This paper uses co-integration method and error-correction model to re-examine the relationship between real exchange rate and expected interest rate differentials, including cumulated current account balance, over floating exchange rate periods. As indicated by the dynamic model, I find that there is a long run relationship among the variables using Johansen co-integration method. Final conclusion is that the empirical evidence is provided to show that our error-correction model leads to a good real exchange rate forecast.

The relationship between increased productivity and improved standard of living is not a questioned statement on the global level, but does productivity growth necessarily lead to higher standard of living on the national level? Supported by empirical results it is suggested that a high relative productivity growth should not always be worth striving for, since it translates into decreased welfare, in terms of deteriorated terms of trade. This study attempts to examine the impact of relative productivity on the terms of trade in the OECD-countries and in Sweden, with an error-correction model. Further is an extension of the purpose made in order to estimate the impact of increased relative productivity growth on the welfare. The results suggest that the method for measuring productivity has a great impact on the findings, but concludes that a 1% higher relative labour productivity growth is associated with a 0.23% decline in the terms of trade.

Forecasting the stock market is a complex task, partly because of the random walk behavior of the stock price series. The task is further complicated by the noise, outliers and missing values that are common in financial time series. Despite of this, the subject receives a fair amount of attention, which probably can be attributed to the potential rewards that follows from being able to forecast the stock market. Since artificial neural networks are capable of exploiting non-linear relations in the data, they are suitable to use when forecasting the stock market. In addition to this, they are able to outperform the classic autoregressive linear models. The objective of this thesis is to investigate if the stock market can be forecasted, using the so called error correction neural network. This is accomplished through the development of a method aimed at finding the optimum forecast model. The results of this thesis indicates that the developed method can be applied successfully when forecasting the stock market. Of the five stocks that were forecasted in this thesis using forecast models based on the developed method, all generated positive returns. This suggests that the stock market can be forecasted using neural networks.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

This paper examines the forecast accuracy of an unrestricted Vector Autoregressive (VAR) model for GDP, relative to a comparable Vector Error Correction (VEC) model that recognizes that the data is characterized by co-integration. In addition, an alternative forecast method, Intercept Correction (IC), is considered for further comparison. Recursive out-of-sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VEC models consistently outperform the VAR models. Further, IC enhances the forecast accuracy when applied to the VEC model, while there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the VEC IC model compared to the VAR model.

This study aimed to investigate a) which type of written feedback (direct feedback or use of error codes) university prep-school EFL students with elementary level of proficiency prefer to receive on their written texts, b) whether or not the (mis)match between students&rsquo / preferences and received feedback affect their level of improvement in writing, and c) to what extent the students&rsquo / previous writing experience affect their preference for the type of written feedback. In order to determine the students&rsquo / preferences for a specific type of feedback and to find out their previous writing experiences, a questionnaire was designed. Moreover, the participants were given two subsequent writing tasks with the purpose of determining the level of improvement in their linguistic accuracy after receiving their (not) preferred type of feedback. The results show that the majority of the students in the preparatory class with Elementary level prefer to receive use of error codes in their written texts / however, giving them what they ask for may not contribute to their improvement as would be expected. Moreover, some conclusions are made as to the relationship between the students&rsquo / previous writing experience and their current practices.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.

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In order to convert the analog information in the digital domain, pipelined analog-to-digital converter (ADC) offers an optimum balance of resolution, speed, power consumption, size and design effort. In this thesis work we design and optimize a 8-bit pipelined ADC for low-power. The ADC has stage resolution of 1.5-bit and employ current mode multiplying analog-to-digital converter (MDAC). The main focus is to design and optimize the MDAC. Based on the analysis of "On current mode circuits" discussed in chapter 2, we design and optimize the MDAC circuit for the best possible effective number of bits (ENOB), speed and power consumption. Each of the first six stages consisting of Sample-and-Hold, 1.5-bit flash ADC and MDAC is realized at the circuit level. The last stage consisting of 2-bit flash ADC is also realized at circuit level. The delay logic for synchronization is implemented in Verilog-A and MATLAB. A first order digital error-correction algorithm is implemented in MATLAB. The design is simulated in UMC 0.18um technology in Cadence environment. The choice of technology is made as the target application for the ADC, 'X-ray Detector System' is designed in the same technology. The simulation results obtained in-term of ENOB and power consumption are satisfactory for the target application.

The use of wireless technology in the process industry is becoming increasingly important to obtain fast deployment at low cost. However, poor channel quality often leads to retransmissions, which are governed by Automatic Repeat Request (ARQ) schemes. While ARQ is a simple and useful tool to alleviate packet errors, it has considerable disadvantages: retransmissions lead to an increase in energy expenditure and latency. The use of Forward Error Correction (FEC) however offers several advantages. We consider a Hybrid-ARQ-Adaptive-FEC scheme (HAF) based on BCH codes and Channel State Information. This scheme is evaluated on AWGN and fading channels. It is shown that HAF offers significantly improved performance both in terms of energy efficiency and latency, as compared to ARQ.