Comparing the forecasting ability of financial conditions indices: The case of South Africa

Balcılar, Mehmet; Gupta, Rangan; Eyden, Reneé van; Thompson, Kirsten L.; Majumdar, Anandamayee

doi:10.1016/j.qref.2018.03.012

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…To address this problem, the generalised impulse responses (GIRFs) are proposed by Koop, Pesaran, and Potter (1996), which can be based on bootstrap or Monte Carlo method. The bootstrap‐based GIRF method is used by Weise (1999) and recently used by Balcilar et al (2018), Balcilar et al. (2016) and Rahman and Serletis (2010).…”

Section: Resultsmentioning

confidence: 99%

“…Following Anderson and Vahid (1998), Auerbach and Gorodnichenko (2012), Balcilar, Gupta, Van Eyden, Thompson, and Majumdar (2018), Balcilar, Thompson, Gupta, and Van Eyden (2016), Camacho (2004), Teräsvirta and Yang (2014) and Weise (1999), among others, who extended the smooth transition autoregressive threshold models to a multi‐equation setting based on the single equation introduced by Teräsvirta and Anderson (1992), our STVAR model is specified as follows:

Y_{t} = μ^{1} + {falsefalse}_{\sum}^{i = 1} Φ_{i}^{1} Y_{t - 1} + (μ^{2} + {falsefalse}_{\sum}^{i = 1} Φ_{i}^{2} Y_{t - 1}) F (z_{t}; γ; c) + ε_{t},

where

Y_{t} = {()(, Y_{1 t}, Y_{2 t}, \dots, Y_{Nt})}^{'}

. Indeed, the number of variables,

N, is 4

; hence,

Y_{1 t} = {NEER}_{t}

Y_{2 t} = {CPI}_{t}

Y_{3 t} = {IRATE}_{t}

, and

Y_{4 t} = {IPI}_{t}

.…”

Section: Econometric Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

2020

Self Cite

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One of the central issues within the framework of the new open-economy macroeconomic models is the pass-through of exchange rate to prices. This issue is largely triggered by persistent high volatility in terms of exchange rates between countries following the collapse of the Bretton Woods system of adjustable peg in the 1970s. Essentially, the issue has demonstrated a vastly increased risk to achieving the monetary policy goal of low and stable inflation levels especially during periods of greater openness, characterised by a significant change in exchange rate and trade policies (see Balcilar,

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Section: Resultsmentioning

confidence: 99%

Y_{t} = μ^{1} + {falsefalse}_{\sum}^{i = 1} Φ_{i}^{1} Y_{t - 1} + (μ^{2} + {falsefalse}_{\sum}^{i = 1} Φ_{i}^{2} Y_{t - 1}) F (z_{t}; γ; c) + ε_{t},

where

Y_{t} = {()(, Y_{1 t}, Y_{2 t}, \dots, Y_{Nt})}^{'}

. Indeed, the number of variables,

N, is 4

; hence,

Y_{1 t} = {NEER}_{t}

Y_{2 t} = {CPI}_{t}

Y_{3 t} = {IRATE}_{t}

, and

Y_{4 t} = {IPI}_{t}

.…”

Section: Econometric Methodologymentioning

confidence: 99%

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

2020

Self Cite

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“…If the economic variables have sudden structural changes in the process of generation and collection, the linear model used in the existing literature is prone to statistical bias; therefore, this paper will use the time-varying parameter vector autoregression method to study the dynamic correlation between the uncertainty of the U.S. trade policy and China's grain trade. Compared with the traditional linear model, on the one hand, the vector autoregressive model can be er reflect the influence relationship between the variables within the system and help to analyze the impulse response and variance decomposition of the variables; on the other hand, the model does not need to assume the existence of a clear linear correlation between the variables, and it can also overcome the impact of the structural mutation of the economic data [71][72][73].…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Impact of U.S. Trade Policy Uncertainty on China’s Grain Trade

Yang,

Gai,

Zhang

et al. 2024

Sustainability

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U.S. trade protectionism has frequently risen recently, and trade policy fluctuations have become increasingly significant. In this context, examining the impact of U.S. trade policy uncertainty on China’s grain trade is of great significance to China’s response to changes in the international trade situation, guaranteeing national food security and promoting sustainable agricultural development. From the statistical data, the U.S. trade policy uncertainty and China’s grain imports primarily show a reverse trend, and China’s grain exports show a positive trend. To further explore the impact of U.S. trade policy uncertainty on China’s grain trade, this study selects the monthly data from July 2003 to December 2022. It conducts impulse response analysis by constructing a vector autoregressive model with stochastic volatility. It is found that the impact of U.S. trade policy uncertainty on China’s grain trade has prominent time-varying characteristics and point-in-time effects, and the impact on different kinds of grain is heterogeneous. In this regard, China needs to clarify the nature of the trade dispute between China and the United States, reasonably utilize the multilateral coordination mechanism of the WTO, coordinate the international and domestic markets, adjust the short board of grain trade, and safeguard the sustainable development of Chinese agriculture.

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“…Empirical applications of the framework suggested in Koop and Korobilis (2015) range from constructing financial indices for different countries to investigating the transmission mechanisms in the cryptocurrency markets. With regard to the former, Balcilar et al (2018) compare the out-of-sample performance of the model suggested in Koop and Korobilis (2015) with other techniques using South African data, and Wang et al (2018) use it to construct an FCI for China. With regard to the latter, we can refer the reader to Antonakakis et al (2019) for a good illustration.…”

Section: Factor Augmented Tvp-var Modelsmentioning

confidence: 99%

“…With regard to the former, Balcilar et al . (2018) compare the out‐of‐sample performance of the model suggested in Koop and Korobilis (2015) with other techniques using South African data, and Wang et al . (2018) use it to construct an FCI for China.…”

Section: Extensions and New Dma Modelsmentioning

confidence: 99%

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

Nonejad¹

2021

Journal of Economic Surveys

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Dynamic model averaging (DMA) has become a widely used estimation technique in macroeconomic applications. Since its introduction in econom(etr)ics by Gary Koop and Dimitris Korobilis in 2009, applications of DMA have increased in unimaginable ways. Besides applying the original (univariate) framework suggested by Koop and Korobilis on the data of interest, for example, the inflation rate of the country of choice or return on the rate of equity, practitioners have been able to use DMA-based techniques to extend current models, thereby further improving out-of-sample forecast accuracy, overcome computational bottlenecks, and even help improve our understanding of economic phenomena by introducing new models. These include using Google search data in combination with the predictive likelihood to govern switching between different predictive regressions in the model set or specifying large time-varying parameter vector autoregressions that can be estimated without resorting to simulation-based techniques. This study provides an overview of DMA techniques and the ways in which they have evolved since the contribution of Koop and Korobilis.

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Comparing the forecasting ability of financial conditions indices: The case of South Africa

Cited by 12 publications

References 27 publications

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

Analysis of the Impact of U.S. Trade Policy Uncertainty on China’s Grain Trade

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

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