4/28/2014

Summary of Complete Metric Spaces and Function Spaces-----It’s all about compactness.



     Royden once commented that the structure of a metric space by itself is too barren to be fruitful in the study of interesting problems in analysis and topology.[1] However, we can add one more property, namely completeness, to establish many theorems that are topological in character. This chapter is devoted to some of the most used examples of complete compact spaces and their properties.
     First we recall the notion of Cauchy sequence, which is directly related to the definition of completeness. A sequence {xn} in a metric space (X, d) is Cauchy if for all ε>0, there is an index N such that if n, m≥0, then d(xn, xm)<ε. A metric space is said to be complete if every Cauchy sequence in X converges to a point in X. Equivalently, we can say that metric space X is complete if every Cauchy sequence in X has a convergent subsequence. We can prove the equivalence easily by showing that the subsequence has the same limit point with the original sequence.
     Some metric spaces we are familiar with are actually complete. For example, Euclidean space Rk is complete either in Euclidean metric or in square metric. Let d_bar denote the standard bounded metric on R. If J is countable then the metric D(x,y)=sup{d_bar (xi, yi)/i} induces the product topology on Rω, and it is complete under this metric. When J is not countable, RJ is complete given the uniform metric. 
     Given topological spaces X and Y, we use C(X, Y) to denote the set of all continuous maps from X to Y. When X is a topological space and Y is a complete metric space, C(X, Y) is complete in the uniform metric. C(X, Y) is rather important in this chapter’s study because it turns out that we can impose many fruitful topologies on this space and derive a deep theorem, namely the Ascoli’s theorem. Before we get that far, we should empower ourselves with some new notions and relate them to what we have learned, such as compactness.
     One thing noticeable is that completeness is not a topological property because it is not preserved by homeomorphisms.[2] For example, on (-1, 1) with Euclidean metric, the sequence {xn} defined by xn=1-1/n is Cauchy but doesn’t converge (because limit point 1 is not in the space). However, R, which is homeomorphic to (-1, 1), is complete with Euclidean metric. This is also an example showing that in general, a subspace of a complete metric space is not complete. However, if the subset is closed, then it is complete and vice versa.
     Proof: suppose Y is a closed subset of X, which is complete and metrizable, and let the sequence {yn} to be a Cauchy sequence in Y. Because {yn} is also Cauchy in X, by definition of X being complete, {yn} converges to a point in X, say y. Since Y is closed, it contains the limit point of {yn}, which is y. By definition Y is complete.
     Conversely, assume Y is complete and we prove that it is closed by showing the limit of a convergent sequence in Y belongs to Y. Let {yn} be a sequence in Y and converges to y in X. Because X is a metric space, {yn} is also Cauchy, and thus by completeness of Y it contains limit point of {yn}, which is exactly y. So Y is closed.                                                                                                                                 (Q.E.D)
     An interesting application of completeness is the Peano space-filling curve, which passes through every point of the unit square. Roughly speaking, this curve is a continuous mapping from unit interval onto the unit square. The discovery was motivated by Cantor’s theory that R and R2 has the same cardinality, and it also had historical importance. For example, it motivated the study of algebraic topology by showing that result like Jordan Curve Theorem and invariance of domain might need more careful proofs[3].
     After having learned something about completeness, a natural question might emerge: is there any way that we can relate this definition to materials we have known before? The answer is “yes” because compactness and completeness can be combined together to form a new kind of metric space.
     Let us first recall a very important equivalence theorem with regard to metric space. Namely, if X is a compact metric space, then X is also limit point compact and sequentially compact. [4] Previously we have shown that a metric space where every Cauchy sequence has a convergent subsequence is complete, and thus it is clear that every compact metric space is complete (sequential compactness).
     Note that the converse is not true in general. One straightforward counterexample is the real line R under Euclidean metric (it is complete, but cannot be covered by finitely many open sets). Here comes a new question: what extra requirements should we put on a complete metric space so that it can also be compact? It turns out that we need to introduce a new notion: total boundedness.
     Definition: a metric space X is totally bounded if for each ε>0, X can be covered by a finite number of open balls of radius ε. A subset E of X is totally bounded provided that E, considered as a subspace of the metric space X, is totally bounded.
     A metric space (X, d) is compact if and only if it is complete and totally bounded. Munkres[5] laid out a detailed proof in section 45, so interested readers can refer to the book. The main reason we need total boundedness is because we can inductively construct a Cauchy sequence in X by choosing a decreasing radius epsilons, and with property of completeness, we can show that X is sequentially compact.
     Now that we know when a complete metric space is compact, our next step is to find the compact subspaces of C(X, Rn) in the uniform topology. By Heine-Borel theorem, a subspace of Euclidean space Rn is compact if and only if it is closed and bounded. However, in a general metric space, the condition of being closed and bounded is necessary but not sufficient. For example, the closed unit ball of C[0,1] is closed and bounded in C[0,1], but it fails to be sequentially compact and thus compact. As a remedy, we need to impose another property, i.e. equicontinuity, upon the subspace of C(X, Rn).
     Definition: Let (Y, d) be a metric space. Let F be a collection of real-valued functions of C(X, Y). If x0 belongs to X, then F is equicontinuous at x provided that for all ε>0, there is a neighbourhood U of x such that for all x’ in U, d(f(x), f(x’))<ε for all f in F.
     Note that equicontinuity is a stronger condition than continuity because it requires all functions in F to be continuous given the same choice of epsilon. In general, an infinite collection of continuous functions is not equicontinuous. For instance, define fn(x)=xn (0≤x≤1) for all n belongs to N. Then {fn} is a countable collection of continuous functions on [0, 1]. However, it is not equicontinuous at x=0.
     It turns out that if X is a space and (Y, d) is a metric space, and the subset F of C(X, Y) is totally bounded under the uniform metric corresponding to d, then F is equicontinuous under d[6]. If we further assume that X and Y are compact, the converse is also true. One thing worth mentioning about these two proofs is that I think when it comes to something related to equicontinuity, we can often use the ε/3 trick. Namely, we first use continuity of one function, say f, to create an ε/3 distance between f(x) and f(x’), in which x’ is in U(x). Then we can use corresponding uniform metric of continuous functions to define the other two inequality, and finally we use triangle inequality to wrap up the whole proof.
      Before we prove the classical version of Arzela-Ascoli theorem, we introduce two more definitions on a family of functions. A sequence of functions {fn} of C(X, Y) is pointwise bounded under d if for each x in X, the subset Fa={f(a)|f in F} of Y is bounded. {fn} is uniformly bounded if there is M≥0 such that |fn|≤M for all n.
     Now we can combine things together and prove the classical version of Ascoli’s theorem: Let X be a compact space; let (Rn, d) denote Euclidean space in Euclidean metric; give C(X, Rn) the corresponding uniform topology. A subspace F of C(X, Rn) has compact closure if and only if F is equicontinuous and pointwise bounded under d.
At first glance this is a dense theorem; however, we can piece things together to handle it. First assume G to be the compact closure of F. Since X is compact, by extreme value theorem all functions in C(X, Rn) have extreme values, and thus sup metric is well-defined. Since G is compact and metrizable, it is totally bounded[7], and total boundedness further implies it is equicontinuous under d. Compactness of G also implies it is bounded under sup metric, and thus G is pointwise bounded under d by definition. Since F is contained in G, F is also equicontinuous and pointwise bounded under d.
   Conversely, assume that F is equicontinuous and pointwise bounded under d. To show that G is compact, we can equivalently show that it is complete and totally bounded. Completeness is easy to prove since G is closed in the complete metric space (C(X, Rn), sup metric). To prove total boundedness, the book used two steps. Firstly, we prove that G is also equicontinuous and pointwise bounded under d by using the ε/3 trick. Then we show that there is a compact subspace Y of Rn such that G in contained in C(X, Y). Since F is equicontinuous, we can apply the previous lemma and claim that G is totally bounded.
   Now I tend to prove Arzela’s theorem given as an exercise in page 280.
Theorem (Arzela’s theorem).  Let X be compact; let fn  C(X, Rk). If the collection {fn} is pointwise bounded and equicontinuous, then the sequence fn has a uniformly convergent subsequence. [8]
Proof:
    The topology of pointwise convergence is simply the subspace topology that C(X, Y) receives from the product topology on YX. Since {fn} is pointwise bounded and equicontinuous, by Ascoli’s theorem, {fn} has compact closure. As a result C|({fn}) is sequentially compact, which implies that { fn } has a subsequence {fnk} which converges under the uniform metric. By the very definition of uniform metric, {fnk } is uniformly convergent.[9]                                                                                                              (Q.E.D)
    Arzela-Ascoli’s theorem can be applied to determine whether certain integrals have minimizers or to solve some non-linear differential equations like Peano’s theorem. However, if X is not compact in the first place, then this theorem loses its power because we cannot even define sup metric on C(X, Rn)! So in terms of Arzela-Ascoli theorem, it’s all about compactness.    
     Now we should explore some interesting topologies on C(X, Y) in addition to the uniform topology. Specifically, we look at three topologies: the topology of pointwise convergence, the topology of compact convergence, and the compact-open topology.
    Given spaces X and Y, a sequence of functions {fn} is said to be pointwise convergent if for every point x in X, the sequence fn(x) in Y is convergent. Given a point x of X and an open set U of Y, let S(x, U) = {f | f belongs to Y and f(x) is contained in U}. Then the sets S(x, U) are subbasis for topology of pointwise convergence on YX.
     A typical basis element about the function f is all functions that are epsilon close to it at finitely many points. Simply put, the topology of pointwise convergence is just the subspace topology that C(X, Y) inherits from the product topology on YX. The reason why we call it pointwise convergent topology is because if for each x in X, fn(x) converges to f(x), then fn converges to f in this topology and vice versa.
     A convergent sequence of continuous functions in the topology of pointwise convergence doesn’t necessary have a continuous limit. A classic example is fn(x)=xn , 0≤x≤1, whose convergence function is piecewise. We know that in uniform topology, if {fn} is continuous and convergent to f, then f is also continuous. So naturally a new question pops up: is there a topology coarser than uniform topology but satisfies that the limit point of convergent sequence of continuous functions is continuous? It turns out we can find one, namely the topology of compact convergence.
     Definition: Let (Y, d) be a metric space; let X be a topological space. Given an element f of YX, a compact space C of X, and a number   >0, let BC (f, ε) denote the set of all those elements g of YX for which: sup{d(f(x), g(x)) | x belongs to C} < ε. The sets BC (f, ε) form a basis for topology on YX, and it’s called the topology of compact convergence.
     Note that this topology is stronger than the one on pointwise convergence because it demands basis element containing f to consists of function that are ε close to f at all points at some specified compact set. The reason why we call it topology of compact convergence is due to the theorem that fn converges to f in the topology of compact convergence if and only if for each compact subspace C of X, the sequence fn | C converges uniformly to f | C.
     A space X is compactly generated provided that set A  C being open in C for each compact subspace C of X implies A to be open in X.[10] It turns out that if X is locally compact or has a countable basis at each of its point, then X is compactly generated.
     Compactly generated spaces have many useful properties. For example (assume X is compacted generated), if we can show that for each compact subspace of X, the restricted function f | C is continuous, then we can actually conclude that f is continuous. Using this property, we can prove that in terms of X and (Y, d), C(X, Y) is closed in YX in the topology of compact convergence.
      If X is compact, then uniform convergence on compact set implies the uniform convergence upon X. If X is discrete, the only compact sets are the finite sets, so compact convergence coincides with pointwise convergence. If X is arbitrary, then uniform convergence implies compact convergence, which further indicates pointwise convergence.
      Since both uniform and compact convergence topologies depend on metrics, we are curious whether these topologies can work when Y is arbitrary. We focus our exploration on C(X, Y) and it turns out that compact-open topology on C(X, Y), generated by S(C, U) = {f | f belongs to C(X, Y) and f(C) is contained in U}, where C is a compact subspace of X and U is an open subset of Y, is a generalization of the compact convergence topology to arbitrary X and Y. Furthermore, when Y is metrizable, compact convergence topology is the same as compact-open topology.  The proof is just to show that one topology is finer than the other and vice versa. As a corollary, the compact convergent topology on C(X, Y) doesn’t depend on metric of Y. (The reason is that we didn’t say anything about metric when defining compact-open topology)
      Another desirable property of compact-open topology is that it is jointly continuous because the expression f(x) is continuous both in x and f. In fact, if X is locally compact Hausdoff, and let C(X, Y) have the compact-open topology, then the map
e: X × C(X, Y) → Y
defined by e(x, f)=f(x) is continuous. (e is called the evaluation map)
      Local compactness is the crucial property in order for C(X, Y) to be jointly continuous. A counter-example is to let X= Q, set of rational numbers.[11]
      Compact-open topology is widely applied in algebraic topology. Its connection with homotopy is briefly discussed in page 288.
      Using the three topologies on C(X, Y), along with Tychonoff theorem, we can pprove a more general Ascoli’s theorem, in which C(X, Y) is given the topology of compact convergence. Interested readers can refer to section 47 of the book. Munkres certainly does a better job laying out details of proof than I do!    


[1] Real Analysis, 4th edition. H.L. Royden and P.M. Fitzpatrick
[2] Topology, 2nd edition. J.R. Munkres
[3] A.P.M Kupers, “On Space-Filling Curves and the Hahn-Mazurkiewicz Theorem.” Notes for a Stanford SUMO talk on space-filling curves.

[4] A space X is limit point compact if every infinite subset of X has a limit point, and X is said to be sequentially compact if every sequence of points X has a convergent subsequence. 
[5] Topology 2nd edition, J.R. Munkres, pp.276
[6] Again, the detailed proofs are in page 277 and 278.
[7] Refer to previous discussion.
[8] For whatever reason, this is what most people think about when it comes to discussion of Arzela-Ascoli theorem. I read Introduction to Real Analysis by Stephen Abbott and Real Analysis by Royden for references, and their discussion of Arzela-Ascoli theorem is this Arzela’s theorem. I guess the reason is that in terms of analysis, finding a criterion of a uniform subsequence is more important than determining a compact closure.  

[9] This is probably the easiest proof since we can cite Ascoli’s theorem right away. Another proof with more analysis flavor is to use Cantor diagnonalization argument. I read this proof in Royden’s Real Analysis.
[10] This notion can be equivalently defined by using B as a closed set.
[11] We have proved that Q is not locally compact.

11/30/2013

Identity and organizations

Literature review of a seminal paper by George Akerlof and Rachel Kranton.
Inculcating in employees a sense of identity and attachment to the organization may do good to the well-functioning of the whole group. \

Monetary incentives remain a blunt instrument.
(1) Compensation schemes are based on observable variables. But these variables are at best a proxy of effort and ability of the employees. Information asymmetry still exists, incurring moral hazard and adverse selection.
(2) Monetary compensation may have negative effects. For example, employees may overperform on well rewarded tasks and underperform on poorly rewarded tasks. Workers may also backstab one another to be the best relative performer.

Vilfredo Pareto noticed that much of the utility depends not only on what economists usually think of as tastes, but also on norms as to how people think that they and others should behave. Another observation is how people should behave depends upon the particular situation.

The term identity is used to describe a person's social category---gender, race, group affiliation...It is also used to describe a person's self-image. IN a model of utility, a person's identity describes gains and losses in utility from behavior that conforms or departs from the norms for particular social categories in particular situations. In this model, individuals' utility function can change because people internalize norms at different time periods, triggered by different actions. 

We add a new variable, identity to basic principal-agent model.
In this new model, the worker's utility varies with his category as either an insider or an outsider, and his utility depends on his effort from the ideal for his respective social category, depending on his identity and situation.

Effect of adding this new variable:
When the agent sees himself as an insider, he max his identity utility by exerting the high effort, and thus he doesn't need a large difference in monetary rewards to induce him to work hard. If the agent sees himself as an outsider, then he requires a higher wage differential to compensate him for the utility he loses when he works in the interests of the firm.

The firm may have incentive to change the agent from an outsider to an insider.

11/27/2013

perfect rationality....wait are you kidding me?

In game theory, a fundamental assumption is that people are rational in making decision each time they are called upon to move. Sometimes counter-intuitive equilibria occur in stylized game theory models and it makes people wonder: are people really that rational in making every decision in their daily life? One thing I feel certain about is that I'm not...

For decades a new sub-field of economics, behavioral economics, has been challenging the orthodox assumptions of neo-classical econ that people are predictably rational. Behavioralists have a point right: people can't be as rational as economists assumed in their models. Our brains are not powerful enough to make choices with no consideration of emotion, and most people won't make consistent strategies in subject to Bayes' rule of probability.

Behavioralists come up with a definition, bounded rationality, to state that people's rationality of decision making is impaired by mental constraints on information processing and probability calculation.

Yes I admit that people are not rational all the time, but the market still doesn't fail because it induces participants to be more rational than they otherwise would be in an environment with no price. Trials and errors entail progress and as participants get more experienced, they would know the tacit rules and have better outcomes. In game theory terminology, this is called the refined equilibrium. Mathematically most games have an odd number Nash Equilibira and some equilibria don't make much economic sense. I call them "irrationally rational equilibrium". To winnow out the bad equilibrium, game theorists come up with some refinement strategies like trembling hand equilibrium, consistent belief and reasonable beliefs. One thing surprising is that...after all these delicate and smart manipulations, the final results of games are often what we see in daily life! That is, perfect rationality can mimic the outcome of seemingly irrational behavior in market context.

One drawback of behavioral econ experiments is that they are mostly done in labs, which is a different environment from market, and thus the result may not be compatible with what people really would choose given that they are in a more dynamic and chaotic atmosphere. 

11/03/2013

Unemployment insurance

The US unemployment insurance includes 53 separate jurisdictions, each with unique laws and operating procedures. The system is financed through a federal tax on payrolls, and the majority of the benefits paid by the system are collected by state-level taxes. During recessions, additional federal involvement kicks in.

Many unemployed workers are not covered either because they are recent entrants to the labor market or they quit jobs voluntarily. Some unemployed are not eligible for the insurance because they have not earned enough in their previous recent jobs or they have exhausted the insurance. Some unemployed don't get covered because they didn't file for the insurance in the first place.

Programs in different states vary due to difference in four general areas: rules of eligibility, what jobs are covered, variation in weekly benefit amounts, and variation in weeks for exhaustion.

Eligibility:
To be eligible, three conditions must be met:
(1) whether a worker has sufficient employment during some defined base period
(2) whether a worker has an acceptable reason for the job separation
(3) whether a worker continues to be unemployed, (in other words, determine if he actively searches new job while collecting the insurance)

The problem of condition (1): it may exclude low-wage, temporary workers and those who have a short labor history.

Condition(2): may be hard to differentiate voluntary quitting
Condition(3): information problem (Hayek's critique I guess...)

Unemployment insurance to some extent prevent major declines in consumption spending in response to layoffs, but we have to consider that variation across workers and those who don't collect insurance. 

Design of unemployment insurance:
1) most models find that optimal replacement ratios are less than 1 when unemployment benefits pose  significant disincentive to find work
2) models that allow for realistic levels of personal borrowing and saving lead to lower optimal replacement ratios than those that do not.
3) replacement rates that decline over the duration of the unemployment spell may be perferable to constant wage replacement rates
4) consider moral hazard when deciding the duration of insurance coverage.


10/19/2013

Summary of Home ownership, job duration, and wages



In this paper, the authors first hypothesized that employed home owners, compared to renters, are less likely to be unemployed, have longer employment spell lengths, and earn higher wages. Then the authors tested the hypotheses empirically based on a rich Danish micro data set. The data set they used covered one percent of the Danish population from years 1993 to 2001 and contained detailed information of housing, labor market experiences and demographic characteristics.   The pattern of the data showed that on average homeowners have more favorable labor market outcomes than renters.

To investigate the impact of home ownership on job duration and wages, the authors specified a competing risks duration model for job spells, used a mixed proportional hazard model for the labor market transitions to capture unobservable work characteristics, and estimated a standard human capital wage equation.

To identify the causal linkage between homeownership and hypothesis, the authors modelled the selection process into homeownership and used two different identification strategies to check the robustness of their results. The first identification related to the fact that some people change their home ownership status during job spells. This identification required authors to observe job spells both when the individual is a home owner and when he is a renter. Conditional on heterogeneity, it helped authors to see the relation between employment outcomes and home ownership status. The second identification was about exclusion restrictions, which helped tease out variables that affect home ownership decision but have no direct impact on labor market outcomes. The authors considered regional home ownership rate, home owner status of the parents, and home ownership rate in the region of birth as variables that only affected home ownership status. The whole model endogenized home ownership status.

The results of the paper were conform to the hypotheses. Home owners are less likely to transit into new jobs, and they earn higher wages. The paper suggests that there are labor market gains for homeownership. The problem of the model is that to make it more tractable, the authors restricted the correlation structure.  

Summary of Tenure Choice and Labor Market Outcomes



This paper tested at the individual level the argument that regional homeownership rates are positively correlated with regional unemployment rates.
The null hypotheses were based on search theory which argued that renters have a higher matching rates to firms while home owners, due to fixed cost in property acquisition and immobility, suffer more from regional demand shocks. Specifically, the hypotheses contends that renters, compared to homeowners, have lower unemployment probabilities, shorter duration of unemployment and higher wages.
Some previous research papers showed that at aggregate level house ownership has a negative impact on labor market outcomes. However, these papers failed to present evidence at individual level. Besides, much of the current evidence is bivariate, which means there may be omitted variables involved. To test hypotheses at individual level, Coulson and Fisher used two data sets, namely the March 2000 wave of Current Population Survey (CPS) and 1993 wave of the Panel Survey of Income Dynamics (PSID), with a multivariate model.

The CPS data included information on 29753 individuals’ housing, labor market experiences and demographic characteristics. The sample reflected the relatively wealthier portion of the country. Coulson and Fisher first used probit model, in which the dependent variable is a dummy variable, to test the hypothesis that homeowners are more likely to be unemployed. The univariate test turned out to reject the hypothesis. To tease out the impact of omitted variables, Coulson and Fisher then added some other significant variables such as age, races, education levels, marital status, and locations, but refined regression still rejected the hypothesis.

Coulson and Fisher then used CPS data and OLS method to test the hypothesis that renters have more wages. Both the unconditional and conditional tests showed that home owners have more wages than renters, which rejected the hypothesis.  

Having rejected the first and third hypothesis using CPS data, Coulson and Fisher used PSID to test the three hypotheses, particularly the second that renters have shorter duration of unemployment. Compared to CPS, which only included a simple cross-section, PSID had longitudinal data, which could be better used to test spell length of the sample. The sample consisted of 5125 individuals, which had on average lower incomes, lower education levels and younger ages compared to those in the CPS data. The univariate and multivariate regression tests both showed that home ownership is a significant negative indicator for unemployment and home owners earn more than renters. Assuming that the length of spells satisfies Weibull distribution, Coulson and Fisher ran the regression and found that home ownership exerts a negative influence on the length of the unemployment. The second hypothesis was rejected.

Coulson and Fisher rejected the null hypotheses that homeowners have lower unemployment probabilities, longer unemployment spells and lower wages than renters. The possible reasons why the hypotheses didn’t hold may be that mobility of renters tend to equalize unemployment rates across areas regardless of the homeowners’ behavior, or firms don’t observe status of homeownership and tend to pool renters and owners into a single labor market.

However, this paper was criticized by Munch, Rosholm, and Svarer. They pointed out that Coulson and Fisher didn’t address the potential endogeneity of the home owner variable, which made it unclear if the positive labor market outcomes for home owners are causal or spurious.