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Title: Three essays in applied econometrics
Author: Lin, Yanjun
ISNI:       0000 0004 6421 8631
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2016
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This thesis presents three essays in the field of applied econometrics. In the first essay, we use the establishment-level Annual Respondents Database (ARD) data and the sector-level Confederation of British Industry (CBI) Industrial Trends Survey data to identify the key determinants of U.K. manufacturing investment. We first examine the trends in the ARD microdata aggregates, the relative price of investment goods data, and the CBI survey data. Subsequently, we estimate a baseline dynamic error correction investment model which separates out short-run and long-run investment dynamics. When we introduce additional variables derived from the CBI survey data to the baseline model, the estimation results show that survey variables pertaining to financing constraints and demand uncertainty have negative effects on investment, while the survey variable related to the volume of total new orders has a positive effect on investment. In the second essay, we develop forecasting models for aggregate U.K. manufacturing investment. After assessing the CBI's forecasting record over the recent financial crisis, we conclude that CBI forecasters were slow in realizing the severe negative effect of the credit crisis on manufacturing investment. Subsequently, we develop our own baseline error-correction forecasting model, which conditions only on lagged explanatory variables, and apply the general-to-specific modeling approach to simplify the model. However, the selected baseline specification has poor out-of-sample forecast properties over the crisis period. When we include additional CBI survey variables in the baseline model, there is an improvement in the out-ofsample forecast performance in most cases. Survey measures of business optimism and expected future demand are found to be particularly useful in this context. Finally, in the third essay, we employ a Threshold Vector Autoregression (TVAR) model to examine the potentially nonlinear impact of fiscal stimulus on output under tight and loose credit supply conditions in the U.S. In our main specification, we choose the excess bond premium as the threshold variable to identify periods of tight credit and loose credit. The empirical results suggest that government spending increases are more effective at stimulating output than tax cuts, especially when credit conditions are loose.
Supervisor: Bond, Stephen R. ; Bowdler, Christopher Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Investments ; Econometrics