Nous proposons d’étudier la méthodologie de la segmentation de processus avec un bruit autorégressif sous ses aspects théoriques et pratiques. Par « segmentation » on entend ici l’inférence de points de rupture multiples correspondant à des changements abrupts dans la moyenne de la série temporelle. Le point de vue adopté est de considérer les paramètres de l’autorégression comme des paramètres de nuisance, à prendre en compte dans l’inférence dans la mesure où cela améliore la segmentation.D’un point de vue théorique, le but est de conserver un certain nombre de propriétés asymptotiques de l’estimation des points de rupture et des paramètres propres à chaque segment. D’un point de vue pratique, on se doit de prendre en compte les limitations algorithmiques liées à la détermination de la segmentation optimale. La méthode proposée, doublement contrainte, est basée sur l’utilisation de techniques d’estimation robuste permettant l’estimation préalable des paramètres de l’autorégression, puis la décorrélation du processus, permettant ainsi de s’approcher du problème de la segmentation dans le cas d’observations indépendantes. Cette méthode permet l’utilisation d’algorithmes efficaces. Elle est assise sur des résultats asymptotiques que nous avons démontrés. Elle permet de proposer des critères de sélection du nombre de ruptures adaptés et fondés. Une étude de simulations vient l’illustrer. / We propose to study the methodology of autoregressive processes segmentation under both its theoretical and practical aspects. “Segmentation” means here inferring multiple change-points corresponding to mean shifts. We consider autoregression parameters as nuisance parameters, whose estimation is considered only for improving the segmentation.From a theoretical point of view, we aim to keep some asymptotic properties of change-points and other parameters estimators. From a practical point of view, we have to take into account the algorithmic constraints to get the optimal segmentation. To meet these requirements, we propose a method based on robust estimation techniques, which allows a preliminary estimation of the autoregression parameters and then the decorrelation of the process. The aim is to get our problem closer to the segmentation in the case of independent observations. This method allows us to use efficient algorithms. It is based on asymptotic results that we proved. It allows us to propose adapted and well-founded number of changes selection criteria. A simulation study illustrates the method.

Master of Science / Department of Agricultural Economics / Hikaru H. Peterson / Organic has become a familiar term in agriculture, usually bringing to mind the phrases “no chemicals” and “large premiums.” While organic products usually command a substantial price premium over their conventional counterparts, the determinants of this premium are generally unknown. The lack of literature covering organic prices is not from a lack of interest but from a lack of information and data for organic commodities. This study examines two aspects of organic grain prices in an attempt to learn more about the organic grain sector. The first objective was to identify determinants of organic premiums received by members of a Kansas organic grain cooperative. Six different grains along with alfalfa hay were examined using hedonic models and bootstrapping statistical techniques. Findings of the hedonic analyses are as follows. Dairy farms seemed to pay a lower premium for feed grade corn and hard red winter wheat compared to other types of buyers. Buyers located in Kansas tended to provide a smaller premium than buyers located elsewhere. Early contract periods produced a smaller premium than later periods. Shipment timing was much the same, with fourth quarter shipments receiving the largest premium. Additionally, each subsequent contract year resulted in a larger premium. If the cooperative had arranged shipment of the commodity, a lower premium was acquired. Finally, longer contract lengths resulted in a larger premium. The second part of this study examined various price series of organic and conventional commodities to determine if the two markets were related. Using vector autoregressive models, cointegration and causality tests were conducted, and speed of adjustment to a shock in the long run equilibrium and exogeneity were also examined. Of the 43 pairs of organic and conventional price series tested, 29 were found to be cointegrated. Of those cointegrated pairs, 11 causal relationships were found. Five of these causal relationships indicated that the conventional commodity prices led the organic. There were six instances where the organic commodity prices were found to lead the conventional. For most causal relationships, about 5% of the adjustment to a shock, or divergence from long run equilibrium occurred in one week.

The degree work is focused on analysis of inflation with help of suitable econometric models. Inflation with it's forms and possibilities of measuring is described at the beginning of the paper. There is mentioned an importance of monitoring and analysing inflation in view of Czech national bank. Consequently there are described characteristics of time series, which are important from viewpoint of construction of econometric models. Next part of this paper is focused on characterization of econometrics models. At first there is vector autoregression model, in this connection there is discussed the essence of Granger causality and impulse reaction. There are also noticed both error correction model and vector error correction model. The empirical part of degree work involves the use of these models on selected macroeconomic time series of the Czech republic. The objective is to analyze the relationship between inflation and other individual macroeconomic quantities. There is established the optimal vector autoregressive model and the results of Granger causality and impulse reaction are interpretated. Both error correction model and vector error correction model examining cointegration are also applied.

Abstract   In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts

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This study empirically examines the relationship between financial development and economic growth in Nigeria. It employs statistical techniques such as the Autoregressive Distributed Lag approach as well as a short and long run Granger Causality test on time series data spanning from 1960-2016. Empirical results reveal that the financial development indicators have a long run relationship with economic growth in Nigeria and the existence of unidirectional and bidirectional Granger causality was also discovered. This study recommends that policy should be geared towards promoting financial development in the country as well as encouraging more financial depth and openness – in order to foster economic growth in Nigeria.

<div> <div> <div> <p>First Chapter: The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.<br></p><p><br></p><p>Second Chapter: This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.<br></p><p><br></p><p>Third Chapter: This paper presents a sample selection model with spatial autoregressive interactions and studies the maximum likelihood (ML) approach to estimating this model. Consistency and asymptotic normality of the ML estimator are established by the spatial near-epoch dependent (NED) properties of the selection and outcome variables. Monte Carlo simulations, based on the characteristics of female labor supply example, show that the proposed estimator has good finite-sample performance. The new model is applied to empirical study on examining the impact of climate change on agriculture in Southeast Asia.<br></p></div></div></div><div><div><div> </div> </div> </div>

Advances in modern technologies have led to an abundance of high-dimensional time series data in many fields, including finance, economics, health, engineering, and meteorology, among others. This causes the “curse of dimensionality” problem in both univariate and multivariate time series data. The main objective of time series analysis is to make inferences about the conditional distributions. There are some methods in the literature to estimate the conditional mean and conditional variance functions in time series. However, most of those are inefficient, computationally intensive, or suffer from the overparameterization. We propose some dimension reduction techniques to address the curse of dimensionality in high-dimensional time series dataFor high-dimensional matrix-valued time series data, there are a limited number of methods in the literature that can preserve the matrix structure and reduce the number of parameters significantly (Samadi, 2014, Chen et al., 2021). However, those models cannot distinguish between relevant and irrelevant information and yet suffer from the overparameterization. We propose a novel dimension reduction technique for matrix-variate time series data called the "envelope matrix autoregressive model" (EMAR), which offers substantial dimension reduction and links the mean function and the covariance matrix of the model by using the minimal reducing subspace of the covariance matrix. The proposed model can identify and remove irrelevant information and can achieve substantial efficiency gains by significantly reducing the total number of parameters. We derive the asymptotic properties of the proposed maximum likelihood estimators of the EMAR model. Extensive simulation studies and a real data analysis are conducted to corroborate our theoretical results and to illustrate the finite sample performance of the proposed EMAR model.For univariate time series, we propose sufficient dimension reduction (SDR) methods based on some integral transformation approaches that can preserve sufficient information about the response. In particular, we use the Fourier and Convolution transformation methods (FM and CM) to perform sufficient dimension reduction in univariate time series and estimate the time series central subspace (TS-CS), the time series mean subspace (TS-CMS), and the time series variance subspace (TS-CVS). Using FM and CM procedures and with some distributional assumptions, we derive candidate matrices that can fully recover the TS-CS, TS-CMS, and TS-CVS, and propose an explicit estimate of the candidate matrices. The asymptotic properties of the proposed estimators are established under both normality and non-normality assumptions. Moreover, we develop some data-drive methods to estimate the dimension of the time series central subspaces as well as the lag order. Our simulation results and real data analyses reveal that the proposed methods are not only significantly more efficient and accurate but also offer substantial computational efficiency compared to the existing methods in the literature. Moreover, we develop an R package entitled “sdrt” to easily perform our program code in FM and CM procedures to estimate suffices dimension reduction subspaces in univariate time series.

This paper examines the effects of monetary policy on macroeconomic variables in Botswana as a developing small macro-economy using the Markov-switching structural vector autoregressive (MS-SVAR) framework, utilising time-series data from 1994: Q1 to 2019: Q4. The study makes use of bank rate (interest rate), inflation and output gap. The first model is a structural vector autoregressive (VAR) model that takes the form employed by Rudebusch and Svensson (1999), whilst the second one makes use of the same structure but includes Markov switching in the policy rule (i.e., Markov switching SVAR). Regime-switching models can effectively describe the data generating process when considering both in-sample and out of sample evaluations compared to the linear models, which submerge the structural changes that have occurred in the economy over the years. The results from the SVAR shows that monetary policy has a symmetric impact on the output gap and inflation. Therefore, it can be noted that non-linearities in the structural model do not necessarily imply asymmetric effects of shocks. Furthermore, the MS-SVAR shows that the Central Bank of Botswana responds differently to policy shocks in different regimes. This underscores the importance of regime-switching features in providing a more accurate description of the economy.

This study aims to identify the effects of oil income on economic growth in Iran over the period 1955-2014. The empirical literature indicates that countries with natural resources are growing more slowly than their counterparts. However, the results from this literature are far from conclusive, particularly in regard to the role played by oil-rich countries. Needless to say, this role depends on other factors as well, including the political situation in the country, the quality of institutions, and the efficacy of the financial system. Some empirical research has found that natural resources, particularly oil, can have a positive impact on the output of a country. although natural resources are not a factor of production in growth theories, studies have used different growth frameworks in order to discover whether having natural resources is a blessing or a curse. In line with recent studies, this work uses an augmented neoclassical growth model to develop a theoretical framework where oil enters the long-term output of the country through saving and investment. Overall, the results suggests that oil income has a positive impact on the level of output per capita in Iran. The findings of the econometric results are in line with the historical analysis of the study. Since different methods and proxies were used, a total of eight models were estimated. Interestingly, when PRIVY is used as an index of financial development, the result of the study changes and oil no longer has a significant impact on the economy. However, this can be translated to an inefficient allocation of credit to the private sector.