IMPACT OF MACROECONOMIC VARIABLES ON STOCK MARKET: THE CASE OF IRAN

This paper investigates the relationship between a set of economic variables (i.e. inflation rate, interest rate of one-year investing deposits in state banks, interest rate of bonds and the growth rate of gold price) and Tehran Stock Exchange (TSE) indicators during April 1998 to March 2008Economic variables, Tehran stock Exchange(TSE), Vector auto-regressive model, Johansen co- integration test, Vector-error correction model

Fiscal Policy in the BRICs

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government’s behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the importance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of ‘expansionary fiscal contraction’ in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil.fiscal policy, emerging markets, fully simultaneous system of equations, sign-restrictions VAR, smooth transition regression model

A Vector Auto-Regressıve (VAR) Model for the Turkish Financial Markets

In this paper, we develop a vector autoregressive (VAR) model of the Turkish financial markets for the period of June 15 2006 – June 15 2010 and forecasts ISE100 index, TRY/USD exchange rate, and short-term interest rates. The out-of-sample forecast performance of the VAR model is compared with the results from the univariate models. Moreover, the dynamics of the financial markets are analyzed through Granger causality and impulse response analysis.multivariate financial time series; vector auto-regressive (VAR) model; impulse response analysis; Granger causality

Can Sectoral Shifts Generate Persistent Unemployment in Real Business Cycle Models?

This paper extends the standard Real Business Cycle model to incorporate sectoral shifts in unemployment. Using relative sectoral technology and sectoral tastes shocks, combined with labor adjustment costs across sectors, we assess the possibility of generating persistent aggregate unemployment. Calibrated to Canadian data, the models suggest that the introduction of sectoral labor mobility with adjustment costs improves the ability of the standard real business cycle model to match the observed persistence in unemployment. Empirically, we estimated a Vector Auto-Regressive model and successfully matched the models' overshooting of labor. The results suggest that government policies aimed to alleviate the unemployment burden should pay closer attention to sectoral phenomena, specifically to sectoral labor mobility.Real Business Cycle (RBC), Sectoral Shocks, Unemployment Persistence, Vector Auto-Regressive (VAR), Blanchard-Quah (B-Q) Identification

A seasonal auto-regressive model based support vector regression prediction method for H5N1 avian influenza animal events

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction. © 2011 Imperial College Press

Spatial propagation of macroeconomic shocks in Europe

This paper develops a Spatial Vector Auto-Regressive (SpVAR) model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.Macroeconomics, Spatial Models, VAR

Spatial Propagation of Macroeconomic Shocks in Europe

This paper develops a Spatial Vector Auto-Regressive (SpVAR) model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.Macroeconomics, Spatial Models, VAR

Oil price shocks and their short- and long-term effects on the Chinese economy

A considerable body of economic literature shows the adverse economic impacts of oil-price shocks for the developed economies. However, there has been a lack of empirical study of this kind on China and other developing countries. This paper attempts to fill this gap by answering how and to what extent oil-price shocks impact China’s economy, emphasizing on the price transmission mechanisms. To that end, we develop a structural vector auto-regressive model. Our results show that an oil-price increase negatively affects output and investment, but positively affects inflation rate and interest rate. However, with the differentiated price control policies for materials and intermediates on the one hand and final products on the other hand in China, the impact on real economy, represented by real output and real investment, lasts much longer than that to price/monetary variables. Our decomposition results also show that the short-term impact, namely output decrease induced by the cut of capacity-utilization rate, is greater in the first one to two years, but the portion of the long-term impact, defined as the impact realized through an investment change, increases steadily and exceeds that of short-term impact at the end of the second year. Afterwards, the long-term impact dominates, and maintains for quite some time.Structural vector auto-regressive model; Unit root test; Error-correction model; Oil-price shocks; Price transmission mechanisms; Investment; Output; Producer/consumer price index; Census X-12 approach; China