Relevant bibliographies by topics / GDP Forecasting

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Academic literature on the topic 'GDP Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Dissertations / Theses on the topic "GDP Forecasting"

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Björnfot, Fredrik. "GDP Growth Rate Nowcasting and Forecasting." Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-132951.

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The main purpose of this project was to help Swedbank get a better understandingof how gross domestic product growth rate develops in the future froma data set of macroeconomic variables. Since GDP values are released long aftera quarter has ended Swedbank would like to have a model that could predictupcoming GDP from these data sets. This was solved by a combination ofgrowth rate predictions from a dynamic factor model, a vector autoregressivemodel and two machine learning models. The predictions were combined usinga weighting method called system averaging model where the model predictionwit
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Lindgren, Hanna, and Victor Nilsson. "MIDAS : Forecasting quarterly GDP using higher-frequency data." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242554.

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We forecast US GDP sampled quarterly over horizons ranging from one quarter to three years. Using AR-MIDAS models we study three lag polynomials: the Almon lag, the exponential Almon lag and the beta lag, and nine macroeconomic variables, sampled weekly or monthly. Our benchmark model is an AR(1) and we compare forecast errors using RMSE. In all instances the AR-MIDAS achieves lower forecast errors compared to the benchmark model. The predictor sampled weekly generally performs better compared to other predictors, which are sampled monthly.
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Naser, Hanan. "An econometric investigation of forecasting GDP, oil prices, and relationships among GDP and energy sources." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7041/.

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In order for a policy to be effective, the links between the policy tools and the sub- sequent targets must be known, understandable, stable, and predictable. In this respect, this thesis is composed of three separate yet related empirical studies, that target important macroeconomic variables, which play a central role in the conduct of macroeconomic policies. First, simple regression estimates and a factor-based model are utilized to produce forecasts for Bahrain quarterly GDP growth using quarterly data-set that cover the period from 1995: Q1 to 2008: Q3. To do so, we run a simple regressio
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Pilström, Patrick, and Sebastian Pohl. "Forecasting GDP Growth : The Case of The Baltic States." Thesis, Jönköping University, JIBS, Economics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-9776.

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<p>The purpose of this thesis is to identify a general model to forecast GDP growth for the Baltic States, Estonia, Latvia and Lithuania. If the model provides reliable results for these states, then the model should be able to forecast GDP growth for other countries of interest. Forecasts are made by using a reduced vector autoregressive (VAR) model. The VAR models make use of past values of Gross Domestic Product-Inflation-Unemployment as explanatory variables.</p><p>The performed forecasts have provided good results for horizons up to t+8. The forecasts for 2009 (t+12) are in line with thos
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Campos, Claúdia Cristina Marinho. "Predicting GDP growth in the Euro Area." Master's thesis, NSBE - UNL, 2013. http://hdl.handle.net/10362/9837.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics<br>Predicting GDP growth is a concern of several economic agents. The right way to model such variable is far from consensual. This paper’s goal is to compare different models for GDP growth forecasting in the euro area. For comparative purposes, an autoregressive model (which is used as benchmark) and two Autoregressive Distributed Models (ADL), which contain financial and non-financial variables, chosen based on the literature, are used. The main
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Mattos, Pedro Montero. "Nowcasting Brazilian GDP." reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18775.

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Submitted by Pedro Montero Mattos (pmattos90@gmail.com) on 2017-09-05T14:09:34Z No. of bitstreams: 1 nowcasting-brazilian-gdp-ficha.pdf: 808279 bytes, checksum: 3b790fa6a2be106b618a354ab1f18650 (MD5)<br>Rejected by Joana Martorini (joana.martorini@fgv.br), reason: Prezado Pedro, boa noite, Seu trabalho não condiz com as normas necessárias para aprovação. Favor corrigir para que possamos aceitar o arquivo. Na capa faltou o nome completo da Escola, e ao identificar o local, na parte inferior da página, colocar somente o nome da cidade e o ano, retirar páginas em branco. Dissertação, banc
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Chen, Yu. "FORECASTING WITH MIXED FREQUENCY DATA:MIDAS VERSUS STATE SPACE DYNAMIC FACTOR MODEL : AN APPLICATION TO FORECASTING SWEDISH GDP GROWTH." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-29475.

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Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experime
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Enocksson, David. "Forecasting Swedish Output Growth: : An Empirical Comparison of MIDAS Regressions and theRAMSES Model." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-195873.

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This thesis compares MIDAS regressions, introduced in Ghysels, Santa-Clara and Valkanov (2002), to the RAMSES model, used by the Riksbank,in forecasting Swedish quarterly output growth (GDP). Using Swedish GPDdata from 1993Q1 and onwards, we compare forecasts for the period 2010Q1to 2011Q4, and we show that for longer forecast horizons (commonly usedby the Riksbank for monetary policy decisions), such as two and three yearsahead in time, the MIDAS regressions clearly outperforms the RAMSESmodel. For shorter horizons the results are not conclusive.KEYWORDS: MIDAS, GDP, forecasting, Sveriges Rik
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Adriansson, Nils, and Ingrid Mattsson. "Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?" Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243028.

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GDP is used to measure the economic state of a country and accurate forecasts of it is therefore important. Using the Economic Tendency Survey we investigate forecasting quarterly GDP growth using the data mining technique Random Forest. Comparisons are made with a benchmark AR(1) and an ad hoc linear model built on the most important variables suggested by the Random Forest. Evaluation by forecasting shows that the Random Forest makes the most accurate forecast supporting the theory that there are benefits to using Random Forests on economic time series.
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Lundberg, Otto. "GDP forecasting and nowcasting : Utilizing a system for averaging models to improve GDP predictions for six countries around the world." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-131718.

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This study was issued by Swedbank because they wanted too improve their GDP growth forecast capabilites.  A program was developed and tested on six countries; USA, Sweden, Germany, UK, Brazil and Norway. In this paper I investigate if I can reduce forecasting error for GDP growth by taking a smart average from a variety of models compared to both the best individual models and a random walk. I combine the forecasts from four model groups: Vector autoregression, principal component analysis, machine learning and random walk. The smart average is given by a system that give more weight to the pr
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