The disproportionate **response** of agricultural and industrial prices to monetary changes, both in the short-run and long-run has been empirically validated by a number of studies for different countries. For the U.S., Orden and Fackler (1989) using a **VAR** and **impulse** **response** function show that an increase in money supply raises agricultural prices relative to the general price level for more than a year, implying monetary changes lead to change in real agricultural prices in both the short- and long-run. Saghaian, Reed and Merchant (2002) extended the Frankel’s closed economy model to an open economy framework by including exchange rate to account for international trading of agricultural commodities for the US economy. They found that monetary changes have both short- and long-run effects on agricultural prices.

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This study adopts a panel data set for China’s 30 provinces over the period 1995-2010 to investigate the causal and the dynamic relationship between energy consumption and economic growth, including two other factors of production: capital formation and labor force. To overcome the deficiency of the traditional time-series analysis, panel time-serious techniques are employed to derive more reasonable results. Before testing for causal and dynamic relationship among variables using panel method, panel unit root test and panel co-integration test should be performed in sequence. Firstly, the LLC test, Fisher-ADF test, Fisher-PP test, IPS test, and CIPS test all show that the variables are integrated of order one. Next, within dimension and between dimension approaches of Pedroni’s heterogeneous panel tests indicate that there is a long-run co-integration relationship among variables real GDP, energy consumption, capital formation and labor force. Secondly, from the results of panel VEC model, the effect from economic growth to energy consumption is unidirectional, and the coefficient is 1.562. Real GDP, energy consumption, capital formation and labor force each respond to short-run deviations from long-run equilibrium with a slow adjustment speed. Furthermore, according to panel Granger causality test, there is bidirectional causality between real GDP and energy consumption, which is consistent with the growth hypothesis in terms of the energy consumption-growth nexus. The unidirectional causality from capital formation to energy consumption reveals that energy consumption cannot affect real GDP through capital formation, which may crowd out investment on manufacturing or other sectors. In addition, by estimating the panel **VAR** model and **impulse** **response** functions, it is indicated that the responses of real GDP to a shock of energy consumption are negative, whereas the shock of real GDP changes is positive with most of the energy consumption **response** being absorbed during the six years, showing excessive energy consumption of short-run could depress economic growth of long-run. Finally, by variance decompositions, derived from the orthogonalized **impulse**-**response** coefficient matrices, a shock in the energy consumption takes the biggest effect on real GDP in both short-run and long-run.

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Note that the GIRFs to (one standard error) investment shock (Panel 2-a in Figure 2) coincide with the OIRFs to an -shock when is assumed to be contemporaneously unaﬀected by other two variables, and (Panel 2-b). Note also that under this assumption, the OIRFs to a -shock are very diﬀerent from the corresponding GIRFs. However, the GIRFs to a -shock are identical to the OIRFs when is ordered ﬁ rst in the **VAR** (Panel 2-d) by construction. Again, the other OIRFs under that assumption are quite diﬀerent from the corresponding GIRFs. Likewise, the GIRFs to a -shock are identical only to the OIRFs to a -shock when is ordered ﬁ rst (Panel 2-c).

The objective of this paper is to revisit the resource curse hypothesis both within and between countries of different democratic footprint, based on a dynamic model that properly accounts for endogeneity issues. To achieve that, we apply a panel Vector Auto-Regressive (PVAR) approach along with panel **impulse** **response** functions to data on oil abundance vari- ables, economic growth and several political institutional variables in 76 countries classified by different income groupings, level of development and oil importing or exporting status, over the period 1980-2012. Our results suggest that controlling for the quality of political in- stitutions is important in rendering the resource course hypothesis significant. Doing so, the resource curse hypothesis is documented mainly for developing economies, net oil-exporters and medium-high income countries. Specifically, when economies from the aforementioned groups are characterised by weak quality of political institutions, then oil abundance is not growth-enhancing.

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This paper puts search and matching models, the workhorse of modern labour market marcoeconomics, under novel empirical scrutiny. Using state-of-the-art Bayesian estimation techniques, we fit an extended TVP **VAR** to US labour market data from 1962–2016. We depart from existing literature (see e.g. Yashiv (2006); Faccini et al. (2013); Hall (2005); Hagedorn and Manovskii (2008); Lubik (2009)) by proposing a simple and intuitive method to test calibrated model **impulse** **response** functions against empirical estimates. Our results provide three main messages. First, search frictions models are unable to match the responses of key labour market variables to structural shocks. Second, we find evidence against the hypothesis outlined in Shimer (2005) that unemployment and wage volatility possess a negative relationship. Third, empirical estimates show that the key shocks underpinning search and matching models explain, at best, 50% of total variation.

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SVAR studies with more carefully considered schemes quickly became widely used see, e.g. Blanchard and Watson (1984), Bernanke (1986), Sims (1986), Eichenbaum and Evans (1995), Sims and Zha (2006), Basher et al. (2010). Non-arbitrary orthogonalisation schemes which impose contemporaneous restrictions on the **VAR** are referred to as short-run identification schemes. Most short-run restrictions are zero restrictions e.g. that output reacts only with a lag to monetary shocks. Although a seemingly reasonable assumption, clearly the frequency of one‘s data is of vital importance; if one had annual data, a contemporaneous zero restriction is likely to be more debatable than if it were on quarterly or monthly data. It should be noted that restrictions are not confined to forcing parameters to be zero as in the Wold causal chain, other linear (e.g. Keating 1992) and non-linear (e.g. Galí 1992) restrictions are occasionally employed on the contemporaneous relations between variables. Restrictions can also be implemented depending on assumptions about what information is available to agents at the time of a shock e.g. Sims (1986), West (1990). Opinions concerning short-run restrictions are mixed. Faust and Leeper (1997) claim there is often simply an insufficient number of tenable contemporary restrictions to achieve identification. However, Christiano et al. (2006) argue that short-run SVARs perform ‗remarkably well‘ by way of the relatively strong sampling properties of the IRFs they produce.

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Although the **impulse** **response** function depicts the impact of a disturbance of endogenous variable on other variables in **VAR**, but variance decomposition makes changes in the endogenous variables broken down into **VAR** factor impact, and thus variance decomposition provides relative importance of each random innovation influencing the variables in **VAR** (Eviews help file). Figure 8 shows that, throughout the forecasting period, 99% of the forecasting variance of RJ is due to the disturbance of RJ, and 1% of the forecasting variance of RJ is due to the disturbance of RC, and these show that the forecasting variance of RJ is mainly due to the impact of RJ. Throughout the forecasting period, 31% of the forecasting variance of RC is due to the disturbance of RJ, and the remaining 69% of the forecasting variance of RC is due to the disturbance of RC, and these show that the forecasting variance of RC is mainly due to the impact of RC (Pindyck, Rubinfeld, 1999[16]; Eviews help file). According to Figure 8 we can also found that, throughout the forecast period, the part of the forecasting variance of RJ due to the disturbance of RC is much less than the part of the forecasting variance of RC due to the disturbance of RJ. This is the same with the conclusion, gotten by the former Grange causality test, that “there is a one-way causal relationship from Japanese rubber futures prices to Chinese rubber futures prices and Japanese rubber futures prices have an unidirectional guiding role on Chinese rubber futures prices between Japanese rubber futures prices and Chinese rubber futures prices, and vice versa”.

To investigate the dynamics of entrepreneurial activity and unemployment rate I estimate vector autoregressions (**VAR**). For the empirical estimation on the panel data, variables need to be stationary and one needs to decide about the optimal lag length according to Holtz‑Eakin et al. (1988). Hušek (2009) suggests to use for lag selection information criteria. The impact of unemployment rate on entrepreneurship is then interpreted based on the results of the Granger causality test, testing the time dependency and the ability to forecast each of the variable (Granger, 1969), and based on the construction of **impulse** **response** function applying Choleski’s decomposition (Hušek, 2009).

Many previous researches focused on the pass-through of world food prices to domestic prices using linear approaches. In this paper, MS-**VAR** model is used to estimate the world food price pass-through to consumer prices in Iran. After the estimation of MS-**VAR** model, regime dependent **impulse** **response** functions are used to calculate the magnitude of the world food prices pass-through into domestic prices in Iran. We employed MS-**VAR** model to capture nonlinearities in the process of price pass-through from world markets into domestic markets. Markov switching (MS) technique was proposed by Hamilton (1990) and later extended to multivariate models such as MS-**VAR** and MS-VEC by Krolzig (1997, 1999). The MS-**VAR** model can be written as:

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The unit root test, vector autoregressive (**VAR**) model, Granger causality test, and **impulse** **response** function are applied in this study to explore the correlation between wholesale price and trading volume of milkfish in Taiwan milkfish market over the period 2001- 2017. The empirical results show that both the wholesale price and the trading volume of the Taiwan milkfish market are stationary series, and there is a bidirectional causal relationship between the two. That is, the price and the volume of the milkfish affect each other, i.e., there is a short-term and interactive relationship between the wholesale price and the trading volume. When the wholesale price rises (falls), there would be a negative (positive) relationship in the first and second period, and a positive (negative) **response** in the seventh period. If the trading volume increases (decreases) in the current period, then the price would experience decline (increase) in the first period.

In the **VAR** system, all variables are treated as endogenous variables which are symmetrically into various estimated equation, and can avoid the problems of omitted variables. Because the economic meaning obtained from the test results is difficult to analyze directly using the **VAR** model, it is often using **impulse** **response** function (**Impulse** **Response** Function, IRF) to analyses[10]. **Impulse** **response** analysis reflected that adding one **impulse** in the disturbance shows the impact on the current value and future value of endogenous variable. Koop et al (1996) presented improved **response** function method (Generalized **Impulse** **Response** Function, GIRF) to carry on the analysis, the decomposition of the does not rely on sequential relationship of the all variables in a **VAR** system, which improves the stability and reliability of the estimation results. This paper investigates one standard deviation of return on equity, RMB real exchange rate and the consumer impact on the current account and the dynamic impact Using this the **impulse** **response** function.

Another important feature appears when we calculate the **impulse** **response** functions through the two empirical methodologies is the interval con…dence interval obtained through the di¤erent shocks. In this context, the interpretation of structural **impulse** **response** functions in the framework of **VAR** based on cointegration and vector error correction models (VECMs), becomes a standard practice to report con…dence intervals (CIs) around the point estimates to assess the estimation uncertainty. Di¤erent methods for the construction of the **impulse** **response** functions intervals have been suggested in the literature. CIs may be based on the asymptotic distributions of the **impulse** responses (Lütke- pohl (2005)), on Monte Carlo integration methods (Sims and Zha (1999)) and on various variants of bootstrap methods (Lütkepohl and Wolters (2001)). In the context of SVECMs with long run restrictions, four methods have been used in applied works. The …rst one is based on a generalization of the asymptotic intervals given by Lütkepohl and Reimers (1992) and Vlaar (2004a). In e¤ect, in the presence of long run restrictions, a correction of the asymptotic distribution is needed, which takes the stochastic nature of the identifying restrictions into account. Empirical applications of this method include Coenen and Vega (1999) and Vlaar (2004b). As an alternative to the asymptotic intervals, bootstrap methods have been used in the context of SVECM. In particular, the standard percentile interval of Efron and Tibshirani (1993), the Hall percentile interval and the studentized Hall interval (Hall (1992)) have been used in Lütkepohl and Wolters (2003), Brüggemann (2004) and Breitung et al (2004) (these three bootstrap versions are available for SVECMs with long run restrictions in form of the menu driven software JMULTI (www.jmulti.com)).

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The second FOM, the risetime, characterizes the **response** of the system to a positive step change in input. The **response** of the circuit of Figure 1 to an applied unit step input is shown in Figure 2. The rise time most typically used as a FOM for a first order system is the “10-90% risetime”, which is simply the amount of time necessary for the output to rise from 10% to 90% of its final value. This measurement in shown in Figure 2 as the time difference between the times t 10 and

As we introduce the study of systems, it will be good to keep the discussion well grounded in the circuit theory you have spent so much of your energy learning. An important problem in modern high speed digital and wideband analog systems is the **response** limitations of basic circuit elements in high speed integrated circuits. As simple as it may seem, the lowly RC lowpass filter accurately models many of the systems for which speed problems are so severe.

IIR digital filter is unlimited duration of **impulse** **response**, this kind of filter generally need to realize using a recursive structure, and called a recursive filter. IIR filters filter expression can be defined as a difference equation: Equation (1) and (2) below:

As we introduce the study of systems, it will be good to keep the discussion well grounded in the circuit theory you have spent so much of your energy learning. An important problem in modern high speed digital and wideband analog systems is the **response** limitations of basic circuit elements in high speed integrated circuits. As simple as it may seem, the lowly RC lowpass filter accurately models many of the systems for which speed problems are so severe.

Measuring these two FOMs in the laboratory is actually a relatively simple effort for low bandwidth systems. The requirements are the circuit, or the system under test (CUT or SUT), and appropriate step and **impulse** waveform generation and measurement equipment. However, some thought needs to go into the waveform specification in order to facilitate measurement of the two FOMS.

Filtering is the processing of a time-domain signal resulting in the reduction of some unwanted input spectral components. The filters allow certain frequencies to pass while attenuating other frequencies in the frequency domain. FIR filter is a non-recursive. The IIR filter, known as a recursive filter, uses feedback to compute output (1). The magnitude and the phase responses of the network function are the two main factors of designing the filter. The magnitude **response** is studied frequently in db through the gain function as in (3). The phase **response** is expressed by phase function or group delay function as in (4). The group delay function and the phase function have profound time-domain ramifications as they have a direct effect on the wave-shape of the output signals.

MATLAB design used for filter **response** and generating coefficient tables, for implementing on FPGA tool. To program on FPGA, VERILOG hardware description language offers an easy way towards different implementation. In DSP, for a finite **impulse** **response** (FIR) filters **impulse** **response** is finite duration. It makes difference to infinite **impulse** **response** (IIR) filters, in terms of linear phase and stability. Show in Figure 1, discrete time FIR filter of order N in transform notations. It contains N- stage delay lines with N+1 taps. Output y is a linear time invariant (LTI) system determine by convolving its input signal x (n) with its **impulse** **response** h (n).

The indoor power line channel is a time-varying fre- quency selective fading channel with interference due to the colored and impulsive noise generated by electrical appliances and external sources. Multipath **response** due to the power cable layout and loading conditions is also a problem. Nevertheless, the worst signal impairment is due to the short duration and high peak impulsive noise. To model the noise in PLC, we use a Gaussian mixture model .The probability density function (PDF) of the impulsive noise can be described as