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This thesis deals with different types of uncertainty in various macroeconomic contexts and investigates ways in which these can be accommodated by adopting flexible techniques that allow a robust inference in estimation, testing and prediction. This thesis covers a wide range of aspects in macroeconomic analysis, including the choice of an appropriate unit root test, inference when the presence of breaks and the autocorrelation properties of data are unknown, characterisation of inflation dynamics when structural and specification uncertainty are present, as well as model uncertainty in forecasting when real-time data are available.\ud Chapter 1 presents the general motivations and describes the main research objectives and methodology for each chapter, providing a thesis outline at the same time.\ud Chapter 2 examines the behaviour of OLS-demeaned/ detrended and GLS-demeaned/ detrended unit root tests that employ stationary covariates in situations where the\ud magnitude of the initial condition of the time series under consideration may be nonnegligible. We show that the asymptotic power of such tests is very sensitive to the initial condition; OLS- and GLS- based tests achieve relatively high power for large and small magnitudes of the initial condition, respectively. Combining information from both types of test via a simple union of rejections strategy is shown to effectively capture the\ud higher power available across all initial condition magnitudes.\ud In Chapter 3, we consider a two-step procedure for estimating level break size(s) when the presence of the structural break(s) is uncertain and when the order of integration of the data is unknown. In other words, we deal with uncertainty over the appropriate filtering of the data, as well as structural uncertainty over the existence of a break. Our approach is motivated by the well known interplay between the unit roots and structural changes: Evidence in favour of unit roots can be a manifestation of structural changes and vice versa. The proposed procedure is shown to exhibit substantial accuracy gains in estimating the level break-size and breakpoint.\ud Chapter 4 provides a characterisation of U.S. inflation dynamics within a generalised Phillips Curve framework that accommodates uncertainties about the duration a given Phillips Curve holds and the specification of the relationship, in addition to parameter and stochastic uncertainties accommodated within a typical Phillips Curve analysis. Our approach is based on an innovative method to deal with such uncertainties based on Bayesian model averaging techniques. Employing data for the U.S. in the period 1950q1- 2012q4, the estimated version of the "meta" Phillips Curve provides an interesting characterisation of inflation dynamics which is in accordance with a number of distinguished studies.\ud Chapter 5 investigates the extent to which nowcast and forecast performance is enhanced by the use of real-time datasets that incorporate past data vintages and survey data on expectations in addition to the most recent data. The paper proposes a modelling framework and evaluation procedure which allow a real-time assessment and a final assessment of the use of revisions and survey data judged according to a variety of statistical and economic criteria. Both survey data and revisions data are found to be important in calculating density forecasts in forecasting the occurrence of business cycle events. Through a novel "fair bet" exercise, it is shown that models that incorporate survey and/or revisions data achieve higher profits in decision-making. The analysis also highlights the need to focus on future growth and inflation dynamics relevant to decision-makers rather than relying on simple point forecasts.
1. Andrews, D.W.K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, pp.821-856.
2. Andrews, D.W.K. and W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62 (6), pp.1383-1414.
3. Carrion-i-Silvestre, J.L., D. Kim, and P. Perron (2009), "GLS-based unit root tests with multiple structural breaks both under the null and the alternative hypotheses," Econometric Theory, 25, pp.1754-1792.
4. Croushore, D. (2010), "An Evaluation of Ination Forecasts from Surveys using Real-Time Data," The BE Journal of Macroeconomics: Contributions, 10(1), pp.1-32.
5. Dickey, D.A. and W.A. Fuller (1979), "Distribution of the estimators for autoregressive time series with a unit root," Journal of the American Statistical Association, 74, pp.427-431.
6. Elliott, G. and M. Jansson (2003), "Testing for unit roots with stationary covariates," Journal of Econometrics, 115, pp.75-89.
7. Elliott, G., T. J. Rothenberg and J. H. Stock (1996), "E¢ cient Tests for an Autoregressive Unit Root," Econometrica, 64 (4), pp.813-836.
8. Fuhrer, J. and G. Moore (1995a), "Monetary policy trade-o¤ and the correlation between nominal interest rates and real output," American Economic Review, 85(1), pp.219-239.
9. Fuhrer, J. and G. Moore (1995b), "Ination Persistence," Quarterly Journal of Economics, 110, pp.127-159.
12. Garratt, A., K. Lee, E. Mise and K. Shields (2008), "Real time representations of the output gap," Review of Economics and Statistics, 90(4), pp.792-804.
13. Granger, C.W.J and M. Machina (2006), "Forecasting and Decision Theory," in G. Elliott, C.W.J. Granger and A.Timmermann (eds.), Handbook of Economic Forecasting, pp.81-98.
14. Granger, C.W.J. and M.H. Pesaran (2000), "Economic and Statistical Measures of Forecast Accuracy," Journal of Forecasting, 19, pp.537-560.
15. Hansen, B. E. (1995), "Rethinking the univariate approach to unit root testing using covariates to increase power," Econometric Theory, 11, pp.1148-1171.
16. Hansen, B. E. (2009), "Averaging estimators for regressions with a possible structural break," Econometric Theory, 35, pp.1498-1514.
17. Harvey, D. I. and S.J. Leybourne (2013), "Break Date Estimation for Models with Deterministic Structural Change," Oxford Bulletin of Economics and Statistics, 0305-9049, pp.1-20.
19. Harvey, D.I, S.J. Leybourne and A.M.R. Taylor (2012), "Unit root testing under a local break in trend," Journal of Econometrics, 167(1), pp. 140-167.
20. Harvey, D.I, S.J. Leybourne and A.M.R. Taylor (2009a), "Unit root testing in practice: Dealing with uncertainty over the trend and initial condition," Econometric Theory, 25, pp.587- 636.
21. Harvey, D.I, S.J. Leybourne and A.M.R. Taylor (2009b), "Simple, robust and powerful tests of the breaking trend hypothesis", Econometric Theory, Vol.25, pp.995-1029.
22. Harvey, D.I. and P. Newbold (2005), "Forecast Encompassing and Parameter Estimation," Oxford Bulletin of Economics and Statistics (Supplement), 67, pp.815-835.
23. Jacobs, J. and S. van Norden (2011), "Modeling Data Revisions: Measurement Error and Dynamics of True Values," Journal of Econometrics, 161, 2, pp.101-109.
24. Koenig, E. F., S. Dolmas and J. Piger (2003) "The use and abuse of real-time data in economic forecasting," The Review of Economics and Statistics, 85(3), pp.618-628.
25. Lee, K., Morley J. and Shields K. (2015), "The meta Taylor Rule," Journal of Money, Credit and Banking, Vol.47, Issue 1, pp.73-98.
26. Matheson, T.D., J. Mitchell and B. Silverstone (2010), "Nowcasting and predicting data revisions using panel survey data," Journal of Forecasting, 29(3), pp.313-330.
27. Müller, U.K. and G. Elliott (2003), "Tests for unit roots and the initial condition," Econometrica, 71, pp.1269-1286.
28. Perron, P. (1989), "The great crash, the oil price shock and the unit root hypothesis," Econometrica 57, pp.1361-1401.
29. Perron, P. and T. Yabu (2009), "Testing for shifts in trend with an integrated or stationary noise component," Journal of Business and Economic Statistics, Vol.27, pp.369-396.
30. Sbordone, A. (2002), "Prices and Unit Labor Costs: A New Test of Price Stickiness," Journal of Monetary Economics, 49(2), pp.265-292.
31. Vogelsang, T.J. (1998), "Testing for a shift in mean without having to estimate serialcorrelation parameters", Journal of Business and Economic Statistics, Vol.16, pp.73-80.
32. Westerlund, J. (2013), "A computationally convenient unit root test with covariates, conditional heteroskedasticity and e¢ cient detrending," Journal of Time Series Analysis, 34, pp.477-495.
18. Croushore, D. (2010), "An Evaluation of Ination Forecasts from Surveys using Real-Time Data," The BE Journal of Macroeconomics: Contributions, 10(1), pp.1-32.
62. Stock, J. and M.W. Watson (2006), "Forecasting with Many Predictors," in G. Elliott, C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North Holland, pp.515-554.
63. Timmermann, A. (2006),"Forecast Combinations," In Elliott, G., C.W.J. Granger and A. Timmermann (Eds.), Handbook of Economic Forecasting (pp. 135-196). Amsterdam: North Holland.
64. Vogelsang, T.J. (1998), "Testing for a shift in mean without having to estimate serialcorrelation parameters", Journal of Business and Economic Statistics, Vol.16, pp.73-80.