Rapid increases in consumer food price beginning in 2007 generated interest in identifying the main factors influencing these increases. In subsequent years, food prices have fluctuated, but generally have continued their ascent. The effects of crude oil, gasoline, corn, and ethanol prices, as well as, the relative foreign exchange rate of the U.S. dollar and producer price indexes for food manufacturing and fuel products on domestic food prices are examined. Because the data series are non-stationary and cointegrated, a vector error correction model is estimated. Weak exogeneity and exclusion tests in the cointegration space are performed. Directed acyclical graphs are used to specify contemporaneous causal relationships. Dynamic interactions among the series are given by impulse response functions and forecast error variance decompositions. Weak exogeneity tests indicate all eight series work to bring the system back into equilibrium following a shock to the system. Further, exclusion tests suggest crude oil, gasoline, food CPI, ethanol, and food PPI variables are not in the long-run relationships. Dynamic analyses suggest the following relationships. Ethanol price is not a major factor in domestic food prices, suggesting that food prices are largely unaffected by the recent increased use of corn-based ethanol for fuel. Crude oil prices, corn prices, and the relative foreign exchange rate of the U.S. dollar, however, do influence domestic food prices with corn price contributing the most to food price variability. Innovation accounting inferences are robust to potential different contemporaneous causal specifications.

NO / The relationship between exports and economic growth has been analysed by a number of recent empirical studies. This paper re-examines the sources of growth for the period 1971-2001 for India. It builds upon Feder's (1983) model to investigate empirically the relationship between export growth and GDP growth (the export led growth hypothesis), using recent data from the Reserve Bank of India, and by focusing on GDP growth and GDP growth net of exports. We investigate the following hypotheses: (i) whether exports, imports and GDP are cointegrated using the Johansen approach and Breitung's nonparametric cointegration test; (ii) whether export growth Granger causes GDP growth; (iii) and whether export growth Granger causes investment. Finally, a VAR is constructed and impulse response functions (IRFs) are employed to investigate the effects of macroeconomic shocks.

Trade deficit, financial deficit, and current account deficit of the United States have all been problems deeply concerned by economists and politicians in recent decades. Since the third season of 2000, a recession of the United States and the whole world has gradually started to appear. In addition, as a result of the 9/11 terrorist attacks and the war in Iraq the stock market has begun to decline significantly. In order to promote the recovery of its economy, the federal government determines to adopt the expanded financial policy which will most likely in the end cause its financial deficit more serious. The main purpose of this paper is to investigate the factors that influence the current account deficit of the United States. Because the study considers foreign variables that related researches ignore, we choose five variables as follows: regional output differential, regional interest rate differential, terms of trade, regional real effective exchange rate, and current account. Therefore, we adopt the Unit Root Test, the Granger Causality Test, the Co-integrating Test, and SVAR (Structural Vector Autoregressive) model to run RATS and E-views. It is the finding of empirical result that the United States government considers terms of trade and current account that can't be quantized of the first importance rather than the exchange rate factor that general research is thought. This is one of the contributions of the study.

Vector autoregression (VAR) models are widely used in an attempt to identify and measure the effect of monetary policy shocks on an economy and to forecast economic times series. However, the sparse information sets used in the VAR approach have been subject to criticism and in recent decades, the use of factor models as a means of dimension reduction has been a subject of greater focus. The method of summarizing information contained in a large set of macroeconomic time series by principal components, and use these as regressors in VAR models, has been pointed out as a potential solution to the problems of limited information and estimation of too many parameters. This paper combines the standard VAR methodology with dynamic factor analysis on Swedish data for two purposes, to assess the effects of monetary policy shocks and to examine the forecasting properties. Latent factors estimated by the principal components method are in this study found to contribute to a more coherent picture in line with economic theory, when examining monetary policy shocks to the Swedish economy. The factor-augmented models can on the other hand not be shown to increase the forecasting accuracy to a great extent compared to standard models.

The transition from communism to capitalism at the end of the last century was one of the most significant events in the world economy since industrialization. During the latter part of the 1980s, people the Central and Eastern European countries and former Soviet Republics opted for a change from highly distorted command economic system to a market driven economic system. Privatization and liberalization policies led to major changes in the commodity mix and volume of agricultural production, consumption and trade. However, the changes and the impacts varied among countries as they followed different transition strategies. This study investigated the impact of market liberalization on the agricultural sector, as well as how the inter-sectoral linkages among the agricultural, industrial and service sectors responded in Poland, Romania, Bulgaria and Hungary using time-series analysis. The study estimated an econometric model that incorporates the linkages among the sectors using a Vector Error Correction Model. The procedure identified long-run and short-run relationships for each country. The results showed that a sector can have a negative linkage to other sectors in the short-run; however, that does not mean that the linkage will be negative in the long-run. Impulse response functions were constructed to determine how a system reacts to a shock in one of the endogenous variable in a model. The study explored how a shock in the agricultural sector was absorbed by the other sectors in the economy, and how a shock in the other sectors was absorbed by the agricultural sector, in all four countries. The responses reflected how the variables are interrelated within a country, and how the shocks are transferred through different linkages over a long period of time. Such dynamic analysis was used to identify the total impacts of different policy alternatives.

This thesis explores the price determinants of Bitcoin using a macroeconomic model based on the economic equation of exchange presented by Joseph Wang (2014). The thesis provides a concise and structured introduction to Bitcoin and a comprehensive literature review on Bitcoin. The analysis begins with the application of the functions of money to Bitcoin, arguing that while Bitcoin does fulfill the three classical functions of money to a certain extent, its use remains mainly as a speculative instrument. Wang's model is criticized and amended to reflect the realities of empirically analyzing the Bitcoin market. Using the daily number of transactions and Bitcoin days destroyed as proxies for economic activity and inactivity - to measure Bitcoin's velocity on the block chain - vector autoregression modelling is used to determine if there is Granger causality between the price of bitcoin and the two proxies. The results demonstrate that there is a bidirectional Granger-causal relationship between Bitcoin days destroyed and the price of bitcoin and that there is none between the daily number of transactions and the price of bitcoin; proving Wang's two main assumptions. Impulse- response functions are provided to illustrate and discuss this bidirectional relationship. The results are in line with the...

This paper focuses on analysing and modelling inflation and its dynamics in Czech Republic applying a special kind of econometric models. Firstly economic theory of inflation is mentioned - fundamental terms, measuring methods of inflation, the way Czech national bank is monitoring the inflation and obviously a short summary of historical evolution of inflation in Czech economy. In the second part of this paper two econometric concepts of modelling time series are introduced - vector autoregression models (VAR models) and volatility models, concretely ARCH and GARCH models. In connection with the VAR models, Granger causality, impulse response functions, cointegration and error correction models are described. The empirical part includes application of selected models on real time series of chosen macroeconomic indicators. The estimation outputs are interpreted and forecasts are implemented. The quality of chosen econometric models for modelling inflation in Czech Republic is discussed.

The aim of this paper is to analyse and compare inflation and its dynamics between two countries -- the Czech Republic and Germany -- applying a special kind of econometric models. The first part of this paper is dedicated to economic theory of inflation -- fundamental terms, measuring methods and its targeting. The monetary policy in the Czech Republic and Germany is also shortly introduced. Next chapter tries to describe the econometric concept which is used in this paper -- vector autoregression model (VAR model). In connection with the VAR models, Granger causality, impulse response function, cointegration and error correction model are mentioned as well. The empirical part includes application of selected models on real time series of macroeconomic indicators. Next to the interpretation of results, the forecasts are also implemented.

The aim of this diploma thesis is to empirically investigate the explanatory power of Austrian business cycle theory. My dataset consists of US quarterly time series within the period between 1971 and 2009. As regards the NBER classification, this dataset covers six complete business cycles, including the recent global financial crisis. Following Wainhouse (1984), Keeler (2001) and Bjerkenes et al. (2010) I use Granger causality as one of the primary tools of the analysis. Moreover I also add Impulse response functions to discover the direction of observed relationships. As regards my primary group of hypotheses I found significant empirical evidence for the connection between changes in interest rate and structure of production. The secondary group of hypotheses is less successful; however I found the very first empirical illustration of Garrison's version of ABCT.

Studien syftar till att undersöka hur den penningpolitiska transmissionsmekanismen, särskilt hur förändringar i styrräntan, påverkar hushållens konsumtion. Huvudsyftet som undersöks är huruvida effekten av konsumtion-förmögenhetskanalen existerar vid förändringar i styrräntan. Genom att estimera en SVAR- modell och använda impuls respons funktioner (IRF) kan vi besvara våra syften och analysera hur en chock med en standardavvikelse på styrräntan kommer att påverka hushållens konsumtionsbeteende. Vidare inaktiveras konsumtion- förmögenhetskanalen för att kunna göra en jämförelse med studiens tidigare estimat och påträffa en eventuell existens. Resultaten från studien visar att det inte finns några signifikanta indikationer på en konsumtion-förmögenhetskanal i samband med penningpolitiska åtgärder under tidsperioden från första kvartalet 1996 till fjärde kvartalet 2022. Slutsatsen av studien indikerar således att en konsumtion-förmögenhetskanal inte är den dominerande transmissionsmekanismen för att påverka hushållens konsumtion vid penningpolitiska åtgärder. / The study aims to examine how the monetary policy transmission mechanism, specifically changes in the policy interest rate, affect household consumption. The main objective investigated is whether the effect of the consumption-wealth channel exists during changes in the policy interest rate. By estimating a Structural Vector Autoregressive (SVAR) model and utilizing impulse response functions (IRFs), we can address our objectives and analyze how a shock of one standard deviation in the policy interest rate will impact household consumption behaviour. Furthermore, the consumption-wealth channel is deactivated to enable a comparison with the study's previous estimates and identify any potential existence. The results of the study indicate no significant evidence of a consumption-wealth channel associated with monetary policy measures during the period from the first quarter of 1996 to the fourth quarter of 2022. Thus, the study's conclusion suggests that the consumption-wealth channel is not the dominant transmission mechanism for influencing household consumption during monetary policy measures.