Recommended Reading for February

Here are some reading suggestions:

• Bruns, S. B., Z. Csereklyei, & D. I. Stern, 2018. A multicointegration model of global climate change. Discussion Paper No. 336, Center for European, Governance and Economic Development Research, University of Goettingen.
• Catania, L. & S. Grassi, 2017. Modelling crypto-currencies financial time-series. CEIS Tor Vegata, Research Paper Series, Vol. 15, Issue 8, No. 417.
• Farbmacher, H., R. Guber, & J. Vikström, 2018. Increasing the credibility of the twin birth instrument. Journal of Applied Econometrics, online.
• Liao, J. G. & A. Berg, 2018. Sharpening Jensen's inequality. American Statistician, online.
• Reschenhofer, E., 2018. Heteroscedasticity-robust estimation of autocorrelation. Communications in Statistics - Simulation and Computation, online.

© 2018, David E. Giles

Here's What I've Been Reading

Here are some of the papers that I've been reading recently. Some of them may appeal to you, too:

• Bampinas, G., K. Ladopoulos, &T. Panagiotidis, 2017.  A note on the estimated GARCH coefficients from the S&P1500 universe. WP 17-09, Rimini Centre for Economic Analysis.
• Heberle, J. & C. Sattarhoff, 2017. A fast algorithm for the computation of HAC covariance matrix estimators. Econometrics, 5(1), 9; doi:10.3390/econometrics5010009.
• Kristensen, D. & B. Salanié, 2017. Higher-order properties of approximate estimators. Journal of Econometrics, online.
• Lei, J., M. G'Sell, A. Rinaldo, R. J. Tibshirani, & L. Wasserman, 2017.Distribution-free predictive inference for regression. Journal of the American Statistical Association, online.
• Tsagbey, S., M. De carvalho, & G. L. Page, 2017. All data are wrong, but some are useful? Advocating the need for data auditing. American Statistician, online. 
• Zhang, X. & C-A. Liu, 2017. Inference after model averaging in linear regression models. IEAS Working Paper No. 17-A005.

© 2017, David E. Giles

Trading Models and Distributed Lags

Yesterday, I received an email from Robert Hillman.

Robert wrote:

"I’ve thoroughly enjoyed your recent posts and associated links on distributed lags. I’d like to throw in a slightly different perspective.

 To give you some brief background on myself: I did a PhD in econometrics 1993-1998 at Southampton University. ............ I now manage capital and am heavily influenced by my study of econometrics and in particular exploring the historical foundations of many things that today that look new and funky but are probably old but no less funky!

I wanted to draw attention to the fact that many finance practitioners have long used ‘models’ that in my view are robust and heuristic versions of nonlinear ADL models. I’m not sure this interpretation is as widely recognised as it could be."

With Robert's permission, you can access the full contents of what Robert had to say, here

Robert provides some interesting and useful insights into the connections between certain trading models and ARDL models, and I thought that these would be useful to readers of this blog.

Thanks, Robert!

© 2017, David E. Giles

December Reading List

Goodness me! November went by really quickly!

• Bagnato, L., L. De Capitani, & A. Punzo, 2016. Testing for serial independence: Beyond the portmanteau approach. American Statistician, in press.
• Aastveit, K.A., C. Foroni, & F. Ravazzolo, 2016. Density forecasts with MIDAS models. Journal of Applied Econometrics, in press.
• Chang, C-L. & M. McAleer, 2016. A simple test for causality in volatility. Discussion Paper TI 2016-094/III, Tinbergen Institute.
• Frank, J. & B. Klar, 2016. Methods to test for equality of two normal distributions. Statistical Methods and Applications, 25, 581-599.
• Gao, J., G. Pan, & Y. Yang, 2016. Estimation of structural breaks in large panels with cross-sectional dependence. Working Paper 12/16, Department of Econometrics and Business Statistics, Monash University.
• Skeels, C.L. & F. Windmeijer, 2016. On the Stock-Yogo tables. Discussion Paper 16/679, Department of Economics, Univesity of Bristol.

 
© 2016, David E. Giles

September Reading

Here are a few suggestions for some interesting reading this month:

• Esquivel, M.L., P.P. Mota, & J.T. Mexia, 2016. On some statistical  models with a random number of observations. Journal of Statistical Theory and Practice, online.
• Gorroochurn, P., 2015. On Galton's change from "reversion" to "regression". American Statistician, in press.
• Kourtellos, A., T. Stengos, & C.M. Tan, 2016. Structural threshold regression. Econometric Theory, 32,827-860.
• Jandhyala, V., S. Fotopoulos, I. MacNeill, & P. Liu, 2016. Inference for single and multiple change-points in time series. Journal of Time Series Analysis, in press.
• Malloch, H., R. Philip, & S. Satchell, 2016. Decomposing the bias in time-series estimates of CAPM betas. Applied Economics, 28, 4291-4298.
• Xu, J. & P. Perron, 2016. Forecasting in the presence of in and out of sample breaks. Mimeo.

© 2016, David E. Giles

Conference on Macro & Financial Economics/Econometrics

The 10th BMRC-DEMS Conference is being held at Brunel University, London (U.K.), at the end of May this year. Details can be found here.

The conference themes include a number of important topics:

• Recent developments in time-varying and nonlinear models
• Economic dynamics and smooth transition modeling
• Structural breaks in financial time series
• Dynamic structural financial and macroeconometric modeling
• Macro-financial modeling using mixed frequency data
• Monetary policy and risk taking
• Fiscal policy, financial development and growth
• Macro-finance interface
• Asset pricing models with time-varying moments
• Financial markets volatility and macroeconomic activity
• Financial crash, stock, bond and commodity prices
• Modeling dynamic correlations during financial crises
• Boom-bust cycles and the linkage between financial and real activity
• Early warning indicators of economic and financial instability

There's an impressive line-up of keynote speakers:

• R. Baillie (Michigan State University)
• V. CorradiI (University of Surrey)
• C. Francq (University of Lille 3)
• M. Hallin (ECARES, Brussels)
• A. Harvey (University of Cambridge)
• D. Hendry (University of Oxford)
• G. Melard (ECARES, Brussels)
• P. Minford (Cardiff University)
• R. Taylor (University of Essex)
• S. Wright (Birkbeck College, University of London)
• P. Zaffaroni (Imperial College, London)
• J.-M. Zakoian (CREST, Paris)

© 2014, David E. Giles

Money 101: Top Resources for Finance Majors

Abigail Moore, the Content Creator at OnlineFinanceDegree.org, emailed me today:

"I'm writing with the exciting news that our latest article, "Money 101: Top Resources for Finance Majors," has been published, and Econometrics Beat: Dave Giles' Blog is cited on it.

Finance is an engaging and highly competitive professional field to get into. For those studying finance, the internet can offer a wealth of resources. That said, it can be hard to sift through and find the best. We hope this feature will help finance students find information about financial organizations, current economic news, financial modeling software and techniques, and the finance industry as a whole. Your site is a valuable addition to this resource.

We're hoping to share this article with as many finance students and professionals as possible and will be contacting our readers and followers. If you're able to post this on your website or share the article anywhere else you can think of too, I'd really appreciate it." 

No problem, Abigail - happy to oblige. You'll find this blog listed as site #8.

© 2013, David E. Giles

Economics Nobel Prize, 2013

The waiting is over - the 2013 Nobel in Economics was announced this morning! Most deservedly, it has been awarded to Eugene F. Fama (U. Chicago), Lars Peter Hansen (U. Chicago), and Robert J. Shiller (Yale U.). The citation says: "For their empirical analysis of asset prices". 

For more details, see here.

It's really  nice to see the recognition of empirical research.

And let's not forget that Hansen gave us GMM estimation; and do you recall Shiller distributed lag models?

© 2013, David E. Giles

So Much Good Reading........

Here are my latest reading suggestions:

• Choi, I., 2013. Panel Cointegration. Working Paper, Department of Economics, Sogang University, Korea.
• Davidson, R. and J. G. MacKinnon, 2013. Bootstrap tests for overdentification in linear regression models. Economics Department Working Paper No. 1318, Queen's University.
• Deng, A., 2013. Understanding spurious regression in financial econometrics. Journal of Financial Econometrics, in press.
• Feng, C., H. Wang, Y. Han, and Y. Xia, 2013. The mean value theorem and Taylor's expansion in statistics. The American Statistician, in press.
• Kiviet, J. F. and G. D. A. Phillips, 2013. Improved variance estimation of maximum likelihood estimation in stable first-order dynamic regression models. EGC Report No. 2012/06, Division of Economics, Nanyang Technical University.
• Lanne, M., M. Meitz, and P. Saikkonen, 2013. Testing for linear and nonlinear predicatability of stock returns. Journal of Financial Econometrics, 11, 682-705.

© 2013, David E. Giles

New Working Paper

Yanan Li (a former graduate student) and I have just released a new Working Paper. It's titled, "Modelling Volatility Spillover Effects Between Developed Stock Markets and Developing Asian Stock Markets".

If you're interested, you can download a copy of the paper from here.

ver Effects Between Developed Stock Markets and Asian Emerging Stock Marketsodelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets

© 2013, David E. Giles

More on Multiple Bubbles

In a recent post I highlighted a new EViews Add-in package, written by Itamar Caspi. His rtdaf package facilitates the application of the Right-Tail Augmented Dickey-Fuller tests that are "....designed to detect the presence of an unobserved bubble component in an observed asset price and to date-stamp its occurrence".

In part, the testing procedures are based on Phillips et al. (2013b). If you're following this literature, there are two other recent papers by those authors that are a must-read - Phillips et al. (2013,a,b)

References

Phillips, P. C. B., Shi, S., and Yu, J., 2013a, Specification sensitivity in right-tailed unit root testing for explosive behaviour. Oxford Bulletin of Economics and Statistics, forthcoming.

Phillips, P., S. Shi, and J. Yu, 2013b. Testing for Multiple Bubbles 1: Historical episodes of exuberance and collapse in the S&P 500. Working paper.

Phillips, P., S. Shi, and J. Yu, 2013c. Testing for Multiple Bubbles 2: Limit theory of real time detectors.  SMU Economics and Statistics Working Paper Series, No. 05-2013.

© 2013, David E. Giles

Conference on Computing in Economics & Finance

The 19th. International Conference on Computing in Economics and Finance is coming up in Vancouver, B.C., this week. There's an interesting program.

One of my M.A. students, Yanan Li, and I have a paper that Yanan will be presenting at the conference. 

The paper is titled "Modeling Volatility Effects Between Emerging Asian Stock Markets and Developed Stock Markets".

© 2013, David E. Giles

Webinar With Robert F. Engle

It's not every day that you can get to see a presentation by a Nobel laureate econometrician for free, and without leaving home.

MoneyScience

MoneyScience - which describes itself as "the community resource for Quantitative Finance, Risk Management and Technology Practitioners, Vendors and Academics" - has recently released version 3 of its site.

There's a great deal of interesting work going on in "Financial Econometrics", and this is one site that provides really good content and excellent networking facilities that will help keep econometricians up to speed with what is going on in the finance community at large.

I'm pleased to be feeding this blog to MoneyScience (here),  and you'll notice a new icon near the bottom of the right side-bar:

© 2011, David E. Giles