Three Essays On The Study Of Macroeconomic Variables Using Time Series Models
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Three Essays On The Study Of Macroeconomic Variables Using Time Series Models
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Author : Ting Qin
language : en
Publisher:
Release Date : 2007
Three Essays On The Study Of Macroeconomic Variables Using Time Series Models written by Ting Qin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Three Essays In Econometrics
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Author : Chaojun Li (Economist)
language : en
Publisher:
Release Date : 2020
Three Essays In Econometrics written by Chaojun Li (Economist) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Econometrics categories.
Regime-switching models have been applied extensively to study how time-series patterns change across different underlying economic states, such as boom and recession, high-volatility and low-volatility financial market environments, and active and passive monetary and fiscal policies. Among various models with regime switching, endogenous regime-switching models have the most general form of the regime process by allowing the determination of regimes to depend on the realizations of observations. The first chapter, jointly written with Yan Liu, proves consistency and asymptotic normality of the maximum likelihood estimator of the endogenous regime-switching models. The dynamic pattern of a time series may change abruptly as the underlying economic environment shifts and, at the same time, may also vary smoothly with other macroeconomic variables. The Markov-switching state-space model accommodates the two types of changes. For this class of models, it is computationally infeasible to calculate the exact likelihood function through the Kalman filter because of the path dependence on regimes. Approximation is widely applied in practice by truncating the path of regimes, but the statistical properties of the estimator based on approximation have not been examined. The second chapter fills the gap and shows consistency and asymptotic normality of the approximated maximum likelihood estimator. In the "big data" era, the large-dimensional factor model proves useful in extracting information from high-dimensional time series, by assuming a small number of factors can summarize the co-movement. In the third chapter, I propose a new method to estimate large-dimensional factor models with two types of structural breaks--in factor loadings and in the number of factors. Such breaks, if undetected, can lead to the estimation of pseudo factors instead of true factors. Compared to the existing method in the literature, the proposed method is computationally faster. Moreover, the estimated break ratios converge at a faster rate.
Three Essays In Asset Pricing
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Author : Alan Picard
language : en
Publisher:
Release Date : 2015
Three Essays In Asset Pricing written by Alan Picard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Abstract This dissertation consists of three essays. My first paper re-examines the link between idiosyncratic risk and expected returns for a large sample of firms in both developed and emerging markets. Recent studies using Fama-French three factor models have shown a negative relationship between idiosyncratic volatility and expected returns for developed markets. This relationship has not been studied to date for emerging markets. This study relates the current-month’s idiosyncratic volatility to the subsequent month’s returns for a sample of both developed and emerging markets expanding benchmark factors by including both a momentum and a systematic liquidity risk component. My second essay contributes to the important literature on the topic of the small capitalization stocks historical outperformance over large capitalization stocks by investigating the hypothesis that the small firm premium is related to macroeconomic and financial variables and that relationship is driven by the economic cycle in the United States and Canada. More specifically, this study employs recent advances in nonlinear time series models to explore the relationship between the small firm premium, and financial and macroeconomic variables in the Canadian and U.S. economies. My third paper re-examines the findings of a recent research paper that suggested that market wide liquidity may act as a leading indicator to the economic cycle. Using several liquidity measures and various macroeconomic variables to proxy for the economic conditions, the paper presents evidence that stock market liquidity could forecast business cycles: A major decrease in the overall level of market liquidity could indicate weak economic growth in the subsequent months. However, the drawback in the analysis is that the relationship is investigated in a linear approach even though it has been proven that most macroeconomic variables follow non-linear dynamics. Employing similar liquidity measures and macroeconomic proxies, and two popular econometrics models that account for non-linear behavior, this study hence re-investigates the relationship between stock market liquidity and business cycles.
Three Essays On Time Series Macroeconomics
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Author : Pedro H. Albuquerque
language : en
Publisher:
Release Date : 2022
Three Essays On Time Series Macroeconomics written by Pedro H. Albuquerque and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The first two chapters of this thesis propose new time-series methods and apply them to macroeconomic problems, while the third chapter evaluates the predictions of a dynamic general equilibrium model. The first chapter develops a practical log-linear aggregation procedure, which is applied to the heterogeneous growth problem in the U.S. The second chapter presents a simple nonparametric long-run correlation estimator with optimal lag-selection and alignment criteria, and uses it to measure interconnections between American and Latin-American stock returns. The third chapter uses a dynamic general equilibrium model to analyze the effects of bank account debits taxation. Time-series techniques are employed to empirically evaluate the model predictions. In the first chapter, a practical aggregation method for heterogeneous log-linear functions is presented. Inequality measures are employed in the construction of an exact representation of the aggregate behavior of an economy formed by heterogeneous log-linear agents. The exact aggregate representation is relatively simple and intuitive. It can be used thereafter in applied issues and in teaching, easing the solving and understanding of aggregation problems. Three macroeconomic applications are discussed: the aggregation of the Lucas supply function, the time-inconsistent behavior of an egalitarian social planner facing heterogeneous discount rates, and the case of a simple heterogeneous growth model. The latter application, which leads to a decomposition of growth rates of the mean into means of growth rates plus inequality changes, is explored empirically. Aggregate CPS data is used to show that, when inequality changes are taken in consideration, the slowdown that followed the first oil shock appears to be worse than usually thought. Additionally, the “new economy” growth resurgence seems less impressive when compared to the growth performance of the period that preceded the first oil shock. In the second chapter, a simple consistent nonparametric estimator of the long-run correlation between two variables is proposed, based on the estimation of the bivariate k-lag difference correlation. It is shown that the estimator is asymptotically equivalent to the Bartlett kernel spectral estimator of the complex coherency at frequency zero. The asymptotic distribution is derived, with a test for the absence of long-run correlation. Optimal lag-selection and alignment criteria are presented. Monte Carlo experiments show that the asymptotic approximations are satisfactory, sometimes even for small samples. They also reveal that the lag-selection and alignment criteria are effective. Long-run correlations between American and Latin-American stock returns are considered. The estimates increase substantially in the second half of the nineties. The results could indicate the presence of a correlation component common to Latin-American markets, which was important in the second half of the period but not in the first. The significant development of investment funds specialized in Latin-American markets and the much-improved foreign access after capital account liberalization in the region may be among the explanations for these patterns. The third chapter uses a dynamic general equilibrium model to study the economic effects of bank account debits (BAD) taxation. Australia and various Latin-American countries have levied or levy BAD taxes. Theoretical aspects such as tax cascading, financial disintermediation, market illiquidity, impacts on dividend and interest rates, tax revenue, government deficit, and effective rates on final transactions are considered. The Brazilian BAD tax (CPMF) experience is evaluated. The empirical analysis shows that revenue productivity appears to be very sensitive to the tax rate, engendering a Laffer curve. It is also shown that there may be impacts on real interest rates. Part of the BAD tax revenue can be lost due to increased interest payments on government debt. Furthermore, the deadweight losses seem to be significant if compared to revenues. Theory and evidence indicate that the BAD acronym is perhaps more than a witticism.
Volatility And Time Series Econometrics
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Author : Tim Bollerslev
language : en
Publisher: OUP Oxford
Release Date : 2010-02-11
Volatility And Time Series Econometrics written by Tim Bollerslev and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-11 with Business & Economics categories.
Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.
Essays In Empirical Macroeconomics
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Author : Dony Alex
language : en
Publisher:
Release Date : 2016
Essays In Empirical Macroeconomics written by Dony Alex and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
This thesis is a collection of three self contained chapters in the area of empirical macroeconomics. Chapter 2 examines the behaviour of the volatility of the structural shocks and the macroeconomic variables in the post-reform period in India in a time-varying framework. A time varying parameters structural vector autoregression with stochastic volatility model is used to investigate the evolving dynamics of the macroeconomy of India in the post-reform period. We detect a sharp reduction in estimated stochastic volatility during the post-reform years for all shocks and variables. In terms of the stochastic volatility, we find that the period 2001 to 2006 seems to have the lowest volatility in the whole sample and can be dubbed as the short 'Great Moderation' period of India. We find that the estimated stochastic volatility of supply shocks is more than the demand shocks. We also note that demand shocks rather seem to be persistent than supply shocks during the period from 2007-14. Chapter 3 explores the role of nominal GDP as an implicitly preferred monetary policy target in the US during the Great Moderation period. Monetary policy via stabilization of inflation expectations by targeting inflation, has been argued as one of the prominent factors contributing for the Great Moderation in the U.S. Studies using Taylor rule type monetary policy reaction functions have found inflation to be the major target variable of the Federal Reserve. This study counters this view, and shows that for accomplishing its objective of stabilizing inflation expectations, the Federal Reserve was instead implicitly targeting nominal GDP. This claim is corroborated by estimating different variants of nominal GDP rules, which then is compared with Taylor rules using both ex-post revised data and real time briefing forecasts of FOMC. The results counter the conventional view, and observe that post Volcker era or during the period of Great Moderation (1984-2007), the Federal Reserve had a stronger implicit preference for nominal GDP as compared to inflation Chapter 4 examines whether nominal GDP can pass the forecasting test to be a monetary policy framework. Forecast targeting became an important component of central banks from 1990's onwards as a systematic approach to monetary policy deliberations and as a good communication medium with the public. Any robust monetary policy regime has to have good forecasting performance of its nominal anchor. Nominal GDP targeting has been suggested as a suitable alternative to the present inflation 'targeting' monetary policy framework. But as a good framework its nominal anchor should have good forecasting ability. This chapter tries to compare the forecast performance between the nominal anchors of inflation and nominal GDP targeting regimes for U.S. This task is undertaken by using a series of models from simple autoregressive models to state space models. U.S Inflation is hard to forecast, but it seems that NGDP is much more harder to forecast.
Three Essays In Time Series Macroeconomics
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Author : Junichiro Ishida
language : en
Publisher:
Release Date : 2000
Three Essays In Time Series Macroeconomics written by Junichiro Ishida and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
The second chapter of the thesis considers the negative correlation between inflation and the average propensity to consume in the U.S. economy. While many explanations are offered for this observation, it is hard to be reconciled within the framework of a rational expectations model. In this paper, however, we argue that this correlation can be derived as an implication of the permanent income hypothesis. This conjecture is tested by identifying the dynamic response of consumption to different types of shock. The data show that this interpretation is largely consistent. This procedure also allows us to identify transitory consumption and the source of the failure of the permanent income hypothesis.
Three Essays In Time Series Econometrics
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Author : Christian Kascha
language : en
Publisher:
Release Date : 2007
Three Essays In Time Series Econometrics written by Christian Kascha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Econometrics categories.
Three Essays In Macroeconomic Dynamics
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Author : Hammad Qureshi
language : en
Publisher:
Release Date : 2009
Three Essays In Macroeconomic Dynamics written by Hammad Qureshi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Autoregression (Statistics) categories.
Abstract: This dissertation examines theoretical and empirical topics in macroeconomic dynamics. A central issue in macroeconomic dynamics is understanding the sources of business cycle fluctuations. The idea that expectations about future economic fundamentals can drive business cycles dates back to the early twentieth century. However, the standard real business cycle (RBC) model fails to generate positive comovement in output, consumption, labor-hours and investment in response to news shocks. My dissertation proposes a solution to this puzzling feature of the RBC model by developing a theoretical model that can generate positive aggregate and sectoral comovement in response to news shocks. Another key issue in macroeconomic dynamics is gauging the performance of theoretical models by comparing them to empirical models. Some of the most widely used empirical models in macroeconomics are level vector autoregressive (VAR) models. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. My dissertation investigates the frequency of explosive roots in estimated level VAR models using Monte Carlo simulations. Additionally, it proposes a way to mitigate explosive roots. Finally, as macroeconomic datasets are relatively short, empirical models such as autoregressive models (i.e. AR or VAR models) may have substantial small-sample bias. My dissertation develops a procedure that numerically corrects the bias in the roots of AR models. This dissertation consists of three essays. The first essay develops a model based on learning-by-doing (LBD) that can generate positive comovement in output, consumption, labor-hours and investment in response to news shocks. I show that the one-sector RBC model augmented by LBD can generate aggregate comovement in response to news shock about technology. Furthermore, I show that in the two-sector RBC model, LBD along with an intratemporal adjustment cost can generate sectoral comovement in response to news about three types of shocks: i) neutral technology shocks, ii) consumption technology shocks, and iii) investment technology shocks. I show that these results hold for contemporaneous technology shocks and for different specifications of LBD. The second essay investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary variables. Monte Carlo simulations based on datasets from the macroeconomic literature reveal that the frequency of explosive roots exceeds 40% in the presence of unit roots. Even when all the variables are stationary, the frequency of explosive roots is substantial. Furthermore, explosion increases significantly, to as much as 100% when the estimated level VAR coefficients are corrected for small-sample bias. These results suggest that researchers estimating level VAR models on macroeconomic datasets encounter explosive roots, a phenomenon that is contrary to common macroeconomic belief, with a very high frequency. Monte Carlo simulations reveal that imposing unit roots in the estimation can substantially reduce the frequency of explosion. Hence one way to mitigate explosive roots is to estimate vector error correction models. The third essay proposes a numerical procedure to correct the small-sample bias in autoregressive roots of univariate AR(p) models. I examine the median-bias properties and variability of the bias-adjusted parameters relative to the least-squares estimates. I show that the bias correction procedure substantially reduces the median-bias in impulse response functions. Furthermore, correcting the bias in roots significantly improves the median-bias in half-life, quarter-life and up-life estimates. The procedure pays a negligible-to-small price in terms of increased standard deviation for its improved median-bias properties.
Essays On Time Varying Volatility And Structural Breaks In Macroeconomics And Econometrics
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Author : Nyamekye Asare
language : en
Publisher:
Release Date : 2018
Essays On Time Varying Volatility And Structural Breaks In Macroeconomics And Econometrics written by Nyamekye Asare and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This thesis is comprised of three independent essays. One essay is in the field of macroeconomics and the other two are in time-series econometrics. The first essay, "Productivity and Business Investment over the Business Cycle", is co-authored with my co-supervisor Hashmat Khan. This essay documents a new stylized fact: the correlation between labour productivity and real business investment in the U.S. data switching from 0.54 to -0.1 in 1990. With the assistance of a bivariate VAR, we find that the response of investment to identified technology shocks has changed signs from positive to negative across two sub-periods: ranging from the time of the post-WWII era to the end of 1980s and from 1990 onwards, whereas the response to non-technology shocks has remained relatively unchanged. Also, the volatility of technology shocks declined less relative to the non-technology shocks. This raises the question of whether relatively more volatile technology shocks and the negative response of investment can together account for the decreased correlation. To answer this question, we consider a canonical DSGE model and simulate data under a variety of assumptions about the parameters representing structural features and volatility of shocks. The second and third essays are in time series econometrics and solely authored by myself. The second essay, however, focuses on the impact of ignoring structural breaks in the conditional volatility parameters on time-varying volatility parameters. The focal point of the third essay is on empirical relevance of structural breaks in time-varying volatility models and the forecasting gains of accommodating structural breaks in the unconditional variance. There are several ways in modeling time-varying volatility. One way is to use the autoregressive conditional heteroskedasticity (ARCH)/generalized ARCH (GARCH) class first introduced by Engle (1982) and Bollerslev (1986). One prominent model is Bollerslev (1986) GARCH model in which the conditional volatility is updated by its own residuals and its lags. This class of models is popular amongst practitioners in finance because they are able to capture stylized facts about asset returns such as fat tails and volatility clustering (Engle and Patton, 2001; Zivot, 2009) and require maximum likelihood methods for estimation. They also perform well in forecasting volatility. For example, Hansen and Lunde (2005) find that it is difficult to beat a simple GARCH(1,1) model in forecasting exchange rate volatility. Another way of modeling time-varying volatility is to use the class of stochastic volatility (SV) models including Taylor's (1986) autoregressive stochastic volatility (ARSV) model. With SV models, the conditional volatility is updated only by its own lags and increasingly used in macroeconomic modeling (i.e.Justiniano and Primiceri (2010)). Fernandez-Villaverde and Rubio-Ramirez (2010) claim that the stochastic volatility model fits better than the GARCH model and is easier to incorporate into DSGE models. However, Creal et al. (2013) recently introduced a new class of models called the generalized autoregressive score (GAS) models. With the GAS volatility framework, the conditional variance is updated by the scaled score of the model's density function instead of the squared residuals. According to Creal et al. (2013), GAS models are advantageous to use because updating the conditional variance using the score of the log-density instead of the second moments can improve a model's fit to data. They are also found to be less sensitive to other forms of misspecification such as outliers. As mentioned by Maddala and Kim (1998), structural breaks are considered to be one form of outliers. This raises the question about whether GAS volatility models are less sensitive to parameter non-constancy. This issue of ignoring structural breaks in the volatility parameters is important because neglecting breaks can cause the conditional variance to exhibit unit root behaviour in which the unconditional variance is undefined, implying that any shock to the variance will not gradually decline (Lamoureux and Lastrapes, 1990). The impact of ignoring parameter non-constancy is found in GARCH literature (see Lamoureux and Lastrapes, 1990; Hillebrand, 2005) and in SV literature (Psaradakis and Tzavalis, 1999; Kramer and Messow, 2012) in which the estimated persistence parameter overestimates its true value and approaches one. However, it has never been addressed in GAS literature until now. The second essay uses a simple Monte-Carlo simulation study to examine the impact of neglecting parameter non-constancy on the estimated persistence parameter of several GAS and non-GAS models of volatility. Five different volatility models are examined. Of these models, three --the GARCH(1,1), t-GAS(1,1), and Beta-t-EGARCH(1,1) models -- are GAS models, while the other two -- the t-GARCH(1,1) and EGARCH(1,1) models -- are not. Following Hillebrand (2005) who studied only the GARCH model, this essay examines the extent of how biased the estimated persistence parameter are by assessing impact of ignoring breaks on the mean value of the estimated persistence parameter. The impact of neglecting parameter non-constancy on the empirical sampling distributions and coverage probabilities for the estimated persistence parameters are also studied in this essay. For the latter, studying the effect on the coverage probabilities is important because a decrease in coverage probabilities is associated with an increase in Type I error. This study has implications for forecasting. If the size of an ignored break in parameters is small, then there may not be any gains in using forecast methods that accommodate breaks. Empirical evidence suggests that structural breaks are present in data on macro-financial variables such as oil prices and exchange rates. The potentially serious consequences of ignoring a break in GARCH parameters motivated Rapach and Strauss (2008) and Arouri et al. (2012) to study the empirical relevance of structural breaks in the context of GARCH models. However, the literature does not address the empirical relevance of structural breaks in the context of GAS models. The third and final essay contributes to this literature by extending Rapach and Strauss (2008) to include the t-GAS model and by comparing its performance to that of two non-GAS models, the t-GARCH and SV models. The empirical relevance of structural breaks in the models of volatility is assessed using a formal test by Dufour and Torres (1998) to determine how much the estimated parameters change over sub-periods. The in-sample performance of all the models is analyzed using both the weekly USD trade-weighted index between January 1973 and October 2016 and spot oil prices based on West Texas Intermediate between January 1986 and October 2016. The full sample is split into smaller subsamples by break dates chosen based on historical events and policy changes rather than formal tests. This is because commonly-used tests such as CUSUM suffer from low power (Smith, 2008; Xu, 2013). For each sub-period, all models are estimated using either oil or USD returns. The confidence intervals are constructed for the constant of the conditional parameter and the score parameter (or ARCH parameter in GARCH and t-GARCH models). Then Dufour and Torres's union-intersection test is applied to these confidence intervals to determine how much the estimated parameter change over sub-periods. If there is a set of values that intersects the confidence intervals of all sub-periods, then one can conclude that the parameters do not change that much. The out-of-sample performance of all time-varying volatility models are also assessed in the ability to forecast the mean and variance of oil and USD returns. Through this analysis, this essay also addresses whether using models that accommodate structural breaks in the unconditional variance of both GAS and non-GAS models will improve forecasts.