Suggested Reading for August

Here are my suggestions for this month:

• Bun, M. J. G. & T. D. Harrison, 2109. OLS and IV estimation of regression models including endogenous interaction terms. Econometric Reviews, 38, 814-827.
• Dufour, J-M., E. Flachaire, & L. Khalaf, Permutation tests for comparing inequality measures. Journal of Business and Economic Statistics, 37, 457-470.
• Jiao, X. & F. Pretis, 2018. Testing the presence of outliers in regression models. Available at SSRN: https://ssrn.com/abstract=3217213.
• Stanton, J. M., 2001. Galton, Pearson, and the peas: A brief history of linear regression for statistics instructors. Journal of Statistics Education, 9, 1-13.
• Trafimow, D., 2019. A frequentist alternative to significance testing, p-values and confidence intervals. Econometrics, 7, 26.

© 2019, David E. Giles

Reading Suggestions for June

Well, here we are - it's June already.

Here are my reading suggestions:

• Abadie, A., S. Athey, G. Imbens, & J. Wooldridge, 2017. When should you adjust standard errors for clustering? Mimeo.
• Berk, R., A. Buja, L. Brown, E. George, A. K. Kuchibhotla, W. Su, & L, Shazo, 2019. Assumption lean regression. American Statistician, in press.
• Ghosh, T., M. Ghosh, & T. Kubokawa, 2019. On the loss robustness of least-square estimators, American Statistician, in press.
• Gustafsson, O. & P. Stockhammar, 2019. Variance stabilizing filters. Communications in Statistics - Theory and Methods, in press.
• Kocherlakota, N. R., 2019. A near-exact finite sample theory for an instrumental variable estimator. Mimeo. (Hat-tip to Frank Diebold.)
• Panagiotelis, A., G. Anathasopoulos, R. J. Hyndman, B. Jiang, & F. Vahid, 2019. Macroeconomic forecasting for Australia using a large number of predictors. International Journal of Forecasting, 35, 613-633.

© 2019, David E. Giles

Some April Reading for Econometricians

Here are my suggestions for this month:

• Hyndman, R. J., 2019. A brief history of forecasting competitions. Working Paper 03/19, Department of Econometrics and Business Statistics, Monash University.
• Kuffner, T. A. & S. G. Walker, 2019. Why are p-values controversial?. American Statistician, 73, 1-3.
• Sargan, J. D.,, 1958. The estimation of economic relationships using instrumental variables. Econometrica, 26, 393-415. (Read for free online.)  
• Sokal, A. D., 1996. Transgressing the boundaries: Towards a trasnformative hermeneutics of quantum gravity. Social Text, 46/47, 217-252.
• Zeng, G. & Zeng, E., 2019. On the relationship between multicollinearity and separation in logistic regression. Communications in Statistics - Simulation and Computation, published online.
• Zhang, X., S. Paul, & Y-G. Yang, 2019. Small sample bias correction or bias reduction? Communications in Statistics - Simulation and Computation, published online.

© 2019, David E. Giles

Some Recommended Econometrics Reading for March

This month I am suggesting some overview/survey papers relating to a variety of important topics in econometrics:

• Bruns, S. B. & D. I. Stern, 2019. Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models. Empirical Economics, 56, 797-830.
• Casini, A. & P. Perron, 2018. Structural breaks in time series. Forthcoming in Oxford Research Encyclopedia in Economics and Finance. 
• Hendry, D. F. & K. Juselius, 1999. Explaining cointegration analysis: Pat I. Mimeo., Nuffield College, University of Oxford.
• Hendry, D. F. & K. Juselius, 2000. Explaining cointegration analysis: Part II. Mimeo., Nuffield College, University of Oxford.
• Horowitz, J., 2018. Bootstrap methods in econometrics. Cemmap Working Paper CWP53/18. 
• Marmer, V., 2017. Econometrics with weak instruments: Consequences, detection, and solutions. Mimeo., Vancouver School of Economics, University of British Columbia.

© 2019, David E. Giles

December Reading for Econometricians

My suggestions for papers to read during December:

• Askanazi, R., F. X. Diebold, F. Schorfheide, & M. Shin, 2018. On the comparison of interval forecasts. PIER Working Paper 18-013, Penn. Institute for Economic Research, University of Pennsylvania.
• Meintanis, S. G., J. Ngatchou-Wandji, & J. Allison, 2018. Testing for serial independence in vector autoregressive models, Statistical Papers, 59, 1379-1410.
• Milas, C., T. Panagiotidis, & T. Dergiades, 2018. Twitter versus traditional news media: Evidence for the sovereign bond markets. Mimeo., Management School, University of Liverpool.
• Ollech, D., 2018. Seasonal adjustment of daily time series. Discussion Paper No. 41/2018, Deutsche Bundesbank.
• Sickles, R. C., W. Song, & V. Zelenyuk, 2018. Econometric analysis of productivity: Theory and implementation in R. Working Paper WP08/2018, Centre for Efficiency and productivity Analysis, Department of Economics, University of Queensland.
• Skeels, C. L. & F. Windmeijer, 2018. On the Stock-Yogo tables. Econometrics, 6 (4), 44.

© 2018, David E. Giles

Econometrics Reading for November

In between raking leaves and dealing with some early snow, I've put together this list of suggested reading for you:

• Beckert, W., 2018. A note on specification testing in some structural regression models. Mimeo., Department of Economics, Mathematics and Statistics, Birkbeck College, University of London.
• Clarke, D., 2018. A convenient omitted bias formula for treatment effect models. Economics Letters, in press.
• Liu, Y. & Y. Rho, 2018. On the choice of instruments in mixed frequency specification tests. Mimeo., School of Business and Economics, Michigan Technological University.
• Lütkepohl, H., A. Staszewska-Bystrova, & P. Winker, 2018. Constructing joint confidence bands for impulse functions of VAR models - A review. Lodz Economic Working Paper 4/2018, Faculty of Economics and Sociology, University of Lodz.
• Richardson, A., T. van Florenstein Mulder, & T. Vehbi, 2018. Nowcasting New Zealand GDP using machine learning algorithms.
• Słoczyński, T., 2018. A general weighted average representation of the ordinary and two-stage least squares estimands. Mimeo., Department of Economics, Brandeis University.

© 2018, David E. Giles

April Reading

Very belatedly, here is my list of suggested reading for April:

• Biørn, E., 2017. Identification, instruments, omitted variables, and rudimentary models: Fallacies in the "experimental approach" to econometrics. Memorandum No. 13/2017, Department of Economics, Oslo University.
• Chambers, M. J., and M. Kyriacou, 2018. Jackknife bias reduction in the presence of a near-unit root. Econometrics, 6, 11.
• Derryberry, D., K. Aho, J. Edwards, and T. Peterson, 2018. Model selection and regression t-statistics. American Statistician, in press.
• Mitchell, J., D. Robertson, and S. Wright, 2018. R² bounds for predictive models: What univariate properties tell us about multivariate predictability. Journal of Business and Economic Statistics, in press. (Free download here.)
• Parker, T., 2017. Finite-sample distributions of the Wald, likelihood ratio, and Lagrange multiplier test statistics in the classical linear model. Communications in Statistics - Theory and Methods, 46, 5195-5202.
• Troster, V., 2018. Testing Granger-causality in quantiles. Econometric Reviews, 37, 850-866.

© 2018, David E. Giles

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

Reading for the Holidays

Here are some suggestions for your Holiday reading:

• Athey, S. and G. Imbens, 2016. The state of econometrics - Causality and policy evaluation. Mimeo., Graduate School of Business, Stanford University.
• Cook, J. D., 2010. Testing a random number generator. Chapter 10, in T. Rily and A. Goucher (eds.), Beautiful Testing, O' Reilly Media, Sebastol, CA. 
• Ivanov, V. and L. Kilian, 2005. A practitioner's guide to lag order selection for VAR impulse response analysis. Studies in Nonlinear Dynamics and Econometrics, 9, article 2.
• Polanin, J. A., E. A. Hennessy, and E. E. Tanner-Smith, 2016. A review of meta-analysis packages in R. Journal of Educational and Behavioural Statistics, 42, 206-242.
• Young, A., 2017. Consistency without inference: Instrumental variables in practical application. Mimeo.,  London School of Economics.
• Zhang, L., 2017, Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study. Communications in Statistics - Theory and Methods, 46, 12317-12323.

© 2017, David E. Giles

Recommended Reading for October

• Andor, N. & C. Parmeter, 2017. Pseudolikelihood estimation of the stochastic frontier model. Ruhr Economic Papers #693.
• Chalak, K., 2017. Instrumental variables methods with heterogeneity and mismeasured instruments. Econometric Theory, 33, 69-104.
• Kim, J. H. & I. Choi, 2017. Unit roots in economic and financial time series: A re-evaluation at the decision-based significance levels. Econometrics, 56 (3), 41.
• Owen, A. B., 2017. Statistically efficient thinning of a Markov chain sampler. Journal of Computational and Graphical Statistics, 26, 738-744. 
• Owen, P. D., 2017. Evaluating ingenious instruments for fundamental determinants of long-run economic growth and development. Econometrics, 5 (3), 38.
• Richard, P., 2017. Robust heteroskedasticity-robust tests. Economics Letters, 159, 28-32.

© 2017, David E. Giles

Canada Day Reading List

I was tempted to offer you a list of 150 items, but I thought better of it!

• Hamilton, J. D., 2017. Why you should never use the Hodrick-Prescott filter. Mimeo., Department of Economics, UC San Diego.

• Jin, H. and S. Zhang, 2017. Spurious regression between long memory series due to mis-specified structural breaks. Communications in Statistics - Simulation and Computation, in press.

• Kiviet, J. F., 2016. Testing the impossible: Identifying exclusion restrictions.Discussion Paper 2016/03, Amsterdam School of Economics, University of Economics.

• Lenz, G. and A. Sahn, 2017. Achieving statistical significance with covariates. BITSS Preprint (H/T  Arthur Charpentier)

• Sephton, P., 2017. Finite sample critical values of the generalized KPSS test. Computational Economics, 50, 161-172.

• Stypka, O., P. Grabarczyk, R. Kawka, and M. Wagner, 2017. "Linear" fully modified OLS estimation of cointegrating polynomial regressions. Discussion Paper Nr. 77/2016, SFB 823. (H/T David Stern)

© 2017, David E. Giles

Instrumental Variables & the Frisch-Waugh-Lovell Theorem

The so-called Frisch-Waugh-Lovell (FWL) Theorem is a standard result that we meet in pretty much any introductory grad. course in econometrics.

The theorem is so-named because (i) in the very fist volume of Econometrica Frisch and Waugh (1933) established it in the particular context of "de-trending" time-series data; and (ii) Lovell (1963) demonstrated that the same result establishes the equivalence of "seasonally adjusting" time-series data (in a particular way), and including seasonal dummy variables in an OLS regression model. (Also, see Lovell, 2008.)

We'll take a look at the statement of the FWL Theorem in a moment. First, though, it's important to note that it's purely an algebraic/geometric result. Although it arises in the context of regression analysis, it has no statistical content, per se.

What's not generally recognized, however, is that the FWL Theorem doesn't rely on the geometry of OLS. In fact, it relies on the geometry of the Instrumental Variables (IV) estimator - of which OLS is a special case, of course. (OLS is just IV in the just-identified case, with the regressors being used as their own instruments.)

Implicitly, this was shown in an old paper of mine (Giles, 1984) where I extended Lovell's analysis to the context of IV estimation. However, in that paper I didn't spell out the generality of the FWL-IV result.

Let's take a look at all of this.

When Everything Old is New Again

Some ideas are so good that they keep re-appearing again and again. In other words, they stand the test of time, and prove to be useful in lots of different contexts – sometimes in situations that we couldn’t have imagined when the idea first came to light.

This certainly happens in econometrics, and here are just a few examples that come to mind.

Read Some Econometrics this Month!

There are no April Fool's tricks in the following list of suggestions. 😐

• Antoine, B. and E. Renault, 2017. On the relevance of weaker instruments. Econometric Reviews, online.
• Golan, A. and A. Ullah, 2017. Interval estimation: An information theoretic approach. Econometric Reviews, online.
• Hendry, D. F. and P. C. B. Phillips, 2017. John Denis Sargan at the London School of Economics. Cowles Foundation Discussion Paper No. 2082, Yale University.
• Ozonur, D., H. T. K. Akdur, & H. Bayrak, 2016. Comparisons of tests of distributional assumption in Poisson regression model. Communications in Statistics - Simulation and Computation, online.
• Vijayamohanan, P. N., 2017. How do you interpret your regression coefficients? MPRA Paper No. 76867.
• Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, online.

© 2017, David E. Giles

Vintage Years in Econometrics - The 1970's

Continuing on from my earlier posts about vintage years for econometrics in the 1930's, 1940's, 1950's, 1960's, here's my tasting guide for the 1970's.

Once again, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

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

My Latest Paper About Dummy Variables

Over the years I've posted a number of times about various aspects of using dummy variables in regression models. You can use the "Search" window in the right sidebar of this page if want to take a look at those posts.

One of my earlier working papers on this topic has now been accepted for publication.

The paper is titled, "On the Inconsistency of Instrumental Variables Estimators for the Coefficients of Certain Dummy Variables". Here's the abstract:

"In this paper we consider the asymptotic properties of the Instrumental Variables (IV) estimator of the parameters in a linear regression model with some random regressors, and other regressors that are dummy variables. The latter have the special property that the number of non-zero values is fixed, and does not increase with the sample size. We prove that the IV estimator of the coefficient vector for the dummy variables is inconsistent, while that for the other regressors is weakly consistent under standard assumptions. However, the usual estimator for the asymptotic covariance matrix of the I.V. estimator for all of the coefficients retains its usual consistency. The t-test statistics for the dummy variable coefficients are still asymptotically standard normal, despite the inconsistency of the associated IV coefficient estimator. These results extend the earlier results of Hendry and Santos (2005), which relate to a fixed-regressor model, in which the dummy variables are non-zero for just a single observation, and OLS estimation is used".

You can download the final working paper version of the paper from here.

The paper will be appearing in an upcoming issue of Journal of Quantitative Economics.

© 2016, David E. Giles

February Reading List

Here's a suggested reading list for February:

• Casey, G. and M. Klemp, 2016. Instrumental variables in the long run. MPRA Paper No. 68696.
• Coglianese, J., L. W. Davis, L. Kilian, and J. H. Stock, 2016. Anticipation, tax avoidance, and the price elasticity of gasoline demand. Journal of Applied Econometrics, in press.
• Falorsi, S., A. Naccarato, and A. Pierini, 2015. Using Google trend data to predict the Italian unemployment rate. Working Paper No. 203, Dipartimento di Economia, Università degli studi Roma Tre.
• Harris, D., S. J. Leybourne, and A. M. Robert, 2016. Test of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point. Working Paper No. 5 01-2016, Essex Finance Centre, Essex Business School, University of Essex.
• Inoue, A. and G. Solon, 2010. Two-sample instrumental variables estimators. Review of Economics and Statistics, 93, 557-561.
• Kim, N., 2016. A robustified Jarque-Bera test for multivariate normality. Economics Letters, in press.

© 2016, David E. Giles

July Reading

Now that the (Northern) summer is here, you should have plenty of time for reading. Here are some recommendations:

• Ahelegbey, D. F., 2015. The econometrics of networks: A review. Working Paper 13/WP/2015, Department of Economics, University of Venice.
• Camba-Mendez, G., G. Kapetanios, F. Papailias, and M. R. Weale, 2015. An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for Euro area economies. Working Paper No. 1773, European Central Bank.
• Cho, J. S., T-H. Kim, and Y. Shin, 2015. Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188, 281-300.
• De Luca, G., J. R. Magnus, and F. Peracchi, 2015. On the ambiguous consequences of omitting variables. EIEF Working Paper 05/15.
• Gozgor, G., 2015. Causal relation between economic growth and domestic credit in the economic globalization: Evidence from the Hatemi-J's test. Journal of International Trade and Economic Development,  24, 395-408.
• Panhans, M. T. and J. D. Singleton, 2015. The empirical economist's toolkit: From models to methods. Working Paper 2015-03, Center for the History of Political Economy.
• Sanderson. E and F. Windmeijer, 2015. A weak instrument F-test in linear IV models with multiple endogenous variables. Discussion Paper 15/644, Department of Economics, University of Bristol.

© 2015, David E. Giles

June Reading List

• Andrews, I. and T. B. Armstrong, 2015. Unbiased instrumental variables estimation under known first-stage sign. Cowles Foundation Discussion Paper No. 1984R, Yale University,
• Bajari, P., D. Nekipelov, S. P. Ryan, and M. Yang. 2015. Demand estimation with machine learning and model combination. NBER Working Paper 20955.
• Chambers, M. J., 2015. A jackknife correction to a test for cointegration rank. Econometrics, 3, 355-375.
• Mazeu, J. H. G., E. Ruiz and H. Veiga, 2015. Model uncertainty and the forecast accuracy of ARMA models: A survey. UC3M Working Paper 15-08, Statistics and Econometrics, Universidad Carlos III de Madrid. 
• Paldam, M., 2015. Meta-analysis in a nutshell: Techniques and general findings. Economics, 9, 2015-11.
• Triacca, U., 2015. A pitfall in using the characterization of Granger non-causality in vector autoregressive models. Econometrics, 3, 233-239.

© 2015, David E. Giles