| 1. |
- Li, Dao
(författare)
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Common features in vector nonlinear time series models
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in thesearea.Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified.The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An F-type test for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail, and thereafter illustrated within two corresponding macroeconomic data sets.
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| 2. |
- Lundin, Mathias, 1971-
(författare)
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Sensitivity Analysis of Untestable Assumptions in Causal Inference
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the research field of causal inference, where the effect of a treatment on an outcome is of interest is concerned. Many such effects cannot be estimated through randomised experiments. For example, the effect of higher education on future income needs to be estimated using observational data. In the estimation, assumptions are made to make individuals that get higher education comparable with those not getting higher education, to make the effect estimable. Another assumption often made in causal inference (both in randomised an nonrandomised studies) is that the treatment received by one individual has no effect on the outcome of others. If this assumption is not met, the meaning of the causal effect of the treatment may be unclear. In the first paper the effect of college choice on income is investigated using Swedish register data, by comparing graduates from old and new Swedish universities. A semiparametric method of estimation is used, thereby relaxing functional assumptions for the data. One assumption often made in causal inference in observational studies is that individuals in different treatment groups are comparable, given that a set of pretreatment variables have been adjusted for in the analysis. This so called unconfoundedness assumption is in principle not possible to test and, therefore, in the second paper we propose a Bayesian sensitivity analysis of the unconfoundedness assumption. This analysis is then performed on the results from the first paper. In the third paper of the thesis, we study profile likelihood as a tool for semiparametric estimation of a causal effect of a treatment. A semiparametric version of the Bayesian sensitivity analysis of the unconfoundedness assumption proposed in Paper II is also performed using profile likelihood. The last paper of the thesis is concerned with the estimation of direct and indirect causal effects of a treatment where interference between units is present, i.e., where the treatment of one individual affects the outcome of other individuals. We give unbiased estimators of these direct and indirect effects for situations where treatment probabilities vary between individuals. We also illustrate in a simulation study how direct and indirect causal effects can be estimated when treatment probabilities need to be estimated using background information on individuals.
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| 3. |
- Rota, Bernardo João, 1980-
(författare)
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Calibration Adjustment for Nonresponse in Sample Surveys
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we discuss calibration estimation in the presence of nonresponse with a focus on the linear calibration estimator and the propensity calibration estimator, along with the use of different levels of auxiliary information, that is, sample and population levels. This is a fourpapers- based thesis, two of which discuss estimation in two steps. The two-step-type estimator here suggested is an improved compromise of both the linear calibration and the propensity calibration estimators mentioned above. Assuming that the functional form of the response model is known, it is estimated in the first step using calibration approach. In the second step the linear calibration estimator is constructed replacing the design weights by products of these with the inverse of the estimated response probabilities in the first step. The first step of estimation uses sample level of auxiliary information and we demonstrate that this results in more efficient estimated response probabilities than using population-level as earlier suggested. The variance expression for the two-step estimator is derived and an estimator of this is suggested. Two other papers address the use of auxiliary variables in estimation. One of which introduces the use of principal components theory in the calibration for nonresponse adjustment and suggests a selection of components using a theory of canonical correlation. Principal components are used as a mean to accounting the problem of estimation in presence of large sets of candidate auxiliary variables. In addition to the use of auxiliary variables, the last paper also discusses the use of explicit models representing the true response behavior. Usually simple models such as logistic, probit, linear or log-linear are used for this purpose. However, given a possible complexity on the structure of the true response probability, it may raise a question whether these simple models are effective. We use an example of telephone-based survey data collection process and demonstrate that the logistic model is generally not appropriate.
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| 4. |
- Yang, Yishen, 1984-
(författare)
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On Rank-invariant Methods for Ordinal Data
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data from rating scale assessments have rank-invariant properties only, which means that the data represent an ordering, but lack of standardized magnitude, inter-categorical distances, and linearity. Even though the judgments often are coded by natural numbers they are not really metric. The aim of this thesis is to further develop the nonparametric rank-based Svensson methods for paired ordinal data that are based on the rank-invariant properties only.The thesis consists of five papers. In Paper I the asymptotic properties of the measure of systematic disagreement in paired ordinal data, the Relative Position (RP), and the difference in RP between groups were studied. Based on the findings of asymptotic normality, two tests for analyses of change within group and between groups were proposed. In Paper II the asymptotic properties of rank-based measures, e.g. the Svensson’s measures of systematic disagreement and of additional individual variability were discussed, and a numerical method for approximation was suggested. In Paper III the asymptotic properties of the measures for paired ordinal data, discussed in Paper II, were verified by simulations. Furthermore, the Spearman rank-order correlation coefficient (rs) and the Svensson’s augmented rank-order agreement coefficient (ra) were compared. By demonstrating how they differ and why they differ, it is emphasized that they measure different things. In Paper IV the proposed test in Paper I for comparing two groups of systematic changes in paired ordinal data was compared with other nonparametric tests for group changes, both regarding different approaches of categorising changes. The simulation reveals that the proposed test works better for small and unbalanced samples. Paper V demonstrates that rank invariant approaches can also be used in analysis of ordinal data from multi-item scales, which is an appealing and appropriate alternative to calculating sum scores.
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| 5. |
- Andrén, Daniela, Associate Professor, 1968-, et al.
(författare)
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Individual wellbeing and cortisol
- 2024
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Ingår i: Encyclopedia of Happiness, Quality of Life and Subjective Wellbeing. - : Edward Elgar Publishing. - 9781800889668 - 9781800889675 ; , s. 125-133
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Bokkapitel (refereegranskat)abstract
- The variety and the number of ways of measuring individual wellbeing has increased over the past two decades. In addition to all self-reported measures, researchers also consider a wide variety of objectively-measured indicators of wellbeing (e.g., blood pressure, pulse rate, and the pattern of activity in different parts of the brain). However, it has not yet been established if the analysis of one only of these measurement concepts suffices, or rather whether more can be learnt from the joint analyses of both subjective and objective adult wellbeing indicators. This chapter briefly reviews this question, focussing on cortisol (as a potential objective measure) and life satisfaction (as a subjective measure), and suggests directions for future research.
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| 6. |
- Spånberg, Erik, 1990-
(författare)
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Variational Inference of Dynamic Factor Models
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- When we make difficult and crucial decisions, forecasts are powerful and important tools. For that purpose, statistical models can be our most effective aid. Ideally, these models can incorporate large sets of multifaceted data. However, time and computational power may limit our ability to utilize such tools effectively, not least in changeable situations that require frequent updates.This thesis takes a popular model for big data analysis, the dynamic factor model (DFM), and makes it more viable for fast-paced analytics in practice. The DFM assumes that data is driven by latent and unobserved dynamic factors. By estimating these factors, we may gain deeper insights of our data and ultimately predictive power. An appealing method for DFM-estimation is Bayesian inference, producing probability distributions (called posterior distributions) of factors and parameters.We develop variational inference, which approximates Bayesian inference, in order to estimate DFMs very quickly. By this method, DFMs can be estimated in a fraction of computational time relative to standard approaches; hourly long estimations reduce to seconds or a few minutes.Additionally, we allow for any arbitrary missing data pattern. We can consider data of various sizes and shapes, including different sample sizes, unsynchronized publications and mixed frequencies.In the first paper, we develop an “Expectation Maximization” algorithm to find the maximum point of the posterior distribution of DFM parameters. This can be seen as a reduced case of variational inference. A simulation study shows that the method is preferable to the more common maximum likelihood approach.In the second paper we develop variational inference of standard DFMs. Empirical examples show that this method approximates posterior distributions of factors and parameters very well and quickly. Predictive distributions, both in and out of sample, are practically indistinguishable from standard counterparts of much slower simulation techniques.In the third paper we extend the approach to explicitly sort the relevant and irrelevant parts of the data, corresponding to each individual factor. We employ so called “spike-and-slab” priors, such that individual connections between factors and data can be switched on or off. These “switches” become part of the estimation problem. Simulation studies show that the method identifies the connections well.The fourth paper is an application. We construct a very large DFM to predict Swedish macroeconomy, including 250 quarterly and 500 monthly times series, with different sample sizes and publication dates. To our knowledge, this is the largest prediction model on Swedish macroeconomy to date. We introduce a non-dogmatic structural framework, where we direct the analysis to certain features without strictly deciding them. This produces factor estimates interpretable in terms of consumption, production, prices, financial markets and more. Each time series and forecast can be decomposed according to these interpretations. A forecast evaluation shows good prediction precision of blocks of time series, as well as some key series individually. The model can be updated quickly, making it operable in practice, due to the speed acquired from variational inference.
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| 7. |
- Tongur, Can, 1982-
(författare)
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Seasonal Adjustment and Dynamic Linear Models
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this framework to do seasonal adjustments of empirical and artificial data. A simple model and an extended model based on Gibbs sampling are used and the results are compared with the results of a standard seasonal adjustment method. The state space approach is then extended to discuss direct and indirect seasonal adjustments. This is achieved by applying a seasonal level model with no trend and some specific input variances that render different signal-to-noise ratios. This is illustrated for a system consisting of two artificial time series. Relative efficiencies between direct, indirect and multivariate, i.e. optimal, variances are then analyzed. In practice, standard seasonal adjustment packages do not support optimal/multivariate seasonal adjustments, so a univariate approach to simultaneous estimation is presented by specifying a Holt-Winters exponential smoothing method. This is applied to two sets of time series systems by defining a total loss function that is specified with a trade-off weight between the individual series’ loss functions and their aggregate loss function. The loss function is based on either the more conventional squared errors loss or on a robust Huber loss. The exponential decay parameters are then estimated by minimizing the total loss function for different trade-off weights. It is then concluded what approach, direct or indirect seasonal adjustment, is to be preferred for the two time series systems. The dynamic linear modeling approach is also applied to Swedish political opinion polls to assert the true underlying political opinion when there are several polls, with potential design effects and bias, observed at non-equidistant time points. A Wiener process model is used to model the change in the proportion of voters supporting either a specific party or a party block. Similar to stock market models, all available (political) information is assumed to be capitalized in the poll results and is incorporated in the model by assimilating opinion poll results with the model through Bayesian updating of the posterior distribution. Based on the results, we are able to assess the true underlying voter proportion and additionally predict the elections.
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| 8. |
- Berger, Helge, et al.
(författare)
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A note of caution on the relation between money growth and inflation
- 2023
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Ingår i: Scottish Journal of Political Economy. - : John Wiley & Sons. - 0036-9292 .- 1467-9485. ; 70:5, s. 479-496
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Tidskriftsartikel (refereegranskat)abstract
- We assess the bivariate relation between money growth and inflation in the euro area and the United States using hybrid time-varying parameter Bayesian VAR models. Model selection based on marginal likelihoods suggests that the relation is statistically unstable across time in both regions. The effect of money growth on inflation weakened notably after the 1980s before strengthening after 2020. There is evidence that this time variation is related to the pace of price changes, as we find that the maximum impact of money growth on inflation is increasing in the trend level of inflation. These results caution against asserting a simple, time-invariant relationship when modeling the joint dynamics of monetary aggregates and consumer prices.
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| 9. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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A Hybrid Time-Varying Parameter Bayesian VAR Analysis of Okun’s Law in the United States
- 2020
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Ingår i: Economics Letters. - : Elsevier. - 0165-1765 .- 1873-7374. ; 197
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Tidskriftsartikel (refereegranskat)abstract
- Employing quarterly data on GDP growth and the unemployment rate ranging from 1948Q3 to 2019Q4, we study the stability of Okun’s law in the United States. This is done by estimating hybrid time-varying Bayesian VAR models that allow for time-variation in none, one or both of the equations. Model comparison based on marginal likelihoods suggests that the relationship has not been stable. However, the amount of change in the dynamic relationship between the two variables is quantitatively very modest.
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| 10. |
- Karlsson, Sune, Professor, 1960-, et al.
(författare)
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A Note on the Stability of the Swedish Phillips Curve
- 2020
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Ingår i: Empirical Economics. - : Springer. - 0377-7332 .- 1435-8921. ; 59:6, s. 2573-2612
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Tidskriftsartikel (refereegranskat)abstract
- We use Bayesian techniques to estimate bivariate VAR models for Swedish unem-ployment rate and inflation. Employing quarterly data from 1995Q1 to 2018Q3 and new tools for model selection, we compare models with time-varying parameters and/or stochastic volatility to specifications with constant parameters and/or covariance matrix. The evidence in favour of a stable dynamic relationship between the unemployment rate and inflation is mixed. Model selection based on marginal like-lihood calculations indicates that the relation is time varying, whereas the use of the deviance information criterion suggests that it is constant over time; we do, however, note consistent evidence in favour of stochastic volatility. An out-of-sample forecast exercise is also conducted, but similarly provides mixed evidence regarding which model to favour. Importantly though, even if time-varying parameters are allowed for, our results do not suggest that the Phillips curve has been flatter in more recent years. This finding thereby questions the explanation that a flatter Phillips curve is the cause of the low inflation that Sweden has experienced in recent year.
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