Macroeconomic Forecasting With Mixed Frequency Data
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Macroeconomic Forecasting With Mixed Frequency Data
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Author :
language : en
Publisher:
Release Date : 2007
Macroeconomic Forecasting With Mixed Frequency Data written by 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.
Macroeconomic Forecasting With Mixed Frequency Data
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Author : Michael P. Clements
language : en
Publisher:
Release Date : 2006
Macroeconomic Forecasting With Mixed Frequency Data written by Michael P. Clements and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Macroeconomic Forecasting In The Era Of Big Data
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Author : Peter Fuleky
language : en
Publisher: Springer Nature
Release Date : 2019-11-28
Macroeconomic Forecasting In The Era Of Big Data written by Peter Fuleky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Business & Economics categories.
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Essays On Forecasting Macroeconomic Variables Using Mixed Frequency Data
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Author : Kihwan Kim
language : en
Publisher:
Release Date : 2016
Essays On Forecasting Macroeconomic Variables Using Mixed Frequency Data written by Kihwan Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Economic forecasting categories.
This dissertation investigate the forecasting performance of mixed frequency factor models with mixed frequency dataset. In the first chapter, I consider the mixed fre- quency factor approach used in ADS (2009) to construct their co-incident activity index, and ask the question of whether a class of mixed frequency indexes is useful for predicting the future values of quarterly U.S. real GDP growth and monthly industrial production, unemployment and inflation. My forecasting assessment of the mixed frequency factor model is performed in conjunction with standard prediction models such as autoregression, multivariate distributed lag models, and diffu- sion index models of the variety examined by Stock and Watson (2002a). The main findings of the study are as follows. First, prediction models using only mixed frequency indexes show their best performance at short-term GDP forecasting horizons, and are particularly good during recessions. Second, prediction models using both mixed frequency indexes and diffusion indexes forecast monthly variables more accurately than models using single frequency type indexes. Third, model combi- nations perform relatively poorly in real GDP forecasting contexts, although they perform better when applied to the prediction of monthly variables. Fourth, survey information can be conveniently exploited with higher frequency variables such as daily and weekly variables, and mixed frequency indexes using such survey information are sometimes useful in forecasting lower frequency variables. In the second chapter, I evaluate the predictive performance of hybrid models for forecasting four economic variables. The hybrid approach takes into account the notion that simple autoregression and sophisticated factor models' predictive abilities may change according to the state of the econ- omy. I find that hybrid prediction models produce better forecasts than standard models and than combination models, in most cases, using the same menu of models discussed above. For example, in one-quarter ahead GDP forecasts, the best hybrid model reduces the mean squared forecast error of the best model combinations and the linear models by 14 and 11 percent, on average, respectively. More specifically, the mean squared forecast error of autoregression is reduced by approximately 35 percent. In 12-month ahead predictions of inflation, the best hybrid model improves the best model combinations and the linear models by 11 percent and 16 percent, on average, respectively. This number again increases, in this case to 36 percent, when comparing only with autoregression. One reason for these findings is that hybrid prediction models more effectively utilize survey information.
The Oxford Handbook Of Economic Forecasting
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Author : Michael P. Clements
language : en
Publisher: OUP USA
Release Date : 2011-07-08
The Oxford Handbook Of Economic Forecasting written by Michael P. Clements and has been published by OUP USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-08 with Business & Economics categories.
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Macroeconomic Forecasting Using Data Of Mixed Frequencies
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Author : MARK NISSEN GREENE
language : en
Publisher:
Release Date : 1982
Macroeconomic Forecasting Using Data Of Mixed Frequencies written by MARK NISSEN GREENE and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with categories.
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Mixed Frequency Modeling And Economic Forecasting
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Author : Clément Marsilli
language : en
Publisher:
Release Date : 2014
Mixed Frequency Modeling And Economic Forecasting written by Clément Marsilli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Economic downturn and recession that many countries experienced in the wake of the global financial crisis demonstrate how important but difficult it is to forecast macroeconomic fluctuations, especially within a short time horizon. The doctoral dissertation studies, analyses and develops models for economic growth forecasting. The set of information coming from economic activity is vast and disparate. In fact, time series coming from real and financial economy do not have the same characteristics, both in terms of sampling frequency and predictive power. Therefore short-term forecasting models should both allow the use of mixed-frequency data and parsimony. The first chapter is dedicated to time series econometrics within a mixed-frequency framework. The second chapter contains two empirical works that sheds light on macro-financial linkages by assessing the leading role of the daily financial volatility in macroeconomic prediction during the Great Recession. The third chapter extends mixed-frequency model into a Bayesian framework and presents an empirical study using a stochastic volatility augmented mixed data sampling model. The fourth chapter focuses on variable selection techniques in mixed-frequency models for short-term forecasting. We address the selection issue by developing mixed-frequency-based dimension reduction techniques in a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Our model succeeds in constructing an objective variable selection with broad applicability.
Using Mixed Frequency Data To Forecast Recessions And Gdp
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Author : Ryan Mitchell
language : en
Publisher:
Release Date : 2021
Using Mixed Frequency Data To Forecast Recessions And Gdp written by Ryan Mitchell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This dissertation studies how and if mixed frequency time series data should be used to forecast recessions in the US and Canada, as well as GDP in a selection of OECD countries. The first chapter combines daily and weekly financial data with monthly macroeconomic indicators in a mixed frequency probit (MFP) regression to forecast and nowcast US and Canadian recessions. This chapter adds to the existing literature in multiple ways, including; developing a mixed frequency binary model that could be helpful for topics outside of recession forecasting, examining how higher frequency data should be weighted in the context of recession prediction, as well as a methodology that can nowcast current economic conditions. Overall I find significant improvements in the forecasting and nowcasting accuracy of recessions when using mixed frequency data, compared to a benchmark model that aggregates data into the same frequency. The second chapter extends from the first to apply machine learning techniques to the same problem. I add to the existing literature by incorporating mixed frequency data directly into a classification artificial neural network (MF-ANN) as well as using novel cross validation methods to tune hyperparameters and carry out feature selection with time series data. Overall when comparing US recession forecasting results to the reduced form methodology of Chapter 1, I find mixed results. While some metrics indicate similar performance between the two methods, the ANN makes less extreme forecasting errors on average. The third chapter uses a seemingly unrelated regressions (SUR) approach with a mixed frequency framework to forecast GDP of 10 OECD countries. This chapter adds to the literature as a way to efficiently include cross country information in GDP forecasting equations, as well as being an effective methodology when the researcher is constrained by small sample sizes. Overall we find that SUR outperforms OLS for the majority of countries and forecasting horizons, however as sample sizes are extended this benefit is reduced.
Applied Economic Forecasting Using Time Series Methods
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Author : Eric Ghysels
language : en
Publisher: Oxford University Press
Release Date : 2018-03-23
Applied Economic Forecasting Using Time Series Methods written by Eric Ghysels and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-23 with Business & Economics categories.
Economic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable. Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online at authors' website.
Predictive Performance Of Mixed Frequency Nowcasting And Forecasting Models
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Author : Roberto S. Mariano
language : en
Publisher:
Release Date : 2020
Predictive Performance Of Mixed Frequency Nowcasting And Forecasting Models written by Roberto S. Mariano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.