So far, our emphasis has been on the interactive use of PcGets. However, because command-driven operation can be useful on occasion, both to document research (facilitating re-running crucial stages), and when preparing teaching sessions, PcGets supports a batch language, operated through GiveWin. Batch commands are issued in GiveWin, which then decides whether to handle any given command (e.g., data loading and saving, algebra, database selection etc.), and if not, which module to access.
PcGets can generate four types of batch code which are all accessible via the Test Menu:
Generates batch code to re-estimate the GUM with your current configuration of the PcGets algorithm. This will document the work done and allows you to replicate your results later on. It can also be useful if you are likely to consider changes to the specification of the GUM, or the way PcGets analyzed the data, by changing the model settings or the expert options. Note that this batch code can also be generated via the Progress dialog box or just by opening the Tools/Batch Editor in GiveWin after a successful estimation.
Generates batch code to re-estimate the Specific model selected. The estimation method is pre-set to OLS if the reduction resulted from GETS, and IVE in the case of GETSIVE. If many variables are unreliable in the selected model and further reductions are taken into consideration, it can be useful to rerun GETS or GETSIVE on the specific. Note that such a two-stage reduction process does not ensure that the finally selected model is a valid reduction of the original GUM.
Generates batch code which estimates the (specific) model with PcGive. This allows you to analyze the (specific) model using any additional features offered by PcGive. For example, it makes it easy to consider non-linear generalizations of the model.
Generates batch code to prepare for system estimation by PcGive. This makes it easy to perform equation-by-equation reductions of all the equations in a vector autoregression by PcGets, then use the system-estimation facilities of PcGive to obtain FIML estimates of the reduced system etc. An example is considered in Chapter 8.
In the following, the various batch codes are illustrated for the M1UK model considered earlier.
As an example, testimate a model of m on a Constant, m_1, p, p_1, y, y_1, R, and R_1, by Gets using the full sample. We use the Liberal research strategy and the default settings of the PcGets testimation algorithm.
Using data from 1963(2) to 1989(2), PcGets reports the following OLS estimation results for the GUM:
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with:
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In the GUM, the FAR(1-4) diagnostic test for serial autocorrelation is rejected at 5%, and the diagnostic for heteroscedasticity at 10%:
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PcGets finds the following parsimonious valid reduction of the GUM:
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with:
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As in the GUM, the diagnostic tests show potential problems with both the autocorrelation and heteroscedasticity of the residuals. As discussed in the previous chapters, the results would suggest starting with a more general GUM.
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After the (otherwise) successful testimation of the model, we use the Test Menu to generate the various batch codes.
Switch to GiveWin, and activate the batch editor via the Tools/Batch Editor (or use the toolbar icon), which should show:
//PcGets batch file
module("PcGets");
package("PcGets");
usedata("ukm1.in7");
system
{
Y = "m";
X = "Constant","m_1","p","p_1","y","y_1","R","R_1";
}
setdetectoutliers(0);
set0lagorder(1);
set0topdown(1);
set0bottomup(1);
setsplitsample(1);
setstrategy("lib",1);
setreporting(0);
estimate("GETS",1963,2,1989,2);
Should you wish to save the selected model as a batch file, after using GETS, click on Test, PcGets batch (specific) and switch to the GiveWin Results window, which should show (if you used the same settings as above):
//PcGets batch file
module("PcGets");
package("PcGets");
usedata("ukm1.in7");
system
{
Y = "m";
X = "Constant","m_1","p","y_1","R";
}
setdetectoutliers(0);
set0lagorder(1);
set0topdown(1);
set0bottomup(1);
setsplitsample(1);
setstrategy("lib",1);
setreporting(0);
estimate("OLS",1963,2,1989,2);
Edit the last line of the batch code and change it to:
estimate("GETS",1963,2,1989,2);
then cut and paste the lines to the GiveWin batch editor and run it (alternatively, highlight the lines and press Ctrl+B). The testimation process now starts from the selected reduction of the original GUM. As all coefficients of the new GUM are significant, no further reductions are feasible and PcGets reports the GUM as the final model. In general, such a two-stage reduction process need not lead to the same model selected in the first round. The reason is that the finally-selected model might not be a valid reduction of the original GUM.
Return to the original GUM by recalling the model in the data selection dialog, or by running the first generated batch code. Should you wish to analyze the selected model further with PcGive, click on Test, PcGive, which reports:
//PcGive batch file created by PcGets
module("PcGive");
package("PcGive");
usedata("ukm1.in7");
system
{
Y = "m";
Z = "Constant","m_1","p","y_1","R";
}
estimate("OLS",1963,2,1989,2, 0, 0);
Copy the code, switch to the GiveWin batch editor, paste, run and (if you have PcGive10 installed), PcGive is activated and returns its estimation output. All further features of PcGets are then to your disposal.
Should you wish to undertake system estimation of a VAR using PcGive, click on Test, PcFiml Batch which prints in the GiveWin results window:
//PcGive batch file created by PcGets
module("PcGive");
package("PcGive");
usedata("ukm1.in7");
system
{
Y = "m";
Z = "Constant","m_1","p","p_1","y","y_1","R","R_1";
}
model
{
m = "Constant","m_1","p","y_1","R";
}
estimate("FIML",1963,2,1989,2, 0, 0);
This code can be easily edited to run FIML estimation of systems of variables using PcGive. Chapter 8 illustrates the efficient use of batch commands to perform model selection of all the equations in a vector autoregression.
Chapter 16 documents the commands available for batch usage of PcGets. Consider again a model of m on a Constant, m_1, p, p_1, y, y_1, R, and R_1, estimated by OLS, using the maximum sample with 10 forecasts. Switch to GiveWin, and activate the batch editor via Tools/Batch Editor (or use the toolbar icon).
The edit dialog appears, with the current model already formulated in the batch language:
The complete batch code is:
module("PcGets");
package("PcGets");
usedata("ukm1.in7");
system
{
Y = m;
X = Constant, m_1, p, p_1, y, y_1, R, R_1;
}
expertsignificance(0.025, 0.01, 0.1, 0.01, 0.01, 0.0005);
expertpresearch(0.25, 0.95, 0.5, 0.05, 0.1, 0.05, 0.05,1);
expertblocksearch(1,1,1,1,1,1,1,0);
expertchoosespecific("sc");
expertsplitsample(0.05, 0.5, 0.2, 0.4, 0.4);
expertoutlierdection(3.00);
experttests(1,1,0,1,1,1,0);
experttestoptions(0.50,0.90,0,1,2,1,2);
setdetectoutliers(0);
set0lagorder(1);
set0topdown(1);
set0bottomup(1);
setsplitsample(1);
setstrategy("expert",1);
setreporting(1);
estimate("GETS", 1963, 2, 1989, 2, 10);
Add some algebra code to create Dm and Dp, and replace m and p in the model by these first differences, as shown in the capture below.
Press OK:run to execute the batch file. Notice that a different model is selected after the data transformations.
After execution, the batch commands are written to the results window, and can be re-run simply by highlighting them (or a valid subset thereof), then pressing Ctrl+B.
Once saved to disk, a batch file can also be run directly using File/Open, or by double clicking on the batch file in Windows Explorer or File Manager. Batch files have the .FL extension, which originally stood for Fiml Language.
As you are getting more familiar with PcGets, you might want to change the build-in Liberal and Conservative strategies according to your own ideas. In the following, we consider as a simple example the adjustment of the PcGets strategies for cross-section data by removing the AR test and the ARCH test from the standard test battery.
Following the procedure discussed in § 6.1, load the XsVars.in7 and XsVars.bn7 files in GiveWin. Then formulate a model. You can start with the model from the previous chapter. However we are not interested in the model itself, so you could specify any model of your choice. After the completion of the model formulation click on OK or press Enter to bring up the Model Settings dialog.
Select Expert user's strategy. All other options are irrelevant here, but you could again choose the same options as in the last tutorial. On the Estimate Model dialog (also referred to as GETS) press the Options button.
This opens the Expert User's Strategy Settings dialog. As we are going to design a `Liberal expert' strategy close to the one of PcGets, the first step is to reset the expert strategy to the Liberal strategy:
By pressing the OK button,
the expert strategy is reset to the Liberal, and the Estimate Model dialog reappears.
Again press the Options button:
Now change the PcGets' Liberal strategy. For example, choose a set of mis-specification tests relevant to cross-section data. Unmark any autocorrelation or ARCH tests, and mark normality, constancy, and heteroscedasticity as shown:
Press the OK button to accept,
and return to the Estimate Model dialog. Here select Testimation (GETS), and start the testimation process by pressing the OK button.
After the successful testimation, click on Test, PcGets batch (general)
and switch to the GiveWin Results window. From the printed batch code, you need the following lines:
module("PcGets");
package("PcGets");
expertsignificance(0.05, 0.1, 0.9, 0.1, 0.01, 0.005);
expertpresearch(0.9, 0.9, 0.75, 0.1, 0.1, 0.05, 0.05,1);
expertblocksearch(1,1,1,1,1,1,1,1);
expertchoosespecific("hq");
expertsplitsample(0.1, 0.75, 0.2, 0.4, 0.4);
expertoutlierdection( 2.56);
experttests(1,1,0,1,0,0,1);
experttestoptions(0.5, 0.9,12,1,4,1,4);
setdetectoutliers(1);
set0lagorder(0);
set0topdown(1);
set0bottomup(1);
setsplitsample(1);
setstrategy("expert",1);
setreporting(0);
Save these lines to a .FL file, and whenever you run the batch file using File/Open, or even by double clicking on the batch file in Windows Explorer or File Manager, PcGets will be reset to your preferred strategy. In the same way, you can proceed to produce a batch file with your personal Conservative strategy, or extend your set of personal strategies by creating batch files for sample-size dependent strategies. If you are used to working with batch files, you might find it easier to edit the batch files directly. The commands relevant for the expert settings are documented in §16.2.4.
Akaike, A. (1973). "Information theory and an extension of the maximum likelihood principle" In Petrov, B. N., and Saki, F. L.(eds.), Second International Symposium of Information Theory. Budapest.
Akaike, H. (1985). "Prediction and entropy" In Atkinson, A. C., and Fienberg, S. E.(eds.), A Celebration of Statistics, pp. 1--24. New York: Springer-Verlag.
Akerlof, G. A. (1979). "Irving Fisher on his head: The consequences of constant target-threshold monitoring of money holdings" Quarterly Journal of Economics, 93, 169--188.
Amemiya, T. (1980). "Selection of regressors" International Economic Review, 21, 331--354.
Anderson, T. W. (1971). The Statistical Analysis of Time Series. New York: John Wiley & Sons.
Andrews, D. W. K. (1991). "Heteroskedasticity and autocorrelation consistent covariance matrix estimation" Econometrica, 59, 817--858.
Banerjee, A., Dolado, J. J., Galbraith, J. W., and Hendry, D. F. (1993). Co-integration, Error Correction and the Econometric Analysis of Non-Stationary Data. Oxford: Oxford University Press.
Bårdsen, G. (1989). "The estimation of long run coefficients from error correction models" Oxford Bulletin of Economics and Statistics, 50.
Bean, C. R. (1977). "More consumers' expenditure equations" Academic panel paper (77)35, H.M. Treasury, London.
Bean, C. R. (1978). "The determination of consumers' expenditure in the UK" Government economic service working paper 4, H.M. Treasury, London.
Bontemps, C., and Mizon, G. E. (2001). "Congruence and encompassing" In Stigum, B.(ed.), Studies in Economic Methodology. Cambridge, Mass.: MIT Press.
Boswijk, H. P. (1992). Cointegration, Identification and Exogeneity, Vol. 37 of Tinbergen Institute Research Series. Amsterdam: Thesis Publishers.
Bowman, K. O., and Shenton, L. R. (1975). "Omnibus test contours for departures from normality based on Öb1 and b2" Biometrika, 62, 243--250.
Box, G. E. P., and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. First published, 1970.
Box, G. E. P., and Pierce, D. A. (1970). "Distribution of residual autocorrelations in autoregressive-integrated moving average time series models" Journal of the American Statistical Association, 65, 1509--1526.
Breusch, T. S. (1990). "Simplified extreme bounds" in Granger 1990, pp. 72--81.
Brown, R. L., Durbin, J., and Evans, J. M. (1975). "Techniques for testing the constancy of regression relationships over time (with discussion)" Journal of the Royal Statistical Society B, 37, 149--192.
Burns, A. F., and Mitchell, W. C. (1946). Measuring Business Cycles. New York: NBER.
Campos, J., and Ericsson, N. R. (1999). "Constructive data mining: Modeling consumers' expenditure in Venezuela" Econometrics Journal, 2, 226--240.
Carruth, A., and Henley, A. (1990). "Can existing consumption functions forecast consumer spending in the late 1980s?" Oxford Bulletin of Economics and Statistics, 52, 211--222.
Chatfield, C. (1995). "Model uncertainty, data mining and statistical inference" Journal of the Royal Statistical Society, A, 158, 419--466. With discussion.
Chow, G. C. (1960). "Tests of equality between sets of coefficients in two linear regressions" Econometrica, 28, 591--605.
Chow, G. C. (1981). "Selection of econometric models by the information criteria" In Charatsis, E. G.(ed.), Proceedings of the Econometric Society European Meeting 1979, Ch. 8. Amsterdam: North-Holland.
Clayton, M. K., Geisser, S., and Jennings, D. E. (1986). "A comparison of several model selection procedures" In Goel, P., and Zellner, A.(eds.), Bayesian Inference and Decision Techniques: Elsevier Science.
Clements, M. P., and Hendry, D. F. (1998). Forecasting Economic Time Series. Cambridge: Cambridge University Press.
Clements, M. P., and Hendry, D. F. (1999a). Forecasting Non-stationary Economic Time Series. Cambridge, Mass.: MIT Press.
Clements, M. P., and Hendry, D. F. (1999b). "Modelling methodology and forecast failure" Unpublished typescript, Economics Department, University of Oxford.
Coen, P. G., Gomme, E. D., and Kendall, M. G. (1969). "Lagged relationships in economic forecasting" Journal of the Royal Statistical Society A, 132, 133--163.
Cox, D. R. (1961). "Tests of separate families of hypotheses" In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 105--123 Berkeley: University of California Press.
Cox, D. R. (1962). "Further results on tests of separate families of hypotheses" Journal of the Royal Statistical Society B, 24, 406--424.
Cran, G. W., Martin, K. J., and Thomas, G. E. (1977). "A remark on algorithms. AS 63: The incomplete beta integral. AS 64: Inverse of the incomplete beta function ratio" Applied Statistics, 26, 111--112.
D'Agostino, R. B. (1970). "Transformation to normality of the null distribution of g1" Biometrika, 57, 679--681.
Davidson, J. E. H., and Hendry, D. F. (1981). "Interpreting econometric evidence: The behaviour of consumers' expenditure in the UK" European Economic Review, 16, 177--192. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Davidson, J. E. H., Hendry, D. F., Srba, F., and Yeo, J. S. (1978). "Econometric modelling of the aggregate time-series relationship between consumers' expenditure and income in the United Kingdom" Economic Journal, 88, 661--692. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Deaton, A. S. (1982). "Model selection procedures or, does the consumption function exist" In Chow, G. C., and Corsi, P.(eds.), Evaluating the Reliability of Macro-Economic Models, Ch. 5. New York: John Wiley.
Doan, T., Litterman, R., and Sims, C. A. (1984). "Forecasting and conditional projection using realistic prior distributions" Econometric Reviews, 3, 1--100.
Doornik, J. A. (1999). Object-Oriented Matrix Programming using Ox 3rd ed. London: Timberlake Consultants Press.
Doornik, J. A., and Hansen, H. (1994). "A practical test for univariate and multivariate normality" Discussion paper, Nuffield College.
Doornik, J. A., and Hendry, D. F. (1996). GiveWin: An Interactive Empirical Modelling Program. London: Timberlake Consultants Press.
Doornik, J. A., and Hendry, D. F. (2001a). Econometric Modelling using PcGive 10, Volume II. London: Timberlake Consultants Press.
Doornik, J. A., and Hendry, D. F. (2001b). Interactive Monte Carlo Experimentation in Econometrics using PcNaive. London: Timberlake Consultants Press.
Engle, R. F. (1982a). "Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation" Econometrica, 50, 987--1007.
Engle, R. F. (1982b). "Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation". 50, 987--1008.
Engle, R. F., Hendry, D. F., and Richard, J.-F. (1983). "Exogeneity" Econometrica, 51, 277--304. Reprinted in Hendry, D. F., Econometrics: Alchemy or Science? Oxford: Blackwell Publishers, 1993, and Oxford University Press, 2000; and in Ericsson, N. R. and Irons, J. S. (eds.) Testing Exogeneity, Oxford: Oxford University Press, 1994.
Engle, R. F., Hendry, D. F., and Trumbull, D. (1985). "Small sample properties of ARCH estimators and tests" Canadian Journal of Economics, 43, 66--93.
Engle, R. F., and White, H.(eds.)(1999). Cointegration, Causality and Forecasting. Oxford: Oxford University Press.
Ericsson, N. R. (1983). "Asymptotic properties of instrumental variables statistics for testing non-nested hypotheses" Review of Economic Studies, 50, 287--303.
Ericsson, N. R., Campos, J., and Tran, H.-A. (1990). "PC-GIVE and David Hendry's econometric methodology" Revista De Econometria, 10, 7--117.
Ericsson, N. R., Hendry, D. F., and Prestwich, K. M. (1998). "The demand for broad money in the United Kingdom, 1878--1993" Scandinavian Journal of Economics, 100, 289--324.
Ericsson, N. R., and Irons, J. S.(eds.)(1994). Testing Exogeneity. Oxford: Oxford University Press.
Faust, J., and Whiteman, C. H. (1997). "General-to-specific procedures for fitting a data-admissible, theory-inspired, congruent, parsimonious, encompassing, weakly-exogenous, identified, structural model of the DGP: A translation and critique" Carnegie--Rochester Conference Series on Public Policy, 47, 121--161.
Friedman, M., and Schwartz, A. J. (1982). Monetary Trends in the United States and the United Kingdom: Their Relation to Income, Prices, and Interest Rates, 1867--1975. Chicago: University of Chicago Press.
Frisch, R., and Waugh, F. V. (1933). "Partial time regression as compared with individual trends" Econometrica, 1, 221--223.
Gilbert, C. L. (1986). "Professor Hendry's econometric methodology" Oxford Bulletin of Economics and Statistics, 48, 283--307. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
Godfrey, L. G. (1978). "Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables" Econometrica, 46, 1303--1313.
Godfrey, L. G., and Orme, C. D. (1994). "The sensitivity of some general checks to omitted variables in the linear model" International Economic Review, 35, 489--506.
Godfrey, L. G., and Veale, M. R. (1999). "Alternative approaches to testing by variable addition" Mimeo, York University, UK.
Gourieroux, C., and Monfort, A. (1995). "Testing, encompassing, and simulating dynamic econometric models" Econometric Theory, 11, 195--228.
Granger, C. W. J. (1969). "Investigating causal relations by econometric models and cross-spectral methods" Econometrica, 37, 424--438.
Granger, C. W. J.(ed.)(1990). Modelling Economic Series. Oxford: Clarendon Press.
Haavelmo, T. (1944). "The probability approach in econometrics" Econometrica, 12, 1--118. Supplement.
Hannan, E. J., and Quinn, B. G. (1979). "The determination of the order of an autoregression" Journal of the Royal Statistical Society, B, 41, 190--195.
Harris, R. I. D. (1995). Using Cointegration Analysis in Econometric Modelling. London: Prentice Hall.
Harvey, A. C. (1981). The Econometric Analysis of Time Series. Deddington: Philip Allan.
Harvey, A. C. (1990). The Econometric Analysis of Time Series, 2nd ed. Hemel Hempstead: Philip Allan.
Hendry, D. F. (1976). "The structure of simultaneous equations estimators" Journal of Econometrics, 4, 51--88. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Hendry, D. F. (1979). "Predictive failure and econometric modelling in macro-economics: The transactions demand for money" In Ormerod, P.(ed.), Economic Modelling, pp. 217--242. London: Heinemann. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Hendry, D. F. (1980). "Econometrics: Alchemy or science?" Economica, 47, 387--406. Reprinted in Hendry, D. F., Econometrics: Alchemy or Science? Oxford: Blackwell Publishers, 1993, and Oxford University Press, 2000.
Hendry, D. F. (1985). "Monetary economic myth and econometric reality" Oxford Review of Economic Policy, 1, 72--84. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Hendry, D. F. (1994). "HUS revisited" Oxford Review of Economic Policy, 10, 86--106.
Hendry, D. F. (1995a). Dynamic Econometrics. Oxford: Oxford University Press.
Hendry, D. F. (1995b). "Econometrics and business cycle empirics" Economic Journal, 105, 1622--1636.
Hendry, D. F. (1996). "On the constancy of time-series econometric equations" Economic and Social Review, 27, 401--422.
Hendry, D. F. (1997). "On congruent econometric relations: A comment" Carnegie--Rochester Conference Series on Public Policy, 47, 163--190.
Hendry, D. F. (1999). "An econometric analysis of US food expenditure, 1931--1989" in Magnus, and Morgan 1999, pp. 341--361.
Hendry, D. F. (2000a). Econometrics: Alchemy or Science? Oxford: Oxford University Press. New Edition.
Hendry, D. F. (2000b). "Epilogue: The success of general-to-specific model selection" In Econometrics: Alchemy or Science?, pp. 467--490. Oxford: Oxford University Press. New Edition.
Hendry, D. F., and Doornik, J. A. (1994). "Modelling linear dynamic econometric systems" Scottish Journal of Political Economy, 41, 1--33.
Hendry, D. F., and Doornik, J. A. (1999). "The impact of computational tools on time-series econometrics" In Coppock, T.(ed.), Information Technology and Scholarship, pp. 257--269. Oxford: Oxford University Press.
Hendry, D. F., and Doornik, J. A. (2001). Econometric Modelling using PcGive 10: Volume I. London: Timberlake Consultants Press.
Hendry, D. F., and Ericsson, N. R. (1991a). "An econometric analysis of UK money demand in `Monetary Trends in the United States and the United Kingdom' by Milton Friedman and Anna J. Schwartz" American Economic Review, 81, 8--38.
Hendry, D. F., and Ericsson, N. R. (1991b). "Modeling the demand for narrow money in the United Kingdom and the United States" European Economic Review, 35, 833--886.
Hendry, D. F., and Krolzig, H.-M. (1999a). "General-to-specific model selection using PcGets for Ox" Unpublished paper, Economics Department, Oxford University.
Hendry, D. F., and Krolzig, H.-M. (1999b). "Improving on `Data mining reconsidered' by K.D. Hoover and S.J. Perez" Econometrics Journal, 2, 202--219.
Hendry, D. F., and Krolzig, H.-M. (2000). "The econometrics of general-to-simple modelling" Mimeo, Economics Department, Oxford University.
Hendry, D. F., Leamer, E. E., and Poirier, D. J. (1990). "A conversation on econometric methodology" Econometric Theory, 6, 171--261.
Hendry, D. F., and Mizon, G. E. (1978). "Serial correlation as a convenient simplification, not a nuisance: A comment on a study of the demand for money by the Bank of England" Economic Journal, 88, 549--563. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Hendry, D. F., and Mizon, G. E. (1990). "Procrustean econometrics: or stretching and squeezing data" in Granger 1990, pp. 121--136.
Hendry, D. F., and Mizon, G. E. (1993). "Evaluating dynamic econometric models by encompassing the VAR" In Phillips, P. C. B.(ed.), Models, Methods and Applications of Econometrics, pp. 272--300. Oxford: Basil Blackwell.
Hendry, D. F., and Mizon, G. E. (1999). "The pervasiveness of Granger causality in econometrics" in Engle, and White 1999.
Hendry, D. F., and Mizon, G. E. (2000). "Reformulating empirical macro-econometric modelling" Oxford Review of Economic Policy, 16, 138--159.
Hendry, D. F., and Morgan, M. S. (1995). The Foundations of Econometric Analysis. Cambridge: Cambridge University Press.
Hendry, D. F., Muellbauer, J. N. J., and Murphy, T. A. (1990). "The econometrics of DHSY" In Hey, J. D., and Winch, D.(eds.), A Century of Economics, pp. 298--334. Oxford: Basil Blackwell.
Hendry, D. F., and Neale, A. J. (1987). "Monte Carlo experimentation using PC-NAIVE" In Fomby, T., and Rhodes, G. F.(eds.), Advances in Econometrics, Vol. 6, pp. 91--125. Greenwich, Connecticut: Jai Press Inc.
Hendry, D. F., and Richard, J.-F. (1982). "On the formulation of empirical models in dynamic econometrics" Journal of Econometrics, 20, 3--33. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press and in Hendry D. F., op. cit., (1993) and (2000).
Hendry, D. F., and Richard, J.-F. (1989). "Recent developments in the theory of encompassing" In Cornet, B., and Tulkens, H.(eds.), Contributions to Operations Research and Economics. The XXth Anniversary of CORE, pp. 393--440. Cambridge, MA: MIT Press.
Hendry, D. F., and von Ungern-Sternberg, T. (1981). "Liquidity and inflation effects on consumers' expenditure" In Deaton, A. S.(ed.), Essays in the Theory and Measurement of Consumers' Behaviour, pp. 237--261. Cambridge: Cambridge University Press. Reprinted in Hendry, D. F., op. cit., (1993) and (2000).
Hendry, D. F., and Wallis, K. F.(eds.)(1984). Econometrics and Quantitative Economics. Oxford: Basil Blackwell.
Hoover, K. D., and Perez, S. J. (1999). "Data mining reconsidered: Encompassing and the general-to-specific approach to specification search" Econometrics Journal, 2, 167--191.
Jarque, C. M., and Bera, A. K. (1980). "Efficient tests for normality, homoscedasticity and serial independence of regression residuals" Economics Letters, 6, 255--259.
Johansen, S. (1988). "Statistical analysis of cointegration vectors" Journal of Economic Dynamics and Control, 12, 231--254. Reprinted in R.F. Engle and C.W.J. Granger (eds), Long-Run Economic Relationships, Oxford: Oxford University Press, 1991, 131--52.
Johansen, S. (1992). "Testing weak exogeneity and the order of cointegration in UK money demand" Journal of Policy Modeling, 14, 313--334.
Judge, G. G., and Bock, M. E. (1978). The Statistical Implications of Pre-Test and Stein-Rule Estimators in Econometrics. Amsterdam: North Holland Publishing Company.
Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., and Lee, T.-C. (1985). The Theory and Practice of Econometrics, 2nd ed. New York: John Wiley.
Kent, J. T. (1986). "The underlying nature of nonnested hypothesis tests" Biometrika, 73, 333--343.
Keynes, J. M. (1939). "Professor Tinbergen's method" Economic Journal, 44, 558--568.
Keynes, J. M. (1940). "Comment" Economic Journal, 50, 154--156.
Kiviet, J. F. (1985). "Model selection test procedures in a single linear equation of a dynamic simultaneous system and their defects in small samples" Journal of Econometrics, 28, 327--362.
Kiviet, J. F. (1986). "On the rigor of some mis-specification tests for modelling dynamic relationships" Review of Economic Studies, 53, 241--261.
Koopmans, T. C. (1947). "Measurement without theory" Review of Economics and Statistics, 29, 161--179.
Koopmans, T. C., Rubin, H., and Leipnik, R. B. (1950). "Measuring the equation systems of dynamic economics" In Koopmans, T. C.(ed.), Statistical Inference in Dynamic Economic Models, No. 10 in Cowles Commission Monograph, Ch. 2. New York: John Wiley & Sons.
Krolzig, H.-M. (2001). "General-to-specific reductions of vector autoregressive processes" Economics discussion paper 2000-W34, Nuffield College, Oxford..
Krolzig, H.-M., and Hendry, D. F. (2001). "Computer automation of general-to-specific model selection procedures" Journal of Economic Dynamics and Control, 25, 831--866.
Leamer, E. E. (1978). Specification Searches. Ad-Hoc Inference with Non-Experimental Data. New York: John Wiley.
Leamer, E. E. (1983a). "Let's take the con out of econometrics" American Economic Review, 73, 31--43. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
Leamer, E. E. (1983b). "Model choice and specification analysis" In Griliches, Z., and Intriligator, M. D.(eds.), Handbook of Econometrics, Vol. 1, Ch. 5. Amsterdam: North-Holland.
Leamer, E. E. (1984). "Global sensitivity results for generalized least squares estimates" Journal of the American Statistical Association, 79, 867--870.
Leamer, E. E. (1990). "Sensitivity analyses would help" in Granger 1990, pp. 88--96.
Ljung, G. M., and Box, G. E. P. (1978). "On a measure of lack of fit in time series models" Biometrika, 65, 297--303.
Lovell, M. C. (1983). "Data mining" Review of Economics and Statistics, 65, 1--12.
Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Berlin: Springer.
Magnus, J. R., and Morgan, M. S.(eds.)(1999). Methodology and Tacit Knowledge: Two Experiments in Econometrics. Chichester: John Wiley and Sons.
Majunder, K. L., and Bhattacharjee, G. P. (1973a). "Algorithm AS 63. The incomplete beta integral" Applied Statistics, 22, 409--411.
Majunder, K. L., and Bhattacharjee, G. P. (1973b). "Algorithm AS 64. Inverse of the incomplete beta function ratio" Applied Statistics, 22, 411--414.
Mayo, D. (1981). "Testing statistical testing" In Pitt, J. C.(ed.), Philosophy in Economics, pp. 175--230: D. Reidel Publishing Co. Reprinted as pp. 45--73 in Caldwell B. J. (1993), The Philosophy and Methodology of Economics, Vol. 2, Aldershot: Edward Elgar.
McAleer, M., Pagan, A. R., and Volker, P. A. (1985). "What will take the con out of econometrics?" American Economic Review, 95, 293--301. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
Mizon, G. E. (1977a). "Inferential procedures in nonlinear models: An application in a UK industrial cross section study of factor substitution and returns to scale" Econometrica, 45, 1221--1242.
Mizon, G. E. (1977b). "Model selection procedures" In Artis, M. J., and Nobay, A. R.(eds.), Studies in Modern Economic Analysis, pp. P97--120. Oxford: Basil Blackwell.
Mizon, G. E. (1984). "The encompassing approach in econometrics" in Hendry, and Wallis 1984, pp. 135--172.
Mizon, G. E. (1995). "Progressive modelling of macroeconomic time series: the LSE methodology" In Hoover, K. D.(ed.), Macroeconometrics: Developments, Tensions and Prospects, pp. 107--169. Dordrecht: Kluwer Academic Press.
Mizon, G. E., and Richard, J.-F. (1986). "The encompassing principle and its application to non-nested hypothesis tests" Econometrica, 54, 657--678.
Moore, H. L. (1914). Economic Cycles -- Their Law and Cause. New York: MacMillan.
Muellbauer, J. N. J. (1994). "The assessment: Consumer expenditure" Oxford Review of Economic Policy, 10, 1--41.
Nicholls, D. F., and Pagan, A. R. (1983). "Heteroscedasticity in models with lagged dependent variables" Econometrica, 51, 1233--1242.
Pagan, A. R. (1984). "Model evaluation by variable addition" in Hendry, and Wallis 1984, pp. 103--135.
Pagan, A. R. (1987). "Three econometric methodologies: A critical appraisal" Journal of Economic Surveys, 1, 3--24. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
Paroulo, P. (1996). "On the determination of integration indices in I(2) systems" Journal of Econometrics, 72, 313--356.
Pesaran, M. H. (1974). "On the general problem of model selection" Review of Economic Studies, 41, 153--171.
Pesaran, M. H.(ed.)(1987). The Limits of Rational Expectations. Oxford: Basil Blackwell.
Pike, M. C., and Hill, I. D. (1966). "Logarithm of the gamma function" Communications of the ACM, 9, 684.
Rahbek, A., Kongsted, H. C., and Jørgensen, C. (1999). "Trend-stationarity in the I(2) cointegration model" Journal of Econometrics, 90, 265--289.
Sargan, J. D. (1964). "Wages and prices in the United Kingdom: A study in econometric methodology (with discussion)" In Hart, P. E., Mills, G., and Whitaker, J. K.(eds.), Econometric Analysis for National Economic Planning, Vol. 16 of Colston Papers, pp. 25--63. London: Butterworth Co. Reprinted as pp. 275--314 in Hendry D. F. and Wallis K. F. (eds.) (1984). Econometrics and Quantitative Economics. Oxford: Basil Blackwell, and as pp. 124--169 in Sargan J. D. (1988), Contributions to Econometrics, Vol. 1, Cambridge: Cambridge University Press.
Sargan, J. D. (1973). "Model building and data mining" Discussion paper, London School of Economics. Presented to the Association of University Teachers of Economics, Meeting, Manchester, April 1973.
Sargan, J. D. (1980). "Some tests of dynamic specification for a single equation" Econometrica, 48, 879--897. Reprinted as pp. 191--212 in Sargan J. D. (1988), Contributions to Econometrics, Vol. 1, Cambridge: Cambridge University Press.
Sargan, J. D. (1981). "The choice between sets of regressors" Mimeo, Economics Department, London School of Economics.
Savin, N. E. (1984). "Multiple hypothesis testing" In Griliches, Z., and Intriligator, M. D.(eds.), Handbook of Econometrics, Vol. 2--3, Ch. 14. Amsterdam: North-Holland.
Sawa, T. (1978). "Information criteria for discriminating among alternative regression models" Econometrica, 46, 1273--1292.
Schwarz, G. (1978). "Estimating the dimension of a model" Annals of Statistics, 6, 461--464.
Shea, B. L. (1988). "Algorithm AS 239: Chi-squared and incomplete gamma integral" Applied Statistics, 37, 466--473.
Shenton, L. R., and Bowman, K. O. (1977). "A bivariate model for the distribution of Öb1 and b2" Journal of the American Statistical Association, 72, 206--211.
Shibata, R. (1980). "Asymptotically efficient selection of the order of the model for estimating parameters of a linear process" Annals of Statistics, 8, 147--164.
Sims, C. A. (1980). "Macroeconomics and reality" Econometrica, 48, 1--48. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press.
Sims, C. A., Stock, J. H., and Watson, M. W. (1990). "Inference in linear time series models with some unit roots" Econometrica, 58, 113--144.
Smith, G. W. (1986). "A dynamic Baumol-Tobin model of money demand" Review of Economic Studies, 53, 465--469.
Spanos, A. (1989). "On re-reading Haavelmo: A retrospective view of econometric modeling" Econometric Theory, 5, 405--429.
Sullivan, R., Timmermann, A., and White, H. (1998). "Dangers of data-driven inference: The case of calendar effects in stock returns" Mimeo, Economics Department, University of California at San Diego.
Summers, L. H. (1991). "The scientific illusion in empirical macroeconomics" Scandinavian Journal of Economics, 93, 129--148.
Teräsvirta, T. (1976). "Effect of feedback on the distribution of the portmanteau statistic" Manuscript, London School of Economics.
Theil, H. (1971). Principles of Econometrics. London: John Wiley.
Tinbergen, J. (1940a). Statistical Testing of Business-Cycle Theories. Geneva: League of Nations. Vol. I: A Method and its application to Investment Activity.
Tinbergen, J. (1940b). Statistical Testing of Business-Cycle Theories. Geneva: League of Nations. Vol. II: Business Cycles in the United States of America, 1919--1932.
Vuong, Q. H. (1989). "Likelihood ratio tests for model selection and nonnested hypotheses" Econometrica, 50, 1--25.
White, H. (1980). "A heteroskedastic-consistent covariance matrix estimator and a direct test for heteroskedasticity" Econometrica, 48, 817--838.
White, H. (1984). Asymptotic Theory for Econometricians. London: Academic Press.
White, H. (1990). "A consistent model selection" in Granger 1990, pp. 369--383.
Wooldridge, J. M. (1999). "Asymptotic properties of some specification tests in linear models with integrated processes" in Engle, and White 1999, pp. 366--384.
Wright, P. G. (1915). "Moore's economic cycles" Quarterly Journal of Economics, 29, 631--641.
Yancey, T. A., and Judge, G. G. (1976). "A Monte Carlo comparison of traditional and stein-rule estimators under squared error loss" Journal of Econometrics, 4, 285--294.