There is much evidence that people are inaccurate in predicting the impact of future situations on their emotions. At the same time, affective forecasts have important implications for behavior, decision-making, and current mood, and may play an important role in the maintenance of emotional disorders. This study investigated two factors that influence affective forecasting: (1) Whether affective forecasting is associated with depressive symptoms and (2) Whether strategies people use to regulate their current affect influence their predictions of future emotional responses. Participants ruminated or reappraised in response to a sad mood and completed a measure of depressive symptoms (BDI). Results indicated that severity of depression symptoms was related to forecasts of greater sadness and anger to positive scenarios, as well as negative appraisals of future negative events. As expected, both BDI score and habitual use of emotion regulation strategies were correlated with participants' predictions about use and effectiveness of emotion regulation strategies in response to future scenarios. Results reinforced the usefulness of examining future-oriented cognitive processes in depression, providing insight into the role of hopelessness in the disorder. This study also shed light on the relationship between depression and predictions about the use and effectiveness of various emotion regulation strategies.

This dissertation can be broadly divided into two independent parts. The first three chapters analyse issues related to temporal and contemporaneous aggregation of econometric models. The fourth chapter contains an application of Bayesian techniques to investigate whether the post transition fiscal policy of Poland is sustainable in the long run and consistent with an intertemporal budget constraint. Chapter 1 surveys the econometric methodology of temporal aggregation for a wide range of univariate and multivariate time series models. A unified overview of temporal aggregation techniques for this broad class of processes is presented in the first part of the chapter and the main results are summarized. In each case, assuming to know the underlying process at the disaggregate frequency, the aim is to find the appropriate model for the aggregated data. Additional topics concerning temporal aggregation of ARIMA-GARCH models (see Drost and Nijman, 1993) are discussed and several examples presented. Systematic sampling schemes are also reviewed. Multivariate models, which show interesting features under temporal aggregation (Breitung and Swanson, 2002, Marcellino, 1999, Hafner, 2008), are examined in the second part of the chapter. In particular, the focus is on temporal aggregation of VARMA models and on the related concept of spurious instantaneous causality, which is not a time series property invariant to temporal aggregation. On the other hand, as pointed out by Marcellino (1999), other important time series features as cointegration and presence of unit roots are invariant to temporal aggregation and are not induced by it. Some empirical applications based on macroeconomic and financial data illustrate all the techniques surveyed and the main results. Chapter 2 is an attempt to monitor fiscal variables in the Euro area, building an early warning signal indicator for assessing the development of public finances in the short-run and exploiting the existence of monthly budgetary statistics from France, taken as "example country". The application is conducted focusing on the cash State deficit, looking at components from the revenue and expenditure sides. For each component, monthly ARIMA models are estimated and then temporally aggregated to the annual frequency, as the policy makers are interested in yearly predictions. The short-run forecasting exercises carried out for years 2002, 2003 and 2004 highlight the fact that the one-step-ahead predictions based on the temporally aggregated models generally outperform those delivered by standard monthly ARIMA modeling, as well as the official forecasts made available by the French government, for each of the eleven components and thus for the whole State deficit. More importantly, by the middle of the year, very accurate predictions for the current year are made available. The proposed method could be extremely useful, providing policy makers with a valuable indicator when assessing the development of public finances in the short-run (one year horizon or even less). Chapter 3 deals with the issue of forecasting contemporaneous time series aggregates. The performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA (VARMA) processes is compared. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is a predictor obtained from aggregation of univariate forecasts for the individual components of the data generating vector process. The econometric framework is broadly based on Lütkepohl (1987). The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods in the bivariate VMA(1) case is provided. It is argued that the condition of equality of predictors as stated in Lütkepohl (1987), although necessary and sufficient for the equality of the predictors, is sufficient (but not necessary) for the equality of mean squared errors. Furthermore, it is shown that the same forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure. Finally, an empirical application that involves the problem of forecasting the Italian monetary aggregate M1 on the basis of annual time series ranging from 1948 until 1998, prior to the creation of the European Economic and Monetary Union (EMU), is presented to show the relevance of the topic. In the empirical application, the framework is further generalized to deal with heteroskedastic and cross-correlated innovations. Chapter 4 deals with a cointegration analysis applied to the empirical investigation of fiscal sustainability. The focus is on a particular country: Poland. The choice of Poland is not random. First, the motivation stems from the fact that fiscal sustainability is a central topic for most of the economies of Eastern Europe. Second, this is one of the first countries to start the transition process to a market economy (since 1989), providing a relatively favorable institutional setting within which to study fiscal sustainability (see Green, Holmes and Kowalski, 2001). The emphasis is on the feasibility of a permanent deficit in the long-run, meaning whether a government can continue to operate under its current fiscal policy indefinitely. The empirical analysis to examine debt stabilization is made up by two steps. First, a Bayesian methodology is applied to conduct inference about the cointegrating relationship between budget revenues and (inclusive of interest) expenditures and to select the cointegrating rank. This task is complicated by the conceptual difficulty linked to the choice of the prior distributions for the parameters relevant to the economic problem under study (Villani, 2005). Second, Bayesian inference is applied to the estimation of the normalized cointegrating vector between budget revenues and expenditures. With a single cointegrating equation, some known results concerning the posterior density of the cointegrating vector may be used (see Bauwens, Lubrano and Richard, 1999). The priors used in the paper leads to straightforward posterior calculations which can be easily performed. Moreover, the posterior analysis leads to a careful assessment of the magnitude of the cointegrating vector. Finally, it is shown to what extent the likelihood of the data is important in revising the available prior information, relying on numerical integration techniques based on deterministic methods.

This dissertation consists of five essays. Three of these study countries’ specialisation patterns combining the two classical paradigms of trade theory, namely the Ricardian (technology) and the Heckscher–Ohlin (factor endowments) framework. Of the remaining two essays, one studies convergence in per capita income among the Swedish counties and the other is methodological in that we investigate the issue of how seasonal unit roots and joint modelling may affect forecasts. In each of these essays, an empirical investigation is applied. Essay 1. Technical Progress, Capital Accumulation and Changing International Competitiveness.In this essay we studies how technology, measured by total factor productivity (TFP) and endowments, jointly determines countries’ specialisation patterns.The main findings are that endowments and technology jointly determine trade patterns. In analysing countries level of specialisation we find indications of scale effects at the firm level and that TFP turns out to be a poor determinant in explaining specialisation whereas endowments, and in particular natural resources are significant. When analysing changes in specialisation and trade patterns, TFP growth is found to be a significant explanatory variable. These contradictory results, i.e., that TFP is not significant when studying levels but is when studying changes, may to some extent be explained by potential time invariant measurement errors that are differenced out when analysing changes. There is also evidence for an increased specialisation of human capital intensive production in countries with a high growth rate in the national supply of skilled labour. Essay 2. Technology, Resource Endowments and International Competitiveness.In the second essay we takes the analysis one step further by going behind the black box of technology and relating this to its sources, where R&D is taken to be the new main object of the study. The analysis reveals that competitiveness is determined not only by R&D performance of the firm, but also that industry- and economy-wide stocks of knowledge are important, indicating the presence of local externalities in R&D. Further results point to scale effects in R&D at the firm level and that the impact of R&D is higher in high- and medium- than in low-tech industries. Essay 3. The Dynamics of European Industrial Structure.The third essay focus on changes in countries’ specialisation patterns. In the model building stage, we make the R&D process endogenous. Through domestic input-output linkages, we build in trade-transmitted technology transfers. Econometrically, we find indications of R&D at the firm level to be the main engine shaping technology and competitiveness. There is also evidence of scale effects in R&D at the firm level. Analysing capital accumulation, we find that countries with relatively high capital accumulation increase their specialisation in capital-intensive industries. We also find that capital abundant countries have the highest rate of capital accumulation. Together, this indicates an increased concentration of capital-intensive industries in capital abundant countries. Analysing human capital accumulation in an analogous manner, we find that countries with relatively high human capital accumulation increase their specialisation in human capital intensive industries. However, we find that countries with a relatively high human capital accumulation are those with initially small human capital stocks, indicating convergence in human capital abundance among the countries in the sample. How industries interact, and industrial interdependence, are analysed, and we find significant econometric evidence of interdependence between domestic industries with strong input-output linkages. Essay 4. Convergence, Prices and Geography: An empirical Study of the Swedish Counties 1911-1993.With Joakim Persson.In the fourth essay, we analyse convergence in per capita income among the Swedish counties during the period 1911-93. Some innovative features in this essay are that we explicitly introduce distance in the econometric analysis and construct regional price indices. In the econometric analysis, we find both absolute and conditional convergence in all ten year periods from 1911 to 1993 except in the 20s and 80s. We find no convergence whatsoever in the 20s and only conditional convergence in the 80s. Analysing counties’ interdependence, we find that counties are tied together such that growth in one county will have a significant impact on its neighbours. Further, we find that the regional cost of housing affects counties’ demographic composition and, through this mechanism, growth in per capita income. Essay 5. The Impact of Seasonal Unit Roots and Vector ARMA Modelling on Forecasting Monthly Tourism Flows.With Jonas Nordström.In the fifth and final essay we analyse how neglecting seasonal unit roots and vector ARMA modelling affect forecasts. We study the flow of monthly tourism flows into Sweden. The main conclusion is that the Box and Jenkins approach, taking a 12th difference to make the series stationary, is at least as good as the much more demanding route of analysing seasonal unit roots. In a second step, we investigate potential gains in using joint modelling techniques when making forecasts. We utilise other tourism series in order to improve the forecasts. The results are mixed. The results depend on what evaluation criteria we choose. In summary, find the Box and Jenkins approach to stand up well against more advanced techniques. Essay no 1 has been published as:Gustavsson, P., Hansson, P. and Lundberg, L., "Technical Progress, Capital Accumulation and Changing International Competitiveness" in Fagerberg, J. et al (eds.), Technology and International Trade, pp 20-37. Edward Elgar, 1997.  Essay no 2 has been published as:Gustavsson, P., Hansson, P. and Lundberg, L.., "Technology, resource endowments and international competitiveness." in European Economic Review, Vol. 43, No. 8, 1999, pp 1501-1530. / Diss. Stockholm : Handelshögsk., 2001

This thesis, which consists of four essays, focus on seasonal and periodic cointegration models. These models are tools to describe changing seasonality.Essay 1 "Forecasting performance of seasonal cointegration models", with Johan Lyhagen. Forecasts from two different seasonal cointegration specifications are compared in an empirical forecasting example and in a Monte Carlo study. One of the two specifications include a certain parameter restriction at the annual frequency, wheras the other specification is more general. In the empirical forecasting example we also include a standard cointegration model based on first differences and seasonal dummies and analyze the effects of restricting or not restricting seasonal dummies in the seasonal cointegration models. While the Monte Carlo results favor the general specification, and definitely so if larger sample sizes are considered, we do not find such clear cut evidence in the empirical example.Essay 2 "On forecasting cointegrated seasonal time series", with Philip Hans Franses. In this essay we analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equations methods for those two classes of models. A VAR model in first differences, with and without cointegration restrictions, and a VAR model in annual differences are also included in the analysis, where they serve as benchmark models. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step ahead forecasts are considered. For longer forecast horizons however, the VAR model in annual differences is better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Essay 3 "Size and power of the likelihood ratio test for seasonal cointegration in small samples: A Monte Carlo study", This essay investigates the small sample size and power properties of the likelihood ratio test in the seasonal error correction model. Two specifications of the model at the annual frequency are analyzed. One is more restricted (RS), designed for the particular case of 'synchronous cointegration', whereas the other specification is general (GS). The results indicate that RS has poor size properties in cases where non-synchronous cointegration clearly should play a role. There is a risk of finding 'evidence' of too many cointegrating vectors at the annual frequency when using RS. On the other hand, if the restriction is almost satisfied, the general specification looses power at least for small sample sizes, while tests in RS have good properties. Essay 4 "On seasonal error correction when the processes include different numbers of unit roots", with Johan Lyhagen. We propose a seasonal error correction model (SECM) for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of specifying a SECM with all variables in annual diffrerences in this situation. The SECM in annual differences is compared to the correctly specified model. Pre-testing for unit roots using two different approaches, and where the models are specified according to the unit root test results, is also considered. The results indicate that, in practice, a cointegration model where all variables are transformed with the annual difference filter is more robust than one obtained by pre-testing for a smaller number of unit roots. / Diss. Stockholm : Handelshögsk., 2001 [4], iv s., s. 1-23: sammanfattning, s. 25-110, [5] s.: 4 uppsatser

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.

This is an empirical study which goal is to determine what causes changes in housing prices. It is done by using data for Stockholm and Sydney to create a model to forecast the change of house prices in the two cities. The findings suggest that the main determinants are nominal interest, household income, and the supply of new dwellings. This is in line with previous studies. It is also investigated whether the use of financial indicators such as the development of the stock market has an impact on the house prices. The findings regarding the implication of the financial indicators are dubious. Lastly, an investigation is made to see whether the so-called “ripple effect” can be applied to an international level. The inclusion of the ripple effect seems to be positive to the forecasting models used in this paper.

Vi ställs dagligen inför väldigt många val och beroende på vilka val vi väljer att göra kommer dessa till stor del att påverka hur vi lever vårt liv och även hur tillfreds vi kommer att vara med livet. Vi baserar många av våra val på känslomässiga förutsägelser som är våra antaganden om hur framtida händelser kommer att påverka oss känslomässigt. Våra känslomässiga förutsägelser är dessvärre ofta påverkade av olika bias som gör att vi missbedömer hur starkt och under hur lång tid vi kommer att reagera känslomässigt på kommande händelser, vilket i sin tur påverkar vilka val vi kommer att göra. Den här uppsatsen kommer att undersöka hur våra känslomässiga förutsägelser påverkar vårt subjektiva välbefinnande och även hur förståelse för detta skulle kunna användas i psykologisk coachning i form av interventioner för att hjälpa människor att göra fler val som leder till ett ökat subjektivt välbefinnande.

Little research has been done into implied correlations, and the small literature grows even smaller when referring to currency options. The existing literature has established that implied correlation is a good if not the best forecaster of future realized correlation, and that this ability to forecast is not necessarily universal. This paper will establish that the forecasting ability of implied correlations in currency options varies across currency pairs, thus proving that not all implied correlations are created equal. Using two different proxies for the quality of the forecaster, the paper attempts to explain which characteristics of an option on a currency pair affect the variation in forecasting ability.

When forecasting time series variables, it is usual to use only the information provided by past observations to foresee potential future developments. However, if available, additional information should be taken into account to get the forecast. For example, let us consider a case where the Government announces an economic target for next year. Since the Government has the empowerment to implement the economic or social policies to approach the target, an analyst that does not consider this information to get the forecast and makes use only of the historical record of the variables, will not anticipate the change on the economic system. In fact, if predictions based on historical data would be invalid when a policy change affects the economy, the economic agents are forward rather than backward-looking and adapt their expectations and behavior to the new policy stance. Thus, given some targets for the variables under study it is important to know the simultaneous future path that will lead to achieving those targets. Here it is considered the case in which a system of variables are to be forecasted with the aid of a VAR model with a cointegration relationship. The paths projected forward into the future as a combination of the model-based forecasts and the additional information provides what is known as a restricted forecast.This work is an attempt to contribute to the literature on Restricted Forecasting Theory for Multiple Time Series within the VAR framework. Specifically, Chapter 2 decomposes the JCT into single tests by a variance-covariance matrix associated with the restrictions and derives the formulas of a feasible JCT that accounts for estimated parameters. Chapter 3 develops, by Lagrangian optimization, the restricted forecasts of the multiple time series process with structural change, as well as its mean squared error. In addition, the univariate time series types of changes are considered here in a multivariate setting. Finally, Chapter 4 derives a methodology for forecasting multivariate time series that satisfy a contemporaneous binding constraint for which there exists a future target. A Monte Carlo study of a VEC model with one unit root shows that, for a forecast horizon large enough, the forecasts obtained with the proposed methodology are more efficient. A more detailed account of these contributions is provided below.

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the  used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model.