A global vector autoregression model for softwood lumber trade.

2021 ◽  
pp. 174-193
Author(s):  
Fatemeh Mokhtarzadeh
Keyword(s):  
Export Price ◽  
Propagation Mechanism ◽  
Impulse Response Functions ◽  
Vector Autoregressive ◽  
Softwood Lumber ◽  
Export Prices ◽  
Country Level ◽  
Housing Starts ◽  
Global Vector ◽  
Comprehensive Framework

Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.

A global vector autoregression model for softwood lumber trade.

2021 ◽  
pp. 174-193
Author(s):  
Fatemeh Mokhtarzadeh
Keyword(s):  
Export Price ◽  
Propagation Mechanism ◽  
Impulse Response Functions ◽  
Vector Autoregressive ◽  
Softwood Lumber ◽  
Export Prices ◽  
Country Level ◽  
Housing Starts ◽  
Global Vector ◽  
Comprehensive Framework

Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

Deviations from fundamental value and future closed-end country fund returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Berggrun ◽  
Emilio Cardona ◽  
Edmundo Lizarzaburu
Keyword(s):  
Control Variable ◽  
Asset Returns ◽  
Weak Form ◽  
Share Price ◽  
Local Market ◽  
Impulse Response Functions ◽  
Content Type ◽  
Vector Autoregressive ◽  
Causality Tests ◽  
Fundamental Value

PurposeThis article examines whether deviations from fundamental value or closed-end country fund's discounts or premiums forecast future share price returns or net asset returns.Design/methodology/approachThe main empirical (econometric) tool is a vector autoregressive (VAR) model. The authors model share price returns and net asset returns as a function of their lagged values, the discounts or premiums, and a control variable for local market returns. The authors also conduct Dickey Fuller and Granger causality tests as well as impulse response functions.FindingsIt was found that deviations from fundamental value do predict share price returns. This predictability is contrary to weak-form market efficiency. Premiums or discounts predict net asset returns but weakly.Originality/valueThe findings point to the idea that the closed-end fund market is somewhat predictable and inefficient (in its weak form) since the market appears to be able to anticipate a fund's future returns using information contained in the premiums (or discounts). In particular, the market has the ability to anticipate future behaviour because growing premiums forecast declining share price returns for one or two periods ahead.

scholarly journals Forecasting Cattle Prices in the Presence of Structural Change

1992 ◽  
Vol 24 (2) ◽  
pp. 11-22 ◽  
Author(s):  
Barry K. Goodwin
Keyword(s):  
Structural Change ◽  
A Priori ◽  
Arima Model ◽  
Var Model ◽  
Impulse Response Functions ◽  
Cattle Industry ◽  
Vector Autoregressive ◽  
Economic Relationships ◽  
New Information ◽  
Out Of Sample

AbstractRecent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.

scholarly journals Yield curve dynamics with macroeconomic factors in Iberian economies

2020 ◽  
Vol 10 (3) ◽  
pp. 193-203
Author(s):  
Isabel Maldonado ◽  
Carlos Pinho
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Error Variance ◽  
Impact Factors ◽  
Strong Impact ◽  
Impulse Response Functions ◽  
Macroeconomic Factors ◽  
Vector Autoregressive ◽  
Future Evolution

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.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

scholarly journals The COVID-19 Shock: A Bayesian Approach

2021 ◽  
Vol 14 (10) ◽  
pp. 495
Author(s):  
Oussama Abi Younes ◽  
Sumru Altug
Keyword(s):  
Interest Rates ◽  
Unemployment Rate ◽  
Bayesian Approach ◽  
Economic Activity ◽  
Preventive Measures ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
Unemployment Rates ◽  
Explicit Recognition

The coronavirus crisis that started in December 2019 was declared a pandemic by March 2020 and had devastating global consequences. The spread of the virus led to the implementation of different preventive measures prior to the availability of effective vaccines. While many governments implemented lockdowns to counter the pandemic, others did not let the virus halt economic activity. In this paper, we use a Bayesian Vector Autoregressive framework to study the effects of the pandemic on prices, unemployment rates, and interest rates in nine countries that took distinctive approaches in tackling the pandemic, where we introduce lockdowns as shocks to unemployment. Based on impulse response functions, we find that in most countries the unemployment rate rose, interest rates fell or turned negative, and prices fell initially following the implementation of the lockdown measures. However, the massive fiscal and monetary stimulus packages to counteract the effects of the pandemic reversed some of the effects on the variables, suggesting that models with explicit recognition of such effects should be developed.

scholarly journals Role of Zakat as Social Finance Catalyst to Islamic Banking and Economic Growth

2017 ◽  
Vol 2 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Ai Nur Bayinah
Keyword(s):  
Economic Growth ◽  
Positive Impact ◽  
Islamic Banking ◽  
Annual Data ◽  
Impulse Response Functions ◽  
Islamic Banks ◽  
West Java ◽  
Vector Autoregressive ◽  
The Impact ◽  
Integration Analysis

This paper is aimed to assess the contribution of Zakat in boosting Islamic banks’ financing and economic growth for the period 2011-2015, in 10 district/city of West Java Province, Indonesia. Through Vector Autoregressive (VAR) panel co-integration analysis, variance decompositions (VD) and impulse response functions (IRF), this study investigates Zakat, Islamic Banking, and economic growth nexus. Findings in this research highlight that Zakat has a significant impact on Islamic banking, so this institution would contribute to economic growth both in the short and the long run, with fluctuation in variance from the first year. The results lend support to the view that Zakat not only leads to social benefits but also has a positive impact on the economy through increasing Islamic banks’ financing. Therefore, this research will serve as a motivation for the industry players and regulators to continuously promote Zakat as a strategic policy. The originality of this research is to assess Zakat-led growth and finance by analyzing the impact of Zakat on the Islamic banking and regional economic outcome. Another novel aspect of this study is in the methodology as it employs VAR panel co-integration analysis, VDs and IRFs on the set of annual data. Keywords: Zakat, Islamic Banking Financing, Economic Growth, West Java

Results and Discussion 2

2014 ◽  
pp. 180-225
Keyword(s):  
Financial Development ◽  
Impulse Response ◽  
Productivity Growth ◽  
Variance Decomposition ◽  
Dynamic Responses ◽  
Response Analysis ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
The Empirical Analysis

The empirical analysis of this chapter provides insights into the functioning of the economies of three selected countries. Later in the chapter, the dynamic responses of the model to shocks in indicators of financial development are investigated. To obtain credible impulse response analysis, economic theory is used to set the required identifying restrictions instead of using an “unrestricted” vector autoregressive model. The structural form of the model then is summarised in the chapter by the variance decomposition and impulse response functions. The general results from impulse response functions advocate the theory of financial intermediation arguing that the development of the financial market helps to promote economic growth. Furthermore, the results of variance decomposition shows that different measures of financial development influence the variation of growth variables, particularly investment, savings, and productivity growth.

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