Relevant bibliographies by topics / Macroeconomics – Forecasting

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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  6. Reports

Academic literature on the topic 'Macroeconomics – Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Macroeconomics – Forecasting"

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Chatziantoniou, Ioannis, Stavros Degiannakis, Bruno Eeckels, and George Filis. "Forecasting tourist arrivals using origin country macroeconomics." Applied Economics 48, no. 27 (December 29, 2015): 2571–85. http://dx.doi.org/10.1080/00036846.2015.1125434.

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Maldonado, Isabel, and Carlos Pinho. "Yield curve dynamics with macroeconomic factors in Iberian economies." Global Journal of Business, Economics and Management: Current Issues 10, no. 3 (November 26, 2020): 193–203. http://dx.doi.org/10.18844/gjbem.v10i3.4691.

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Abstract The aim of this paper is to analyse the bidirectional relation between the term structure of interest rates components and macroeconomic factors. Using a factor augmented vector autoregressive model, impulse response functions and forecasting error variance decompositions we find evidence of a bidirectional relation between yield curve factors and the macroeconomic factors, with increased relevance of yield factors over it with increased forecasting horizons. The study was conduct for the two Iberian countries using information of public debt interest rates of Spain and Portugal and macroeconomic factors extracted from a set of macroeconomic variables, including indicators of activity, prices and confidence. Results show that the inclusion of confidence and macroeconomic factors in the analysis of the relationship between macroeconomics and interest rate structure is extremely relevant. The results obtained allow us to conclude that there is a strong impact of changes in macroeconomic factors on the term structure of interest rates, as well as a significant impact factors of the term structure in the future evolution of macroeconomic factors.
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Diebold, F. X., and Kenneth D. West. "Forecasting and empirical methods in finance and macroeconomics." Journal of Econometrics 105, no. 1 (November 2001): 1–3. http://dx.doi.org/10.1016/s0304-4076(01)00067-7.

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Li, Cao. "Macroeconomic Short-Term High-Precision Combined Forecasting Algorithm Based on Grey Model." Security and Communication Networks 2021 (September 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/7026064.

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Using the characteristics of grey forecasting, which requires a small amount of sample data and a simple modeling process, to predict the main macroeconomic indicators in the early stage, combined with the filtering decomposition method and the production function method, establishes a short-term high-precision combination forecasting algorithm for macroeconomics based on the grey model. The algorithm uses the improved HP filter method in the HP filter method to study whether the potential economic growth rate can be more accurately measured, and the production function method is used to calculate the potential economic growth rate. First, the two methods are used to calculate the potential economic growth rate. The accuracy of this method finally established a combined model based on the two models for short-term forecasting. Under the premise of considering economic factors, the input data is preprocessed, and the high-precision combined forecast is used to finally obtain the macroeconomic forecast results. The calculation examples in the paper show that the method is feasible and effective.
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Kurovskiy, Gleb. "Using Textual Information to Predict In Macroeconomics." Moscow University Economics Bulletin 2019, no. 6 (December 30, 2019): 39–58. http://dx.doi.org/10.38050/01300105201965.

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The paper shows how textual information can be used to predict and study cause-effect relationships in macroeconomics. I consider a special case of forecasting - nowcasting on the example of unemployment. The key feature of nowcasting is that the forecast is built for a period that has already passed, but which has not yet come out statistics. As textual information, Internet requests are used. The paper is new in several direction. For the first time in the literature, information from two search engines, Yandex and Google, is used at once for forecasting. Information provided by search engines complements each other and allows performing suitable words’ selection from the bunch of users’ internet-requests. For the first time, the popularity of online systems as sources of information on job availability is taken into account. In Russia, the popularity of the Internet as a source of information on the availability of jobs has more than tripled from 2008 to 2018. If the researcher uses only the dynamics of related internet-requests then the results will show the dynamics of internetservices’ popularity rather than unemployment. Most of the models with internet query words show significant quality improvement in fore(now)casting unemployment. The paper proposes the procedure how to use query data for macroeconomic nowcasting
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Syamsudin, Moch. "Pengujian Kembali Volatilitas Kebijakan Trilemma Terhadap Variabel Makroekonomi di Indonesia." Jurnal Ekonomi Akuntansi dan Manajemen 20, no. 1 (April 8, 2021): 1. http://dx.doi.org/10.19184/jeam.v20i1.18880.

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The trilemma policy is a hypothesis stating a Mundell-Fleming macroeconomic development framework in which there is a state that cannot simultaneously choose three policies because it must sacrifice one policy so that the realization of policies that leads to economic stability is desired. The research aims to see the effect of the policy volatility on macroeconomic variables in Indonesia. The method used is the vector error correction model (VECM). The results show that the volatility of the trilemma policy adopted by Indonesia in the short and long term Affects the rate of economic growth and inflation. Economic shocks and uncertainties in the world economy externally affect macroeconomic variables. Viewing the results of forecasting for the trilemma policy and macroeconomic variables show that the inflation rate is so high and the level of economic openness is very low. This result recommends that there is a need for harmonization of policies undertaken by Bank Indonesia as the monetary authority and the government as a fiscal authority so as to achieve the level of financial stability that impacts on economic stability. Keywords: Trilemma Policy, Macroeconomics, Vector Error Correction Model (VECM), Forecasting
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Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (November 23, 2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.

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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using ARIMA model where the modeling results state that gold price fluctuations as white noise. Multivariate gold price modeling is done using Vector Error Correction Model with gold, oil, US Dollar and Dow Jones indices, and inflation rate as predictors. The results showed that the VECM model has been able to model the gold price well and all the factors studied influenced the gold price. The US dollar and oil prices are negatively correlated with gold prices, while the inflation rate is positively correlated with gold prices. The Dow Jones index was positively correlated with gold prices in just two periods.
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Fischer, Charles C. "On the Design and Use of Forecasting Experiments in Teaching Macroeconomics." Simulation & Gaming 22, no. 1 (March 1991): 75–82. http://dx.doi.org/10.1177/1046878191221006.

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Britton, Andrew. "Seasonal Patterns in the British Economy." National Institute Economic Review 117 (August 1986): 33–42. http://dx.doi.org/10.1177/002795018611700105.

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Applied macroeconomists commonly regard the seasonal variations of the economy as a hindrance rather than a help to the understanding of behaviour. Thus both in commenting informally on economic developments and in the more formal tasks of modelbuilding and forecasting seasonally-adjusted data are almost invariably used in preference to raw data when both are published. The nature of the patterns displayed by seasonal variation is very little discussed. One purpose of this note is simply to describe seasonal variation as it is estimated in some of the official data series, providing some tables which may be useful for general reference. But the aim is not just descriptive. It will be argued that seasonal variations may throw useful light on some controversial issues in macroeconomics.
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Diebold, Francis X., and Kenneth D. West. "Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance: Editors' Introduction." International Economic Review 39, no. 4 (November 1998): 811. http://dx.doi.org/10.2307/2527339.

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Dissertations / Theses on the topic "Macroeconomics – Forecasting"

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Heidari, Hassan Economics Australian School of Business UNSW. "Essays on macoroeconomics and macroeconomic forecasting." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/22800.

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This dissertation collects three independent essays in the area of Macroeconomics and Macroeconomic forecasting. The first chapter introduces and motivates the three essays. Chapter 2 highlights a serious problem of the Bayesian vector autoregressive (BVAR) models with Litterman???s prior cannot be used to get accurate forecasts of the driftless variables in a mixed drift models. BVAR models with Litterman???s prior, because of the diffuse prior on the constant, do not perform well in the long-run forecasting of I(1) variables either, if they have no drift. This is interesting as in practice most of the macro models include both drift and driftless variables. One solution to this problem is using the Bewley (1979) transformation to impose zero drift to driftless variables in a mixed drift VAR models. A novel feature of this chapter is the use of g-prior in BVAR models to alleviate poor estimation of drift parameters of the Traditional BVAR model. Chapter 3 deals with another possible explanation for the poor performance of the Traditional BVAR models in inflation forecasting. BVAR with Litterman???s prior have the disadvantage of a lack of robustness to deterministic shifts, exacerbated by the ill-determination of the intercept. Several structural break tests show that Australian inflation has breaks in the mean. Chapter 3 uses the Kalman filter to allow parameters to vary over time. The novelty of this chapter is modifying the standard BVAR model, where deterministic components evolve over time. Moreover, this chapter set aside the assumption of diagonality in the prior variance-covariance. Hence, another novelty of this chapter is using a BVAR model with modified non-diagonal variance-covariance matrix similar to the g-prior, where the deterministic components are the only source of variation, to forecast Australian inflation. Chapter 4 moves onto DSGE models and estimates a partially microfunded small-open economy (SOE) New-Keynesian model of the Australian economy. In this chapter, structural parameters of the rest of world (ROW), SOE, and closed economy, are estimated using Australian data as the small economy, and the US as the ROW, with the full information maximum likelihood.
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Liu, Dandan. "Essays on macroeconomics and forecasting." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4271.

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This dissertation consists of three essays. Chapter II uses the method of structural factor analysis to study the effects of monetary policy on key macroeconomic variables in a data rich environment. I propose two structural factor models. One is the structural factor augmented vector autoregressive (SFAVAR) model and the other is the structural factor vector autoregressive (SFVAR) model. Compared to the traditional vector autogression (VAR) model, both models incorporate far more information from hundreds of data series, series that can be and are monitored by the Central Bank. Moreover, the factors used are structurally meaningful, a feature that adds to the understanding of the “black box” of the monetary transmission mechanism. Both models generate qualitatively reasonable impulse response functions. Using the SFVAR model, both the “price puzzle” and the “liquidity puzzle” are eliminated. Chapter III employs the method of structural factor analysis to conduct a forecasting exercise in a data rich environment. I simulate out-of-sample real time forecasting using a structural dynamic factor forecasting model and its variations. I use several structural factors to summarize the information from a large set of candidate explanatory variables. Compared to Stock and Watson (2002)’s models, the models proposed in this chapter can further allow me to select the factors structurally for each variable to be forecasted. I find advantages to using the structural dynamic factor forecasting models compared to alternatives that include univariate autoregression (AR) model, the VAR model and Stock and Watson’s (2002) models, especially when forecasting real variables. In chapter IV, we measure U.S. technology shocks by implementing a dual approach, which is based on more reliable price data instead of aggregate quantity data. By doing so, we find the relative volatility of technology shocks and the correlation between output fluctuation and technology shocks to be much smaller than those revealed in most real-business-cycle (RBC) studies. Our results support the findings of Burnside, Eichenbaum and Rebelo (1996), who showed that the correlation between technology shocks and output is exaggerated in the RBC literature. This suggests that one should examine other sources of fluctuations for a better understanding of the business cycle phenomena.
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De, Antonio Liedo David. "Structural models for macroeconomics and forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210142.

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This Thesis is composed by three independent papers that investigate

central debates in empirical macroeconomic modeling.

Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the data

revisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are based

on the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for the

DPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminary

figures, it is not possible for them to quantify it, as done by our model.

The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.

Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparable

to that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.

The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGE

models. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and

Sala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach which

models explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGE

modeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than that

resulting from the original specification.
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Schwarzmüller, Tim [Verfasser]. "Essays in Macroeconomics and Forecasting / Tim Schwarzmüller." Kiel : Universitätsbibliothek Kiel, 2016. http://d-nb.info/1102204021/34.

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Brinca, Pedro Soares. "Essays in Quantitative Macroeconomics." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-92861.

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In the first essay, Distortions in the Neoclassical Growth Model: A Cross Country Analysis, I show that shocks that express themselves as total factor productivity and labor income taxes are comparably more synchronized than shocks that resemble distortions to the ability of allocating resources across time and states of the world. These two shocks are also the most important to model. Lastly, I document the importance of international channels of transmission for the shocks, given that these are spatially correlated and that international trade variables, such as trade openness correlate particularly well with them. The second essay is called Monetary Business Cycle Accounting for Sweden. Given that the analysis is focused in one country, I can extend the prototype economy to include a nominal interest rate setting rule and government bonds. As in the previous essay, distortions to the labor-leisure condition and total factor productivity are the most relevant margins to be modeled, now joined by deviations from the nominal interest rate setting rule. Also, distortions do not share a structural break during the Great Recession, but they do during the 1990’s.  Researchers aiming to model Swedish business cycles must take into account the structural changes the Swedish economy went through in the 1990’s, though not so during the last recession. The third essay, Consumer Confidence and Consumption Spending: Evidence for the United States and the Euro Area, we show that, the consumer confidence index can be in certain circumstances a good predictor of consumption. In particular, out-of-sample evidence shows that the contribution of confidence in explaining consumption expenditures increases when household survey indicators feature large changes, so that confidence indicators can have some increasing predictive power during such episodes. Moreover, there is some evidence of a confidence channel in the international transmission of shocks, as U.S. confidence indices help predicting consumer sentiment in the euro area.
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Galimberti, Jaqueson Kingeski. "Adaptive learning for applied macroeconomics." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-learning-for-applied-macroeconomics(cde517d7-d552-4a53-a442-c584262c3a8f).html.

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The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms to depict the evolution of agents' beliefs over time. In this thesis we assess this practice from an applied perspective, focusing on the use of such algorithms for the computation of forecasts of macroeconomic variables. Our analysis develops around three issues we find to have been previously neglected in the literature: (i) the initialization of the learning algorithms; (ii) the determination and calibration of the learning gains, which are key parameters of the algorithms' specifications; and, (iii) the choice of a representative learning mechanism. In order to approach these issues we establish an estimation framework under which we unify the two main algorithms considered in this literature, namely the least squares and the stochastic gradient algorithms. We then propose an evaluation framework that mimics the real-time process of expectation formation through learning-to-forecast exercises. To analyze the quality of the forecasts associated to the learning approach, we evaluate their forecasting accuracy and resemblance to surveys, these latter taken as proxy for agents' expectations. In spite of taking these two criteria as mutually desirable, it is not clear whether they are compatible with each other: whilst forecasting accuracy represents the goal of optimizing agents, resemblance to surveys is indicative of actual agents behavior. We carry out these exercises using real-time quarterly data on US inflation and output growth covering a broad post-WWII period of time. Our main contribution is to show that a proper assessment of the adaptive learning approach requires going beyond the previous views in the literature about these issues. For the initialization of the learning algorithms we argue that such initial estimates need to be coherent with the ongoing learning process that was already in place at the beginning of our sample of data. We find that the previous initialization methods in the literature are vulnerable to this requirement, and propose a new smoothing-based method that is not prone to this critic. Regarding the learning gains, we distinguish between two possible rationales to its determination: as a choice of agents; or, as a primitive parameter of agents learning-to-forecast behavior. Our results provide strong evidence in favor of the gain as a primitive approach, hence favoring the use of surveys data for their calibration. In the third issue, about the choice of a representative algorithm, we challenge the view that learning should be represented by only one of the above algorithms; on the basis of our two evaluation criteria, our results suggest that using a single algorithm represents a misspecification. That motivate us to propose the use of hybrid forms of the LS and SG algorithms, for which we find favorable evidence as representatives of how agents learn. Finally, our analysis concludes with an optimistic assessment on the plausibility of adaptive learning, though conditioned to an appropriate treatment of the above issues. We hope our results provide some guidance on that respect.
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Arora, Siddharth. "Time series forecasting with applications in macroeconomics and energy." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c763b735-e4fa-4466-9c1f-c3f6daf04a67.

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The aim of this study is to develop novel forecasting methodologies. The applications of our proposed models lie in two different areas: macroeconomics and energy. Though we consider two very different applications, the common underlying theme of this thesis is to develop novel methodologies that are not only accurate, but are also parsimonious. For macroeconomic time series, we focus on generating forecasts for the US Gross National Product (GNP). The contribution of our study on macroeconomic forecasting lies in proposing a novel nonlinear and nonparametric method, called weighted random analogue prediction (WRAP) method. The out-of-sample forecasting ability of WRAP is evaluated by employing a range of different performance scores, which measure its accuracy in generating both point and density forecasts. We show that WRAP outperforms some of the most commonly used models for forecasting the GNP time series. For energy, we focus on two different applications: (1) Generating accurate short-term forecasts for the total electricity demand (load) for Great Britain. (2) Modelling Irish electricity smart meter data (consumption) for both residential consumers and small and medium-sized enterprises (SMEs), using methods based on kernel density (KD) and conditional kernel density (CKD) estimation. To model load, we propose methods based on a commonly used statistical dimension reduction technique, called singular value decomposition (SVD). Specifically, we propose two novel methods, namely, discount weighted (DW) intraday and DW intraweek SVD-based exponential smoothing methods. We show that the proposed methods are competitive with some of the most commonly used models for load forecasting, and also lead to a substantial reduction in the dimension of the model. The load time series exhibits a prominent intraday, intraweek and intrayear seasonality. However, most existing studies accommodate the ‘double seasonality’ while modelling short-term load, focussing only on the intraday and intraweek seasonal effects. The methods considered in this study accommodate the ‘triple seasonality’ in load, by capturing not only intraday and intraweek seasonal cycles, but also intrayear seasonality. For modelling load, we also propose a novel rule-based approach, with emphasis on special days. The load observed on special days, e.g. public holidays, is substantially lower compared to load observed on normal working days. Special day effects have often been ignored during the modelling process, which leads to large forecast errors on special days, and also on normal working days that lie in the vicinity of special days. The contribution of this study lies in adapting some of the most commonly used seasonal methods to model load for both normal and special days in a coherent and unified framework, using a rule-based approach. We show that the post-sample error across special days for the rule-based methods are less than half, compared to their original counterparts that ignore special day effects. For modelling electricity smart meter data, we investigate a range of different methods based on KD and CKD estimation. Over the coming decade, electricity smart meters are scheduled to replace the conventional electronic meters, in both US and Europe. Future estimates of consumption can help the consumer identify and reduce excess consumption, while such estimates can help the supplier devise innovative tariff strategies. To the best of our knowledge, there are no existing studies which focus on generating density forecasts of electricity consumption from smart meter data. In this study, we evaluate the density, quantile and point forecast accuracy of different methods across one thousand consumption time series, recorded from both residential consumers and SMEs. We show that the KD and CKD methods accommodate the seasonality in consumption, and correctly distinguish weekdays from weekends. For each application, our comprehensive empirical comparison of the existing and proposed methods was undertaken using multiple performance scores. The results show strong potential for the models proposed in this thesis.
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Ward, Felix [Verfasser]. "Essays in International Macroeconomics and Financial Crisis Forecasting / Felix Ward." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1167856899/34.

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Xue, Jiangbo. "A structural forecasting model for the Chinese macroeconomy /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECON%202009%20XUE.

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Ricci, Lorenzo. "Essays on tail risk in macroeconomics and finance: measurement and forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/242122.

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This thesis is composed of three chapters that propose some novel approaches on tail risk for financial market and forecasting in finance and macroeconomics. The first part of this dissertation focuses on financial market correlations and introduces a simple measure of tail correlation, TailCoR, while the second contribution addresses the issue of identification of non- normal structural shocks in Vector Autoregression which is common on finance. The third part belongs to the vast literature on predictions of economic growth; the problem is tackled using a Bayesian Dynamic Factor model to predict Norwegian GDP.Chapter I: TailCoRThe first chapter introduces a simple measure of tail correlation, TailCoR, which disentangles linear and non linear correlation. The aim is to capture all features of financial market co- movement when extreme events (i.e. financial crises) occur. Indeed, tail correlations may arise because asset prices are either linearly correlated (i.e. the Pearson correlations are different from zero) or non-linearly correlated, meaning that asset prices are dependent at the tail of the distribution.Since it is based on quantiles, TailCoR has three main advantages: i) it is not based on asymptotic arguments, ii) it is very general as it applies with no specific distributional assumption, and iii) it is simple to use. We show that TailCoR also disentangles easily between linear and non-linear correlations. The measure has been successfully tested on simulated data. Several extensions, useful for practitioners, are presented like downside and upside tail correlations.In our empirical analysis, we apply this measure to eight major US banks for the period 2003-2012. For comparison purposes, we compute the upper and lower exceedance correlations and the parametric and non-parametric tail dependence coefficients. On the overall sample, results show that both the linear and non-linear contributions are relevant. The results suggest that co-movement increases during the financial crisis because of both the linear and non- linear correlations. Furthermore, the increase of TailCoR at the end of 2012 is mostly driven by the non-linearity, reflecting the risks of tail events and their spillovers associated with the European sovereign debt crisis. Chapter II: On the identification of non-normal shocks in structural VARThe second chapter deals with the structural interpretation of the VAR using the statistical properties of the innovation terms. In general, financial markets are characterized by non- normal shocks. Under non-Gaussianity, we introduce a methodology based on the reduction of tail dependency to identify the non-normal structural shocks.Borrowing from statistics, the methodology can be summarized in two main steps: i) decor- relate the estimated residuals and ii) the uncorrelated residuals are rotated in order to get a vector of independent shocks using a tail dependency matrix. We do not label the shocks a priori, but post-estimate on the basis of economic judgement.Furthermore, we show how our approach allows to identify all the shocks using a Monte Carlo study. In some cases, the method can turn out to be more significant when the amount of tail events are relevant. Therefore, the frequency of the series and the degree of non-normality are relevant to achieve accurate identification.Finally, we apply our method to two different VAR, all estimated on US data: i) a monthly trivariate model which studies the effects of oil market shocks, and finally ii) a VAR that focuses on the interaction between monetary policy and the stock market. In the first case, we validate the results obtained in the economic literature. In the second case, we cannot confirm the validity of an identification scheme based on combination of short and long run restrictions which is used in part of the empirical literature.Chapter III :Nowcasting NorwayThe third chapter consists in predictions of Norwegian Mainland GDP. Policy institutions have to decide to set their policies without knowledge of the current economic conditions. We estimate a Bayesian dynamic factor model (BDFM) on a panel of macroeconomic variables (all followed by market operators) from 1990 until 2011.First, the BDFM is an extension to the Bayesian framework of the dynamic factor model (DFM). The difference is that, compared with a DFM, there is more dynamics in the BDFM introduced in order to accommodate the dynamic heterogeneity of different variables. How- ever, in order to introduce more dynamics, the BDFM requires to estimate a large number of parameters, which can easily lead to volatile predictions due to estimation uncertainty. This is why the model is estimated with Bayesian methods, which, by shrinking the factor model toward a simple naive prior model, are able to limit estimation uncertainty.The second aspect is the use of a small dataset. A common feature of the literature on DFM is the use of large datasets. However, there is a literature that has shown how, for the purpose of forecasting, DFMs can be estimated on a small number of appropriately selected variables.Finally, through a pseudo real-time exercise, we show that the BDFM performs well both in terms of point forecast, and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg Survey, and that it outperforms the predictions published by the Norges Bank in its monetary policy report.
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Books on the topic "Macroeconomics – Forecasting"

1

Inc, NetLibrary, ed. Macroeconomic forecasting: A sociological appraisal. London: Routledge, 2002.

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Chipika, Jesimen. Macroeconomic modeling and forecasting manual. Harare, Zimbabwe: Macroeconomic and Financial Management Institute of Eastern and Southern Africa, 2012.

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Economic fluctuations and forecasting. New York: HarperCollins College Publishers, 1996.

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Siviero, Stefano. Macroeconomic forecasting: Debunking a few old wives' tales. [Roma]: Banca d'Italia, 2001.

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Macro-economic forecasting: A sociological appraisal. London: Routledge, 1999.

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Boivin, Jean. Are more data always better for factor analysis? Cambridge, Mass: National Bureau of Economic Research, 2003.

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Vlatko, Ćurković, Škegro Borislav, Ekonomski institut Zagreb, Samoupravna interesna zajednica znanosti SR Hrvatske., Republički zavod za društveno planiranje SR Hrvatske., and Znanstvene osnove dugoročnog društveno-ekonomskog razvoja Hrvatske., eds. Globalna analiza i projekcija dinamike i strukture razvoja. Zagreb: Samoupravna interesna zajednica znanosti Hrvatske, 1990.

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Lamont, Owen A. Macroeconomic forecasts and microeconomic forecasters. Cambridge, MA: National Bureau of Economic Research, 1995.

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Backus, David. Cracking the conundrum. Cambridge, Mass: National Bureau of Economic Research, 2007.

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Backus, David. Cracking the conundrum. Cambridge, MA: National Bureau of Economic Research, 2007.

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Book chapters on the topic "Macroeconomics – Forecasting"

1

Holden, K. "Macroeconomic Forecasting." In Current Issues in Macroeconomics, 163–81. London: Palgrave Macmillan UK, 1989. http://dx.doi.org/10.1007/978-1-349-20286-7_8.

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Ward, Benjamin. "Macroeconomics: Theorem-Seeking, Forecasting Failure." In Dionysian Economics, 53–65. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/9781137597366_7.

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Gandolfo, Giancarlo, Pier Carlo Padoan, and Giuseppe de Arcangelis. "The Theory of Exchange Rate Determination, and Exchange Rate Forecasting." In Open-Economy Macroeconomics, 332–52. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-12884-6_18.

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Ozaki, Tohru, and Valerie H. Ozaki. "Statistical Identification of Nonlinear Dynamics in Macroeconomics Using Nonlinear Time Series Models." In Statistical Analysis and Forecasting of Economic Structural Change, 345–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-662-02571-0_22.

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Buckmann, Marcus, Andreas Joseph, and Helena Robertson. "Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting." In Data Science for Economics and Finance, 43–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_3.

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AbstractWe present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.
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Carnot, Nicolas, Vincent Koen, and Bruno Tissot. "Macroeconomic Models." In Economic Forecasting, 133–54. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815_6.

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Watson, Mark W. "Macroeconomic Forecasting." In The New Palgrave Dictionary of Economics, 1–3. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2434-1.

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Watson, Mark W. "Macroeconomic Forecasting." In The New Palgrave Dictionary of Economics, 8094–96. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2434.

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Cohen, Gerald D. "Macroeconomic Theory and Forecasting." In The Palgrave Handbook of Government Budget Forecasting, 11–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18195-6_2.

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Bassetti, Federico, Roberto Casarin, and Francesco Ravazzolo. "Density Forecasting." In Macroeconomic Forecasting in the Era of Big Data, 465–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31150-6_15.

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Conference papers on the topic "Macroeconomics – Forecasting"

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Nguyen, Hien T., and Duc Trung Nguyen. "Transfer Learning for Macroeconomic Forecasting." In 2020 7th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2020. http://dx.doi.org/10.1109/nics51282.2020.9335848.

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Cook, Thomas R., and Aaron Smalter Hall. "Macroeconomic Indicator Forecasting with Deep Neural Networks." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8571.

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Plevokaitė, Jurgita, and Raimonda Martinkutė-Kaulienė. "Estimation of Investment Perspectives in the Baltic Stock Market." In Contemporary Issues in Business, Management and Education. VGTU Technika, 2015. http://dx.doi.org/10.3846/cibme.2015.01.

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Short analysis of stock market and stock indices of Baltic countries is presented in the article. Theoretical aspects of importance of fundamental economic analysis, presented by Lithuanian and foreign authors for investigation of investment market is analysed and presented in the research. Research of correlation analysis and stochastic dependence test between chosen stock indices and macroeconomic indicators of Baltic countries is fulfilled. After analysis of the 2004–2013 year period statistics, the relationship between macroeconomic indicators and stock indices in the long term is established. After evaluating the results of the research, macroeconomic indicators, mostly influencing the changes in Baltic stock markets are picked out and their influence on stock indices is described. Investment perspectives in the Baltic stock market are estimated in the near future using macroeconomic forecastings of every country.
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Yu-Ge Xu, Fei Luo, and Zhi-Ming Chen. "Macroeconomic forecasting algorithm based on novel adaptive neural network." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635784.

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Karaganis, Anastasios, and Vasiliki Vlachostergiou. "Forecasting the Greek office price index using macroeconomic leading indicators." In 24th Annual European Real Estate Society Conference. European Real Estate Society, 2017. http://dx.doi.org/10.15396/eres2017_311.

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Turner, David. "The use of models in macroeconomic forecasting at the OECD." In Conference on Global Economic Modeling. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813220447_0003.

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Mehrotra, Anupam, and Alka Munjal. "Leveraging Technology in Central Banking: Macroeconomic Forecasting & Managing Volatility." In 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 2021. http://dx.doi.org/10.1109/iccike51210.2021.9410771.

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Waduge, Nisal, and Upeksha Ganegoda. "Forecasting Stock Price of a Company Considering Macroeconomic Effect from News Events." In 2018 3rd International Conference on Information Technology Research (ICITR). IEEE, 2018. http://dx.doi.org/10.1109/icitr.2018.8736133.

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Tarasov, A. N. "ON THE COMPOSITION OF CONCEPTS IN COGNITIVE MODELS OF RURAL DEVELOPMENT." In STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS Volume 2. DSTU-Print, 2020. http://dx.doi.org/10.23947/interagro.2020.2.200-202.

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Modern macroeconomic conditions and long-term global political trends make us pay closer attention to maintaining social control over the country's vast rural areas, including the abandoned In the 1990s, the land in the production of food, the provision of recreational services, other economic and social activities, which will allow Russia to maintain national control over the country's renewable natural resources. This is possible by choosing the best mix of factors and institutions that ensure managed rural development. Choosing these is possible based on the results of forecasting; sociological and expert surveys, monitoring of rural society.
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Abounoori, Esmaiel, and Afsaneh Ghasemi Tazehabadi. "Forecasting Stock Price Using Macroeconomic Variables: A Hybrid ARDL, ARIMA and Artificial Neural Network." In 2009 International Conference on Information and Financial Engineering, ICIFE. IEEE, 2009. http://dx.doi.org/10.1109/icife.2009.23.

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Reports on the topic "Macroeconomics – Forecasting"

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Diebold, Francis. The Past, Present, and Future of Macroeconomic Forecasting. Cambridge, MA: National Bureau of Economic Research, November 1997. http://dx.doi.org/10.3386/w6290.

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Owyang, Michael T., and Ana B. Galvão. Forecasting Low Frequency Macroeconomic Events with High Frequency Data. Federal Reserve Bank of St. Louis, 2020. http://dx.doi.org/10.20955/wp.2020.028.

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Ng, Serena, and Jonathan Wright. Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling. Cambridge, MA: National Bureau of Economic Research, September 2013. http://dx.doi.org/10.3386/w19469.

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Stock, James, and Mark Watson. A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series. Cambridge, MA: National Bureau of Economic Research, June 1998. http://dx.doi.org/10.3386/w6607.

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Monetary Policy Report - January 2021. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr1.-2021.

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Macroeconomic Summary Overall inflation (1.61%) and core inflation (excluding food and regulated items) (1.11%) both declined beyond the technical staff’s expectations in the fourth quarter of 2020. Year-end 2021 forecasts for both indicators were revised downward to 2.3% and 2.1%, respectively. Market inflation expectations also fell over this period and suggested inflation below the 3% target through the end of this year, rising to the target in 2022. Downward pressure on inflation was more significant in the fourth quarter than previously projected, indicating weak demand. Annual deceleration among the main groups of the consumer price index (CPI) was generalized and, except for foods, was greater than projected in the October report. The CPI for goods (excluding foods and regulated items) and the CPI for regulated items were subject to the largest decelerations and forecasting discrepancies. In the first case, this was due in part to a greater-than-expected effect on prices from the government’s “VAT-fee day” amid weak demand, and from the extension of some price relief measures. For regulated items, the deceleration was caused in part by unanticipated declines in some utility prices. Annual change in the CPI for services continued to decline as a result of the performance of those services that were not subject to price relief measures, in particular. Although some of the overall decline in inflation is expected to be temporary and reverse course in the second quarter of 2021, various sources of downward pressure on inflation have become more acute and will likely remain into next year. These include ample excesses in capacity, as suggested by the continued and greater-than-expected deceleration in core inflation indicators and in the CPI for services excluding price relief measures. This dynamic is also suggested by the minimal transmission of accumulated depreciation of the peso on domestic prices. Although excess capacity should fall in 2021, the decline will likely be slower than projected in the October report amid additional restrictions on mobility due to a recent acceleration of growth in COVID-19 cases. An additional factor is that low inflation registered at the end of 2020 will likely be reflected in low price adjustments on certain indexed services with significant weight in the CPI, including real estate rentals and some utilities. These factors should keep inflation below the target and lower than estimates from the previous report on the forecast horizon. Inflation is expected to continue to decline to levels near 1% in March, later increasing to 2.3% at the end of 2021 and 2.7% at year-end 2022 (Graph 1.1). According to the Bank’s most recent survey, market analysts expect inflation of 2.7% and 3.1% in December 2021 and 2022, respectively. Expected inflation derived from government bonds was 2% for year-end 2021, while expected inflation based on bonds one year forward from that date (FBEI 1-1 2022) was 3.2%.
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