stanford.edu/~hastie/ElemStatLearn/.

Hvitfeldt, Emil, and Julia Silge. 2021. Supervised Machine Learning for Text Analysis in R. Chapman & Hall: CRC Press. https://smltar.com/.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. 2nd ed. Springer Texts in Statistics. New York, NY: Springer. https://www.statlearning.com/.

Jazwinski, Andrew H. 1970. Stochastic Processes and Filtering Theory. Mineola, N.Y.: Dover Publications Inc.

Kalman, R. E. 1960. “A New Approach to Linear Filtering and Prediction Problems.” Transactions of the ASME Journal of Basic Engineering 82 (Series D): 35–45.

Kim, Chang-Jin, and Charles R. Nelson. 1999. State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. MIT Press.

King, Robert G., and Mark W. Watson. 2002. “System Reduction and Solution Algorithms for Singular Linear Difference Systems Under Rational Expectations.” Computational Economics 20 (1–2): 57–86.

Klein, Paul. 2000. “Using the Generalized Schur Form to Solve a Multivariate Linear Rational Expectations Model.” Journal of Economic Dynamics and Control 24 (10): 1405–23.

Klugman, Stuart A., Harry H. Panjer, and Gordon E. Willmot. 2008. Loss Models: From Data to Decisions. Third. Hoboken, N.J.: John Wiley & Sons, Inc.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. 1st ed. The R Series. Boca Raton: Chapman & Hall/CRC. https://geocompr.robinlovelace.net/.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd ed. Abingdon, Oxfordshire: CRC Press. https://github.com/rmcelreath/rethinking.

Pappas, T., A. J. Laub, and N. R. Sandell. 1980. “On the Numerical Solution of the Discrete-Time Algebraic Riccati Equation.” IEEE Transaction on Automatic Control AC-25.4: 631–41.

Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. Chichester: John Wiley & Sons. http://bayes.cs.ucla.edu/PRIMER/.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. O’Reilly. https://www.tidytextmining.com/.

Taddy, Matt. 2019. Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions. New York, NY: McGraw-Hill Education. https://github.com/TaddyLab/BDS.

Theil, Henri, and Arthur S. Goldberger. 1961. “On Pure and Mixed Statistical Estimation in Economics.” International Economic Review 2: 317–32.

Triantafyllopoulos, Kostas. 2021. Bayesian Inference of State Space Models: Kalman Filtering and Beyond. Springer Texts in Statistics. Cham, Switzerland: Springer.

Vaughan, D. R. 1970. “A Non Recursive Algorithm Solution for the Discrete Riccati Equation.” IEEE Transactions on Automatic Control AC-15.5: 597–99.

Wilke, Claus O. 2020. ggridges: Ridgeline Plots in ’ggplot2’. https://CRAN.R-project.org/package=ggridges.

Wilkinson, Leland. 2013. The Grammar of Graphics. 2nd ed. New York, NY: Springer-Verlag.

Yong, Laurel Harbridge, Jon A. Krosnick, and Jeffrey M. Wooldridge. 2016. “Presidential Approval and Gas Prices: Sociotropic or Pocketbook Influence?” In Political Psychology, edited by Jon A. Krosnick, I-Chant A. Chiang, and Tobias H. Stark, 246–75. Taylor; Francis Inc.