Vector Autoregressive Modeling

At this article we will try-in light of the macroeconomic statistical data-to assess and analyze the dynamics of employment in Morocco in a polarized economic context marked by the proliferation of free-trade agreements exchange and to be interested in the relationship between international trade and the labor market. For this, we will try to identify factors that determine employment using an econometric model, and to explain the apparent weakness of creation rate of job positions created by our economy in the framework of agreements free trade, when other economically similar countries are experiencing a massive increase in employment rates. This will naturally raise the question of the identification of these factors will allow us also to identify key trends and constraints of the labor market and external trade. Thus, in order to clarify this impact, we proceed to the verification of the link between employment and the variables that seem representative of the openness of our economy

This study aims to analyze the relationship of exchange rate of Rupiah per U.S. Dollar and interest rate, whether the one of these affects the other one or both of these affect each other. This study also wants to know if there is long term relationship between those. This study tested using the Vector Autoregressive (VAR) model and Johansen cointegration test by Eviews 8 software. The data used is monthly data January 2009 – April 2015 periode. The results of this study are the difference of interest rate affects the difference of exchange rate of Rupiah per U.S. Dollar positively, while  the difference of exchange rate of Rupiah per U.S. Dollar does not affects the difference of interest rate. Beside that, there is no long term relationship between interest rate and exchange rate of Rupiah per U.S. Dollar.

This study evaluated and forecasted the impact of FDI in the agricultural sector from 1980-2007, specifically its
impact on agricultural output and labor in a Vector Auto Regression (VAR) environment. Data used in this study
were sourced from Central Bank of Nigeria (CBN) statistical bulletin (2009). Results from the analysis revealed
that FDI in the period under review had no significant impact on agricultural output. In addition, results of the
forecast estimates showed that the current volume of FDI would not significantly affect agricultural output but
will have significant positive impact on labor (employment generation). This study recommended for increase in
the volume of FDI and advised government and other stakeholders to seek FDI that will improve existing or
introduce new technology in the agricultural sector and enhance domestic capacity or domestic investment, even
if the opportunity cost of a reduction in labor may have to be paid.

Base metal prices, especially steel, play a significant role in industrial economics, making them worth knowing about future values. In most cases, we expect superior performance from multivariate forecasting models comparing univariate methods due to the involvement of explanatory variables in the system. Standard vector auto regressive model can only capture short-run dynamics because of the differencing process for non-stationary series that eliminates the possible long-run relationship. Instead, performing non-stationary series on levels through the vector auto-regressive framework does not suffers such loss. Moreover, the vector error correction model can define both short-term and long-run dynamics explicitly. These models can yield more robust forecasts in the mid-term and long-term by investigating short-run and long-run relationships simultaneously. The current study aims to perform an out-of-sample forecast for the United States steel prices index 18 months ahead using cointegrated variables. The results suggest that the non-stationary vector auto-regressive model outperforms the vector error correction model regarding mean absolute percentage error and root mean square error as forecast accuracy measures.

With the growing number of adolescent parents, adolescent fertility rate has become one of the alarming issues in the Philippine society today. According to the statistics released by the National Statistics Office, the adolescent fertility rate has doubled from the year 2000 to 2010. This study aims to find a model that will explain the adolescent fertility rate and the factors that influence it through the course of time.

Using the time series data for adolescent fertility rate, a model was built using independent variables namely, literacy rate, gross domestic product, female labor employment and total maternal deaths. All of the mentioned variables were proven to be stationary series. Hence, the Vector Autoregressive (VAR) modeling technique was utilized. The final model the researchers have arrived at has shown that several lag values of the independent variables have a significant effect on the current value of the adolescent fertility rate at 0.10 level of significance.

The wide range of models needed to support the various short-term operations for electricity generation demonstrates the importance of accurate specifications for the uncertainty in market prices. This is becoming increasingly challenging, since electricity hourly price densities exhibit a variety of shapes, with their characteristic features changing substantially within the day and over time, and the influx of renewable power, wind and solar in particular, has amplified these effects. A general–purpose, analytically tractable representation of the stochastic price formation process would have considerable value for operations control and trading, but existing empirical approaches or the application of standard density functions are unsatisfactory. We develop a general four parameter stochastic model for hourly prices, in which the four moments of the density function are dynamically estimated as latent state variables and furthermore modelled as functions of several plausible exogenous drivers. This provides a transparent and credible model that is sufficiently flexible to capture the shape– shifting effects, particularly with respect to the wind and solar output variations causing dynamic switches in the upside and downside risks. Extensive testing on German wholesale price data, benchmarked against quantile regression and other models in out-of-sample backtesting, validated the approach and its analytical appeal.

This work tries to measure the dynamic of nominal interest rate in a small open economy like Mexico and, while doing so, showing that Bootstrapping experiments are best suited for studies where time series that don't follow a normal distribution function are present.

Base metal prices, especially steel, play a significant role in industrial economics, making them worth knowing about future values. In most cases, we expect superior performance from multivariate forecasting models comparing univariate methods due to the involvement of explanatory variables in the system. Standard vector auto regressive model can only capture short-run dynamics because of the differencing process for non-stationary series that eliminates the possible long-run relationship. Instead, performing non-stationary series on levels through the vector auto-regressive framework does not suffers such loss. Moreover, the vector error correction model can define both short-term and long-run dynamics explicitly. These models can yield more robust forecasts in the mid-term and long-term by investigating short-run and long-run relationships simultaneously. The current study aims to perform an out-of-sample forecast for the United States steel prices index 18 months ahead using cointegrated variables. The results suggest that the non-stationary vector auto-regressive model outperforms the vector error correction model regarding mean absolute percentage error and root mean square error as forecast accuracy measures.

This article explores the paradox that forecasts may be most likely to fail during dramatic moments of historic change that social scientists are most eager to predict. It distinguishes among four types of shocks that can undermine the predictive power of time series analyses: effect shocks that change the size of the causal effect; input shocks that change the causal variables; duration shocks that change how long a causal effect lasts; and actor shocks that change the number of agents in the system. The significance of these shocks is illustrated in Israeli-Palestinian interactions, one of the contemporary world's most intensely scrutinized episodes, using vector autogression analyses of more than 15,000 Reuters news stories over the past three decades. The intervention of these shocks raises the prospect that some historic episodes may be unpredictable, even retrospectively.

Because of the advent of GPS techniques, a wide range of scientific literature on Sport Science is nowadays devoted to the analysis of players’ movement in relation to team performance in the context of big data analytics. A specific research question regards whether certain patterns of space among players affect team performance, from both an offensive and a defensive perspective. Using a time series of basketball players’ coordinates, we focus on the dynamics of the surface area of the five players on the court with a two-fold purpose: (i) to give tools allowing a detailed description and analysis of a game with respect to surface areas dynamics and (ii) to investigate its influence on the points made by both the team and the opponent. We propose a three-step procedure integrating different statistical modelling approaches. Specifically, we first employ a Markov Switching Model (MSM) to detect structural changes in the surface area. Then, we perform descriptive analyses in order to highlight associations between regimes and relevant game variables. Finally, we assess the relation between the regime probabilities and the scored points by means of Vector Auto Regressive (VAR) models. We carry out the proposed procedure using real data and, in the analyzed case studies, we find that structural changes are strongly associated to offensive and defensive game phases and that there is some association between the surface area dynamics and the points scored by the team and the opponent.

This workpaper tests the efficient market hypothesis using a present value model considering constant expected real returns and constant expected excess returns. This model takes form of an autoregressive vector and associates the dividend price ratio with the future expected real return and the growth rate of future dividends. Considering the efficient market hypothesis is expected that the calculated dividend price ratio be equal to the observed ratio. This study uses an equal-weighted index with the entire Economática database, comprising 554 companies from the first quarter of 1995 until the second quarter of 2014. The results reject the hypothesis of equality between the theoretical and the observed dividend price ratio, considering constant expected real returns and constant expected excess returns. This result implies that expected returns are time varying.

This paper investigates the effect of financial fragmentation on the monetary transmission mechanism in different Euro area economies, categorized into two groups: countries considered as “core” economies and countries characterized as “peripheral” economies. We analyze the effects of financial fragmentation on the monetary transmission mechanism through the traditional interest rate channel. To gauge the impact of changes in policy rates on the behavior of real variables such as aggregate output and employment we use a Smooth Transition VAR (VSTAR) model. Employing a nonlinear multivariate time series approach helps us capture the regime-dependent dynamics of the variables under study. The results obtained show that money market rates targeted by the central bank do not completely pass through to banks’ lending rates to firms, particularly in a financially fragmented environment. This finding supports the hypothesis of an impairment of the monetary transmission mechanism as a result of financial fragmentation. Given this impairment in some sectors and regions an accompanying credit volume policy might have been appropriate.

We consider the problem of identifying large-scale effective connectivity of brain networks from fMRI data. Standard vector autoregressive (VAR) models fail to estimate reliably networks with large number of nodes. We propose a new method based on factor modeling for reliable and efficient high-dimensional VAR analysis of large networks. We develop a subspace VAR (SVAR) model from a factor model (FM), where observations are driven by a lower-dimensional subspace of common latent  factors with an AR dynamics. We consider two variants of principal components (PC) methods that provide consistent estimates for the FM hence the implied SVAR model, even of large dimensions. Information criterion is used to select the optimal subspace dimension. We established asymptotic normality and convergence rates for the estimated SVAR coefficients matrix. Evaluation on simulated resting-state fMRI shows that the SVAR models are more robust and produce better connectivity estimates than the classical model for a moderately-large network analysis. Results on real data by varying the subspace dimensions identify strong connections in the default mode network and reveal hierarchical connectivity of resting-state networks with distinct functional relevance.