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Zonder onderwerpscode: economie

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Monetary DSGE models are widely used because they fit the data well and they can be used to address important monetary policy questions. We provide a selective review of these developments. Policy analysis with DSGE models requires using data to assign numerical values to model parameters. The chapter describes and implements Bayesian moment matching and impulse response matching procedures for this purpose.

Zonder onderwerpscode: economie

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We develop a model of investment with financial constraints and use it to investigate the relation between investment and Tobin's q. A firm is financed partly by insiders, who control its assets, and partly by outside investors. When their wealth is scarce, insiders earn a rate of return higher than the market rate of return, i.e., they receive a quasi-rent on invested capital. This rent is priced into the value of the firm, so Tobin's q is driven by two forces: changes in the value of invested capital, and changes in the value of the insiders' future rents per unit of capital. This weakens the correlation between q and investment, relative to the frictionless benchmark. We present a calibrated version of the model, which, due to this effect, generates realistic correlations between investment, q, and cash flow.

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The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.

Macroeconomics --- Economics: General --- International Economics --- Econometrics --- Intelligence (AI) & Semantics --- Foreign Exchange --- Industries: Financial Services --- Informal Economy --- Underground Econom --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- State Space Models --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Economic & financial crises & disasters --- Economics of specific sectors --- Econometrics & economic statistics --- Machine learning --- Currency --- Foreign exchange --- Computer applications in industry & technology --- Factor models --- Econometric analysis --- Technology --- Time series analysis --- Spot exchange rates --- Mobile banking --- Currency crises --- Informal sector --- Economics --- Econometric models --- Banks and banking, Mobile --- South Africa

Choose an application

• Reference Manager
• EndNote
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The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.

South Africa --- Macroeconomics --- Economics: General --- International Economics --- Econometrics --- Intelligence (AI) & Semantics --- Foreign Exchange --- Industries: Financial Services --- Informal Economy --- Underground Econom --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- State Space Models --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Economic & financial crises & disasters --- Economics of specific sectors --- Econometrics & economic statistics --- Machine learning --- Currency --- Foreign exchange --- Computer applications in industry & technology --- Factor models --- Econometric analysis --- Technology --- Time series analysis --- Spot exchange rates --- Mobile banking --- Currency crises --- Informal sector --- Economics --- Econometric models --- Banks and banking, Mobile