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Author:
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Category : Autoregression (Statistics)
Languages : en
Pages : 142
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"Financial and macroeconomic time series data are often nonstationary. My dissertation consists of three essays concerning time series models with nonstationarity. Chapter 1 develops a new jackknife estimator for nonstationary autoregressive model. The remaining two chapters explore the restricted maximum likelihood (REML hereafter) estimation and the restricted maximum likelihood based likelihood ratio test (RLRT hereafter) in predictive regression...."--Author's abstract.
Author:
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 142
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Book Description
"Financial and macroeconomic time series data are often nonstationary. My dissertation consists of three essays concerning time series models with nonstationarity. Chapter 1 develops a new jackknife estimator for nonstationary autoregressive model. The remaining two chapters explore the restricted maximum likelihood (REML hereafter) estimation and the restricted maximum likelihood based likelihood ratio test (RLRT hereafter) in predictive regression...."--Author's abstract.
Author: Xiaoye Li
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ISBN:
Category :
Languages : en
Pages : 123
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Author: Chor-Yiu Sin
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ISBN:
Category : Nonlinear theories
Languages : en
Pages : 292
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Author: Zhongyun Zhao
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Category :
Languages : en
Pages : 228
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Author: Jeffrey Harris Dorfman
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ISBN:
Category : Agriculture
Languages : en
Pages : 218
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Author: Naci Hüseyin Mocan
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ISBN:
Category : Time-series analysis
Languages : en
Pages : 210
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Author: Gerhard Fechteler
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Category :
Languages : en
Pages : 0
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Author: Seungmoon Choi
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ISBN:
Category :
Languages : en
Pages : 216
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Author: Fabio Gómez-Rodríguez
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ISBN:
Category : Finance
Languages : en
Pages : 0
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A time series is said to be non-stationary if the distribution of the random object that generates it changes over time. This dissertation studies models to describe non-stationary functional time series. Specifically, it considers functional autoregressions with unit-roots and functional regime-switching models.The first chapter of this dissertation briefly introduces functional time series. Then, it describes the functional autoregression model (FAR). Setting up this dissertation, I show how one can modify the FAR model to analyze non-stationary time series. Chapter 2 uses a functional autoregression model with unit roots to model the nominal yield curve. I answer the question: "How do the US government's decisions affect its borrowing costs?" I find that government spending raises the long-term end of the yield, increasing the borrowing costs. We consider a decomposition of government spending in consumption and investment. We find that investment spending increases the yields, especially in the yield curve's long-term end. On the other hand, consumption spending lowers the yield curve, particularly in the curve's short-term end. Chapter 3 analyzes the term structure of expected inflation (from 1-30 years). Using data from the Federal Reserve Bank of Cleveland, I use long-run restrictions to determine Monetary and Fiscal policy's effects on the term structure of expected inflation. Finally, I study the effects of Monetary and Fiscal policy on the distribution of inflation expectations. From survey data, I estimate density functions describing the distribution of inflation expectations. I model this time series as a functional autoregressive model with changes in the error term variance with two regimes, a volatile regime, and a stable regime. In response to contractionary monetary policy, the mean expected inflation decreases about three times more during the volatile period than during the stable period. Government spending increases the mean expected inflation, but this effect is only significant in the stable regime.
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Category :
Languages : ko
Pages :
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