Predictive Performance Of Mixed Frequency Nowcasting And Forecasting Models
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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.
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.
Global Economic Modeling
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Author : Pauly Peter
language : en
Publisher: World Scientific
Release Date : 2018-04-24
Global Economic Modeling written by Pauly Peter and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-24 with Business & Economics categories.
Global econometric models have a long history. From the early 1970s to the present, as modeling techniques have advanced, different modeling paradigms have emerged and been used to support national and international policy making. One purpose of this volume -- based on a conference in recognition of the seminal impact of Nobel Prize winner in Economic Sciences Lawrence R Klein, whose pioneering work has spawned the field of international econometric modeling -- is to survey these developments from today's perspective. A second objective of the volume is to shed light on the wide range of attempts to broaden the scope of modeling on an international scale. Beyond new developments in traditional areas of the trade and financial flows, the volume reviews new approaches to the modeling of linkages between macroeconomic activity and individual economic units, new research on the analysis of trends in income distribution and economic wellbeing on a global scale, and innovative ideas about modeling the interactions between economic development and the environment. With the expansion of elaborated economic linkages, this volume makes an important contribution to the evolving literature of global econometric models.
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.
Methods For Pastcasting Nowcasting And Forecasting Using Factor Midas
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Author : Hyun Hak Kim
language : en
Publisher:
Release Date : 2017
Methods For Pastcasting Nowcasting And Forecasting Using Factor Midas written by Hyun Hak Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
We utilize mixed frequency factor-MIDAS models for the purpose of carrying out pastcasting, nowcasting, and forecasting experiments using real-time data. We also introduce a new real-time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor-MIDAS prediction models. Our key empirical findings are that: (i) When using real-time data, factor-MIDAS prediction models outperform various linear benchmark models.Interestingly, the MSFE-best MIDAS models contain no AR lag terms when pastcasting and nowcasting. AR terms only begin to play a role in true forecasting contexts. (ii) Models that utilize only 1 or 2 factors are MSFE-best at all forecasting horizons, but not at any pastcasting and nowcasting horizons. In these latter contexts, much more heavily parameterized models with many factors are preferred. (iii) Real-time data are crucial for forecasting Korean GDP, and the use of first available versus most recent data strongly affects model selection and performance. (iv) Recursively estimated models are almost always MSFE-best, and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our MSFE-best factor-MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.
U Midas
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Author : Claudia Foroni
language : en
Publisher:
Release Date : 2011
U Midas written by Claudia Foroni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
The Oxford Handbook Of Economic Forecasting
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Author : Michael P. Clements
language : en
Publisher: Oxford University Press
Release Date : 2011-06-29
The Oxford Handbook Of Economic Forecasting written by Michael P. Clements 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 2011-06-29 with Business & Economics categories.
This Handbook provides up-to-date coverage of both new and well-established fields in the sphere of economic forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter, and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in forecasting, in terms of the frequency of observations, the number of variables, and the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and economic analysis to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, methods for forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with economic forecasting, such as climate change, health economics, long-horizon growth forecasting, and political elections. Econometric forecasting has important contributions to make in these areas along with how their developments inform the mainstream.
Midas Versus Mixed Frequency Var
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Author : Vladimir Kuzin
language : en
Publisher:
Release Date : 2009
Midas Versus Mixed Frequency Var written by Vladimir Kuzin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Forecasting With Mixed Frequency Time Series Models
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Author : Klaus Wohlrabe
language : en
Publisher:
Release Date : 2008
Forecasting With Mixed Frequency Time Series Models written by Klaus Wohlrabe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Bayesian Econometric Methods
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Author : Joshua Chan
language : en
Publisher: Cambridge University Press
Release Date : 2019-08-15
Bayesian Econometric Methods written by Joshua Chan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-15 with Business & Economics categories.
Illustrates Bayesian theory and application through a series of exercises in question and answer format.